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@Proathletes Practicing Celebrity on #Twitter

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Material Information

Title:
@Proathletes Practicing Celebrity on #Twitter A Tweet-Point Shot
Physical Description:
1 online resource (58 p.)
Language:
english
Creator:
Netzler, Kristina
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Master's ( M.Adv.)
Degree Grantor:
University of Florida
Degree Disciplines:
Advertising, Journalism and Communications
Committee Chair:
Wanta, Wayne M
Committee Members:
Morton, Cynthia R
Wright, John W

Subjects

Subjects / Keywords:
athletes -- branding -- celebrity -- linguistics -- nba -- twitter -- wnba
Journalism and Communications -- Dissertations, Academic -- UF
Genre:
Advertising thesis, M.Adv.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
Celebrity as a practice on Twitter is a new theory defined as the appearance and performance of ‘backstage’ access by celebrity practitioners, referred to as professional athletes in this study, through affiliation, intimacy, authenticity and sincerity, and fan maintenance (Markwick & boyd, 2011). Previous studies have examined professional athletes’ use of Twitter to build their brands (Parmentier & Fischer, 2012; Fischer, Smith & Yongjian, 2012; Yan, 2011), however none have examined their use of celebrity as a practice on Twitter. This study served to extend previous research by using a content analysis to examine how professional athletes can practice celebrity to build their brands through both user-generated content and linguistic tools used on Twitter. Descriptive statistics including Chi Square tests, t-tests, and Pearson Correlation tests were used to satisfy the objectives of this study. A key finding from this study was both NBA players and WNBA players practice celebrity on Twitter through user-generated content and linguistics tools. However, the specific type of user-generated content shared and the specific linguistic tools used to share that content varied. This study found that NBA players are followed 122 times more than WNBA players suggesting popularity differentials between male and female professional athletes exist both offline and online. Interestingly for both NBA players and WNBA players, no correlation was found between number of followers versus the number following, total number of tweets, tweeting frequency, and total number of linguistics tools used.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Kristina Netzler.
Thesis:
Thesis (M.Adv.)--University of Florida, 2013.
Local:
Adviser: Wanta, Wayne M.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2013
System ID:
UFE0045962:00001

MISSING IMAGE

Material Information

Title:
@Proathletes Practicing Celebrity on #Twitter A Tweet-Point Shot
Physical Description:
1 online resource (58 p.)
Language:
english
Creator:
Netzler, Kristina
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Master's ( M.Adv.)
Degree Grantor:
University of Florida
Degree Disciplines:
Advertising, Journalism and Communications
Committee Chair:
Wanta, Wayne M
Committee Members:
Morton, Cynthia R
Wright, John W

Subjects

Subjects / Keywords:
athletes -- branding -- celebrity -- linguistics -- nba -- twitter -- wnba
Journalism and Communications -- Dissertations, Academic -- UF
Genre:
Advertising thesis, M.Adv.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
Celebrity as a practice on Twitter is a new theory defined as the appearance and performance of ‘backstage’ access by celebrity practitioners, referred to as professional athletes in this study, through affiliation, intimacy, authenticity and sincerity, and fan maintenance (Markwick & boyd, 2011). Previous studies have examined professional athletes’ use of Twitter to build their brands (Parmentier & Fischer, 2012; Fischer, Smith & Yongjian, 2012; Yan, 2011), however none have examined their use of celebrity as a practice on Twitter. This study served to extend previous research by using a content analysis to examine how professional athletes can practice celebrity to build their brands through both user-generated content and linguistic tools used on Twitter. Descriptive statistics including Chi Square tests, t-tests, and Pearson Correlation tests were used to satisfy the objectives of this study. A key finding from this study was both NBA players and WNBA players practice celebrity on Twitter through user-generated content and linguistics tools. However, the specific type of user-generated content shared and the specific linguistic tools used to share that content varied. This study found that NBA players are followed 122 times more than WNBA players suggesting popularity differentials between male and female professional athletes exist both offline and online. Interestingly for both NBA players and WNBA players, no correlation was found between number of followers versus the number following, total number of tweets, tweeting frequency, and total number of linguistics tools used.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Kristina Netzler.
Thesis:
Thesis (M.Adv.)--University of Florida, 2013.
Local:
Adviser: Wanta, Wayne M.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2013
System ID:
UFE0045962:00001


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1 @PROATHLETES PRACTICING CELEBRITY ON #TWITTER: A TWEET POINT SHOT By KRISTINA NETZLER A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUI REMENTS FOR THE DEGREE OF MASTER OF ADVERTISING UNIVERSITY OF FLORIDA 2013

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2 2013 Kristina Netzler

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3 To my family

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4 ACKNOWLEDGEMENTS Many people have helped and encourage me throughout the writing and defending of this thesis. I would first like to thank my parents, Don Netzler and Mari Frederick, and stepfather, Steve Dahlke, for their support during my graduate school experience. Thei r love, support and guidance were continuous and always present when I needed it. I want to thank my sister and brother, Whitney and Tanner, for always being my motivation and reminder that life is precious and success comes through happiness and integrity Additionally, I have to thank my cat and dog, Honey and Rolo, for being the best support on the many long days of writing my thesis. Finally, I want to thank all of my family on both the Netzler and Frederick sides for keeping me grounded and humbled by reminding me I will always be a small town girl from Wisconsin who loves the Packers. Next, I want to thank my thesis committee. First, I thank Dr. Wayne Wanta who served as my committee chair and fellow Packers fan. Throughout the entire process Dr. Wanta provided guidance, constructive criticism, perspective, and encouragement while writing my thesis. He answered hundreds of emails and thousands of questions with patience and kindness throughout the process of completing my thesis. Next, I want to thank D r. John Wright and Dr. Cynthia Morton who served on my committee. They provided invaluable insight and assistance, as well as provided the necessary energy needed to continue my thesis meetings. I would also like to thank the Dr. Debbie Treise for taking a chance by allowing me to be a rare Spring admittance. Dr. Treise supported me throughout my time in the College of Journalism at the University of Florida, including two knee surgeries and my heart surgery. She is the reason the graduate sch ool has the exceptional

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5 reputation for being one of the top advertising programs in the nation. Additionally, I would like to thank Jody Hedge. Jody is the heart and soul of the College of Journalism at the University of Florida. Her kindness, compassion, and love for every single student are exemplary and made a lasting impression on me as a scholar, and more importantly as a human being. Without the guidance and support from both Dr. Treise and Jody I would not be the person and professional I am today. I also thank my co workers at New Student and Family Programs in the Dean of Students Office of the Division of Student Affairs at the University of Florida. Thank you for providin g me the opportunity to earn a m You all became my family and cared for me academically, professionally, and personally. Specifically, I would like to thank my boss Jaime Gresley who ta ught me about being the leader of the ki ndness and most genuine people I have ever met and I thank you for believing in me. You have left a lifelong lasting impression and I will forever be grateful. Finally, I would like to thank Brittany who has been with me every step of the way. Throughout t he past year and half you have always had the upmost faith in my abilities and were my rock when I needed reassurance I look forward to a promising future together. Thank you for the never ending love and commitment.

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6 TABLE OF CONTENTS page ACKNOWLEDGEMENTS ................................ ................................ ............................... 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTI ON AND PURPOSE ................................ ................................ ......... 11 2 LITERATURE REVIEW ................................ ................................ .......................... 14 Theoretical Framework ................................ ................................ ........................... 14 Twitte r ................................ ................................ ................................ ..................... 15 Celebrity As A Practice On Twitter ................................ ................................ .......... 16 Linguistic Tools ................................ ................................ ................................ ....... 19 Tweet ................................ ................................ ................................ ................ 20 @ Replies ................................ ................................ ................................ ......... 20 ................................ ................................ ................................ .. 21 Retweet or Modified Retweet (RT) ................................ ................................ ... 21 Links ................................ ................................ ................................ ................. 21 Channels ................................ ................................ ................................ ................. 21 One to One ................................ ................................ ................................ ...... 22 One to Many ................................ ................................ ................................ .... 22 Many to Many ................................ ................................ ................................ .. 22 Branding ................................ ................................ ................................ ................. 23 3 METHODOLOGY ................................ ................................ ................................ ... 26 4 RESULTS ................................ ................................ ................................ ............... 32 Data Set 1 ................................ ................................ ................................ ............... 32 Weekdays and Weekends ................................ ................................ ................ 32 Catego ries ................................ ................................ ................................ ........ 33 Channels ................................ ................................ ................................ .......... 35 Linguistic Tools ................................ ................................ ................................ 37 @ Replies ................................ ................................ ................................ .. 37 Hashtags ................................ ................................ ................................ .... 37 Retweets ................................ ................................ ................................ .... 38 Links ................................ ................................ ................................ .......... 38 Data Set 2 ................................ ................................ ................................ ............... 38 Number of Followers ................................ ................................ ........................ 39 Number Following ................................ ................................ ............................ 41 Number of Tweets ................................ ................................ ............................ 41

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7 Total Number of Linguistic Tools ................................ ................................ ...... 42 NBA Players and WNBA Players Pearson Correlation Tests ........................ 44 5 CONCLUSIONS ................................ ................................ ................................ ..... 47 Summary ................................ ................................ ................................ ................ 47 Time and Frequency ................................ ................................ ........................ 47 Categories ................................ ................................ ................................ ........ 47 Channels ................................ ................................ ................................ .......... 49 Linguistics Tools ................................ ................................ ............................... 50 Number of Followers ................................ ................................ ........................ 51 Overall Conclusions and Implications ................................ ................................ ..... 52 Limitations ................................ ................................ ................................ ............... 53 Recommendations for Future Research ................................ ................................ 53 LIST OF REFERENCES ................................ ................................ ............................... 55 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 58

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8 LIST OF TABLES Table page 4 1 Weekday (V4) vs. Gender (V2) Chi Square ................................ ..................... 33 4 2 Categories (V7) vs. Gender (V2) Chi Square ................................ .................. 3 4 4 3 Channels (V8) vs. Gender (V2) Chi Square ................................ .................... 36 4 4 At Reply (V10) vs. Gender (V2) Chi Square ................................ .................... 37 4 5 Hashtag (V11) vs. Gender (V2) Chi Square ................................ .................... 37 4 6 Retweet (V12) vs. Gender (V2) Chi Square ................................ .................... 38 4 7 Links (V13) vs. Gender (V2) Chi Square ................................ ......................... 38 4 8 Means (Standard Deviation) and T Test Results ................................ ................ 39 4 9 Number of Followers (V3) vs. Gender (V2) T Test ................................ .......... 40 4 10 Condensed Number of Followers Variable (nufol low1) ................................ ....... 40 4 11 Number of Followers (nufollow1) vs. Gender (V2) T Test ............................... 40 4 12 Number Following (V4) vs. Gender (V2) T Test ................................ .............. 41 4 13 Number of Tweets (V5) vs. Gender (V2) T Test ................................ .............. 41 4 14 Number of Actual Tweets (V5) vs. Gender (V2) Chi Square ............................ 42 4 15 Total Number of Linguistic Tools (V8) vs. Gender (V2) T Test ........................ 43 4 16 NBA Player Descriptive Statistics ................................ ................................ ....... 43 4 17 WNBA Players Descriptive Statistics ................................ ................................ .. 44 4 18 All Variables for NBA Players Pearson Correlation Test ................................ .. 45 4 19 All Variables for WNBA Players Pearson Correlation Test .............................. 46

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9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Advertising @PROATHLETES PRACTICING CELEBRITY ON #TWITTER: A TWEET POINT SHOT By Kris t ina Netzler August 2013 Chair: Wayne Wan t a Major: Advertising Celebrity as a practice on Twitter is a new theory defined as the appearance and ess by celebrity practitioners, referred to as professional athletes in this study through affiliation, intimacy, authenticity and sincerity, and fan maintenance (Markwick & boyd, 2011). Previous studies have examined professional athletes use of Twitter to build their brands (Parmentier & Fisc her, 2012; Fischer, Smith & Yongjian, 2012; Yan, 2011), however none have examined their use of celebrity as a practice on Twitter. This study served to extend previous research by using a content analysis to examine how professional athletes can practice celebrity to build their brands through both user generated content and linguistic tools used on Twitter. Descriptive statistics including Chi Square tests, t tests, and Pearson Correlation tests were used to satisfy the objectives of this study. A key fin ding from this study was both NBA players and WNBA players practice celebrity on Twitter through user generated content and linguistics tools. However, the specific type of user generated content shared and the specific linguistic tools used to share that content varied This study found that NBA players are followed 122 times

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10 more than WNBA players suggesting popularity differentials between male and female professional athletes exist both offline and online. Interestingly for both NBA players and WNBA pla yers, no correlation was found between number of followers versus the number following, total number of tweets, tweeting frequency, and total number of linguistics tools used.

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11 CHAPTER 1 INTRODUCTION AND PURPOSE Twitter has become one of the most popular social networking sites (SNSs) of the 21 st century and Web 2.0 era. Celebrity practitioners are using the micro blogging site to practice celebrity by building their personal brand through constructing and maintaining fan bases, known on Twitter as followers. Professional athletes have always held a celebrity status with fans, however the majority of female athletes have traditionally not experienced the same highly visible fame as their male counterparts. The NBA currently has 30 teams compared to 12 teams in the WNBA. In 2012, the Chicago Bulls i n the NBA had the highest attendance with an average of 21,876 fans each game (ESPN 2013 ). In comparison the Los Angeles Sparks in the WNBA averaged only 10, 567 fans each game (Sports Business Daily 2012 ). Celebrity as a practice on Twitter is a new the ory that has little research in general, and even less research is available as it pertains to professio nal athletes. Introduced by Mar wick and celebrity practitioners th rough affiliation, intimacy, authenticity and sincerity, and fan maintenance on Twitter. mainstream media. Twitter has allowed professional athletes to tweet their way into (McManus, 2012). A recent article interviewed Paul Bissonnete, a National Hockey League (NHL) player wh o rarely sees playing time, has used the micro blogging site to practice celebrity and build a brand that includes multiple endorsement deals (Riva, 201 2). His Twitter account @BizNasty2point0 is the top followed NHL player largely due to his comical, clever, and personable persona on the social networking site.

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12 However, not all coverage of the Twitter trend has been positive and well received by the pres s, fans, or the team organizations of professional athletes (Paraham, 2011; Roberts, 2011; Skolnick, 2011). Basketball Association (NBA), Major League Baseball (MLB), Nationa l Football League (NFL), Major League Soccer (MLS), National Hockey League (NHL), and English Premier League (EPL) to impose policies that limit the use of social media for players, league employees, and even the media (Paraham, 2011). While there are down sides to On a changing the way fans connect to the game. As the infusion of social media networks and professional athletes continues to increase there is an imperative need to understand how they can use these resources to build their brand through practicing celebrity on Twitter. Unlike traditional celebrity status in the offline world, NBA players and WNBA players have the same opportunity to increase their marketability o n and off the court in the online world. Although some research has been conducted on the history, practices, linguistics, and users and communities of Twitter (Edosomwan, Prakasan, Kouame, Watson, & Seymour, 2011; Gruzd, Wellman, & Takhteyeev, 2011; boyd, Golder, & Lotan, 2010; Chen, 2010; Kwak, Lee, Park, & Moon, 2010; Marwick & boyd, 2010; Honeycutt & Herring, 2009; boyd & Ellison, 2007), studies up to this point have not examined how male and female

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13 professional athletes use the micro blogging site simi larly or differently to practice celebrity. More specifically, no studies have examined professional athletes using the theory of celebrity as a practice on Twitter other than focusing solely on building brands (Parmentier & Fischer, 2012; Fischer, Smith, & Yongjian, 2012; Yan, 2011). Therefore, this study served to extend previous research by using a content analysis to ex amine how professional athletes can practice celebrity to build their brands through both the user generated content and linguistic too ls used on T witter. Finally, this study aimed to bridge the gap between research on celebrity as a practice brands. Through the content analysis of 10 NBA player and 10 WNB A player Tw itter accounts, this study examine d whether NBA players and WNBA players differ on: 1. their use of Twitter to practice celebrity 2. their use of Twitter for personal purposes 3. their use of Twitter for personal purposes or to practice celebrity based on number of followers 4. their use of Twitter tools such as hashtags (#s), @ replies, retweets (RTs) and links Also, this study aimed to expand previous research to determine new celebrity practices on Twitter that NBA players and WNBA players should use to effectively build their brands.

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14 CHAPTER 2 LITERATURE REVIEW Theoretical Framework Social networking sites (SNSs ) often referred to as social media networks or social media, have advanced the Web to the Web 2.0 era, which includes a myriad of si tes for social interaction and user generated content Social media has transformed communication and removed geographical barriers of individuals throughout the world. While the concept of the phenomenon itself is not new, the purposes and practices of how to use social media are continually evolving. Boyd and Ellison (2007) define social network sites as web based services that allow individuals to (1) construct a public or semi public profile within a bounded system, (2) articulate a list of other user s with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system. SNSs allow individuals to construct an online representation of self and publicly display connections with people they kno w or network with people they have never met. The public or semi public display of connections helps users navigate through the networked social world and build a network of users with similar interests or gather information about their interests. Previous research has offered many ideas about the first occurrences of social me dia, most noting the early 1990 s as the time when social networking sites were officially created (Edosomwan et al., 2011; boyd & Ellison, 2007). Some examples include Six Degrees, Bl ackPlanet, Asian Avenue, MoveOn, ThirdVoice, and Napster. These early niche social networking sites included sites that allowed users to advocate for public policy, blog, share reviews of products, post comments on webpages, and

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15 utilize peer to peer file s haring. The popularity of social networking sites grew exponentially in the 2000 s with a surge of sites launching. Most took the form of visual profile centric sites that allowed users to have a profile picture, personal descriptors (such as age, location, as photo, videos, or music), and a public or semi public display of connections (such as fans, contacts, friends, or followers) (boyd & Ellison, 2007). Some examples of popular social network Flickr, YouTube, and Twitter. Twitter In 2006, Twitter, a micro blogging site, was launched and created by a San Francisco based company called Obvious. The 10 person start up company in troduced Twitter during the same time Facebook was becoming available to everyone. Users can send messages limited to 140 individuals, groups, and the public at large. Tweets can be posted via Twitter .com, text messages, or several th ird party application clients (e.g TweetCaster or TweetBot) that Twitter users can maintain a brief profile that consists of a profile p icture, name, webpage location, and short biography. Unlike Facebook and several other social networking sites, the relationship of following or connecting with users d oes not require reciprocation. This means a user can follow another user, and the user d oes not have to follow them back, which on accounts. Also, users can choose to make their profiles public or private, and can choose to accept all followers or require appr oval before a user can follow their account.

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16 Kwak et al. (2010) found that Twitter overall shows a low level of reciprocity; 77.9% of user pairs with any link between them are connected one way, and only 22.1% have a reciprocal relationship between them. T hey also found that 67.6% of users are not followed by any of their followings, suggesting that Twitter is rather a source of information than solely a social networking site. In contrast, Chen (2010) found that the more months a person is active on Twitte r and the more hours per week the person spends on Twitter, the more the person gratifies a need for an information sense of The top users on Twitter are typically celebrities and most of them do not foll ow their followers back. While the number of followers can easily estimate top users and is increased when their tweet is retweeted or their following includes influen tial people itter followings reaching over 4 0 million users. Unexpectedly, his busi ness teams of managers, directors, stage crew, hair stylists, high school friends, and dancers who typically are behind the scenes, all have significant followings, which can arguably be attributed to his tweets being directed to those users. Even fans tha t Justin directly followed by the lucky fans he noticed on Twitter. Celebrity As A Practice On Twitter Celebrity as a practice on Twitter was introduced by Marwick and boyd (20 11) practitioners, referred to as professional athletes in this study. The authors

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17 conceptualized the practice of celebrity to involve continuous maintenance of fan bases, per formed intimacy, authenticity and access, the construction of a consumable persona, and the equally important, mutual recognition of power differentials between the profes sional athletes and their fans. In this study, celebrity as a practice was operationa lized as: A FFILIATION P ublicly performing a connection with fans using language, words, cultural symbols, and conventions. I NTIMACY P osting personal pictures and videos, addressing rumors, and sharing personal information. A UTHENTICITY AND S INCERITY T aking risks and not playing it safe, being real and honest through discourse and connection. FAN MAINTENANCE Constant interaction with fans to preserve power differentials. Prior to this research, Senft (2008) introduced s the use of social media to develop and maintain an audience using a set of practices. Micro base; popularity is maintained through ongoing fan management; and self present ation 140). The practice of micro celebrity was initially aimed at explaining the phenomenon of non ollowings such as comedian Jenna Marbles, or child singers and regulars on The Ellen Show, Sophia Grace and Rosie Hit. Now, both ordinary and celebrity users on Twitter are adopting the above mentioned practices and Marwick and boyd (2011) argue that ebrity has become a set of circulated strategies and practices that place fame on a continuum,

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18 In other research, Hambrick, Simmons, Greenhalgh, & Greenwell (2010) examined Twitter use amon g professional athletes by applying the uses and gratifications theory to offer suggestions on ways players could adjust their tweets to interactivity, diversion, informati on sharing, content, promotion, and fanship, which will be defined in the methods section of this study. In this study, the categories were operationalized as: I NTERACTIVITY D irect communication with fellow athletes and fans. D IVERSION N on sports related I NFORMATION S HARING I C ONTENT Links to pictures, videos, and other websites. F ANSHIP Discussion of sports other than their own teams and teammates. P ROMOTIO NAL Publicity regarding sponsorships, upcoming games, and related promotions. Hambrick et al. (2010) found that athletes with the most followers had the more conversin g directly with fans is essential to practicing celebrity on Twitter. In contrast, Page (2012) found that 63% of celebrity practitioners tweets were non addressed updates compared to 32% addressed conversational tweets. lebrity on Twitter by sharing personal i nformation, publicly acknowledging fans, and using language and cultural references to create affiliations with followers is providing an uncensored look at the personas behind mmunicate with fans is in the hands of the professional athletes themselves and followers have direct access -or at least have the

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19 perception of direct access -to professional athletes without having the traditional filters of managers, public relation s teams, and the mainstream media. This unprecedented access to professional athletes has shortened the distance between fans and the game, while also creating a platform where fans get to know professional athletes, and professional athletes get to know f ans, which could prove particularly useful to lesser known athletes such as WNBA players, who are looking to increase their awareness and popularity on and off the court (Hambrick et al., 2010). Twitter has knocked down the wall between professional athletes and the public by creating a platform where fans can show support, express dissatisfaction, participate in dialogue about games and events, network with other fans, and discuss information twe eted by their favorite athletes, teams, and sports. It is important to note that celebrity practitioners, fans, media, and intermediaries such as gossip columnists co exist on Twitter, making self representation and the practice of celebrity a negotiation of these multiple audiences to successfully maintain face and manage impressions (Marwick & boyd, 2011). This co existence of identities on Twitter makes a crucial argument for the importance of a better understanding of the practice of celebrity to help p rofessional Linguistic Tools are saying about topics using the practice of tools mentioned above. Originally, Twitter was l aunched as a way for users to update other users about the simple question, Herring (2009) found that despite a noisy environment and interface that is not especially cond ucive to conversational use, both short exchanges and longer

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20 exchanges with multiple participants are surprisingly coherent. Linguistic strategies implementing the practice of tools on Twitter are important for professional athletes to use due to the large number of tweets and speed at which they are posted. As Zappavigna (2011) found, Twitter is witnessing a cultural shift of electronic discourse and Litt (2011) foun d that interest in entertainment and celebrity news in an extremely strong predictor of Twitter adoption for young adults between the ages of 18 24, even when controlling for user background characteristics and digital media experiences. These findings sug gest that what r on the micro blogging site is entertainment and celebrity news. A number of recent studies have focused on the linguistics tools of Twitter (Page, 2012; Zappavigna, 2011; boyd et al., 2010; Honeycu tt & Herring, 2009). Hashtags, retweets, modified retweets, @ replies, and links to photos, videos, websites, etc. are all tools that assist professional athletes in practicing celebrity on Twitter. A brief description of tools are listed below: Tweet On T witter, a tweet consists of 140 characters and can be a statement by a user, a retweet of another user, or a reply to tweet from another user. @ Replies The practice of @ replies allows users to direct tweets at designated users. Addressing tweets to users helps facilitate user to user exchanges and aids users in tracking conversations. Similar to email, there can be one recipient or several, as long as the characters are within the 140 character limit.

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21 Hashtags tweet, which could include an event, place, person, or expression. Using hashtags gives the ability for users to search and follow conversations on a particular topic. The success of a h ashtag is determined by other users adopting a keyword (i.e. #NBAFinals) and continuing to use it to tag tweets. If a hashtag is used with enough various locations o n third party application clients. Retweet or Modified Retweet (RT) Retweets, or modified retweets, are considered the feature that has made Twitter a new medium of information dissemination (Kwak et al., 2010). Retweeting, denoted as of a user reposting a tweet from another user and sharing that information with their followers. The practice is similar to a user forwarding an email message to another user. Modified retweets serve the same function as retweets, but allow users to add o r remove information. Links Users include URL hyperlinks in tweets, known as links, to incorporate outside content such as videos, new stories, blogs, websites, etc. Several URL shortener s to pictures, videos, and websites. Twitter has created its own in application U RL shortener and other sites ( e. g., http://bit.ly or http://goo.gl) are also popular due to their click tracking capabilities. Channels Other researchers believe Twitter is id eally placed to provide a highly interactive one to many information channel, especially by influential individuals, using a

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22 combination of practices; @ replies, retweets, hyperlinks, and hashtags (Burton & Soboleva, 2011). This study argues that Twitter p rovides interactivity for one to one, one to many, and many to many communication levels. Examples include: One to One One to Many Many to Many James has 35 The use of linguistic tools is c rucial to professional athletes effectively reaching audiences through the above mentioned channels. Tools help followers maintain easy s, and keep them in the loop with their daily lives on and off the court. Specifically, hashtags and @ replies are useful tools for making coherent exchanges and helping audiences relate one tweet to another (Honeycutt & Herring, 2009). Hashtags for nation al events, such as sporting events, were the most frequently occurring hashtags used by celebrity practitioners and were all occasions in which they were performing (Page, 2012). This finding is similar to Hambrick et al. (2010) examination of professional discussed sports other than their own teams and teammates. These results suggest two things; (1) hashtags are used by professional athletes to strategically build their brand through performances, product s, and campaigns, and (2) there are practices of celebrity

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23 that could be used more often and more effectively to reach out to fans in different ways. Retweeting is an equally important linguistic tool used to practice celebrity on Twitter. While there are different reasons for how, why, and what users retweet, this practice contributes to conversations composed of a public interplay of voices that give rise to an emotional sense of shared conversational context (boyd et al., 2010). The practice of retweetin g allows users to share information without directly being involved in a conversation. It also serves to invite their followers into a particular thread and be aware of a conversation or topic being discussed. Understanding this tool for practicing celebr ity on Twitter is valuable because the content users retweet is inextricably tied to the goals they have related to self image and self promotion, supporting conversation and building community (boyd et al., 2010). Also, the power of influence a profession al athlete has is indicated by the amount their followers retweet their content (Gruzd et al., 2011). Page (2012) found the practice of modifying retweets is used as a technique of affiliation, most frequently by celebrity practitioners, to encourage engag ement with their followers by expressing positive endorsements of the original tweet authors and sentiment they articulate. Branding Twitter is a brand building tool that provides professional athletes the opportunity to build their personal brand and deve lop sponsorship opportunities. It is imperative to understand the strategies of practicing celebrity on Twitter to capitalize on having a successful social media presence that may result in social or economic gain. While NBA players often have successful p ersonal brands due to the high visibility and popularity of their on court careers, WNBA players are looking for off court opportunities to develop

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24 their personal brands with a wider audience. Many professional athletes who are renowned within their sport lack visibility beyond them, which we posit is especially true for WNBA players. Regardless of the visibility differentials between NBA players and WNBA players in the mainstream media and general public, both groups want to build or continue to build thei r brand equity (the value of the brand) offline and online. Parmentier and Fischer (2012) proposed two brand building goals for professional athletes; (1) a need to build their professional image of the brand they represent in relationship to their sport, and (2) a need to build their mainstream media persona by providing cues to their personality as an individual outside of their sport. When athletes garner attention outside the mainstream media of their sport they reach a wide audience of people other tha n sports fans, and sponsorship opportunities can exist beyond the court and beyond playing careers. High visibility, number of followers, and the public display of connections are crucial components of building a brand on Twitter and attracting potential s ponsors. A with the linguistic marketplace, which is used to build social and economic capital of those already in powerful positions in the offline world (Page, 2012). Smith, Fischer, and central content because of its (p. 109). Sponsors look to capitalize on professional athletes their ability to disseminate their message on their own terms to fans. However, it is not just the size of their following that matters but rather the content they are providing (Pegoraro & Jinnah, 2012).

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25 Professional athletes a Twitter to harness its power to positively build their brands. A tweet is not just a tweet, and a hashtag it not just a hashtag. A tweet invites a follower or searcher to share the values a professional athlete presents, and strategic hashtagging makes the values shared louder and more bondable (Zappavigna, 2011). Recognizing the power of have to use the medium effect ively to build their personal brands. While Twitter provides fans with unprecedented access to professional athletes, its appeal and uniqueness is in its ability for interactions to be immediate and intimate. Professional athletes need to have consistent conversations with their followers or risk them turning away when they feel they no longer have a sense of engagement and oneness resulting in a loss of brand loyalty and credibility, which in this study is the professional athletes themselves (Yan, 2011; Edosomwan et al., 2011). In three case Chad Ochocinco (NFL) all of whom have successful social media presences, Pedgoraro and Jinnah (2012) found their tweets consistent with the practices of celebrity through performed intimacy, authenticity and access, the construction of a consumable persona, and fan maintenance. Using these strategies coupled with brand building practices on Twitter is paramount for professional athlet advertising, and sponsorship potential it provides.

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26 CHAPTER 3 METHOD OLOGY Professional athletes were selected from www.sportsin140.com, a website devoted to identifying verified athlete Tw itter accounts. The sample include d 10 NBA and 10 WNBA players from the sport categories listed on the website. The complete list for both sports categories included 154 NBA and 89 WNBA professional athlete verified accounts. A stratifie d random sampling m ethod was used to ensure equal representation in both s port product categories that were selected. Retired athletes were removed from the sample and replaced with another professional athlete selected from the stratified random sample. Professional athlete s that did not have verified account s had locked accounts, or did not tweet during the randomly selected dates were also removed from the sample and replaced with another professional athlete selected from the stratified random sample. The 20 most recent tweets from a random weekday (Monday Friday) and the 20 most recent tweets from a random weekend day (Saturday Sunday) were selected All tweets for both NBA players and WNBA players were co llected from www.allmytweets.net a website that displays the 3,200 most recent tweets for a Twitter account on one page. The 20 tweets were ebsite. The weekday and weekend day selected were during the regular season of each respective sport to account for differences in weekday and weekend Twitter usage; the NBA regular season is from November through April and the WNBA regular season is from May through September. Finally, the weekday and weekend day selected were in different weeks to circumvent the

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27 possible analysis duplication of the same tweets. For example, a player may only tweet 10 times a day and therefore the 20 tweets analyzed could have been tweeted over two days, resulting in an overlap of the other rand omly selected day. Initially the study aimed to analyze a total of 800 tweets, however one of the NBA players selected only tweeted 17 times from the second randomly selected date ti ll the present. Therefore a total of 797 tweets were analyzed in the content an alysis and two data sets were examined. In the first data set the unit of analysis was the tweet s and in the second data set the unit of analysis was the professional athlete s Content analysis was then employed to categorize and analyze the tweets in the two data sets. To ensure intercoder reliability, each of the 797 tweets were independently coded into one of six categories based on the practices of celebrity on Twitter and fr Hambrick et al., 2010). As previously mentioned, several practices are synonymous in both celebrity as a practice on Twitter and brand building practices such as, being personable, authentic, immediate, consistent, conversational, sincere, genuine, and regularly maintaining their followers (Page, 2012; Pedgoraro & Jinnah, 2012; Parmentier, 2012; Edosomwan et al., 2011; Yan, 2011; Marwick & boyd, 2011; Hambrick et a l., 2010). The categories derived from the previous two studies and u sed in the current study were interactivity, affiliation, professiona l image sharing, diversion, promotional and fanship The categories were operationalized as: I NTERACTIVITY Interacti vity is a fans. The category is derived from the practice of celebrity and micro celebrity that requires constant interactions with fans to preserve fan maintenance and power differentials (Marwick & boyd, 2 011; Senft, 2008). The current study

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28 modified the category to reflect the communication with fans through the one to one, one to many, and many to many communication channels. Tweets in the interactivity category may include @ replies, retweets, modified r etweets, and direct message mentions to their fans. For example, ProAthlete1 star tonight! A FFILIATION P ublicly displaying connections with other public figures in the sports, enterta inment, or political industries. The category is derived from value of the public display of connections to affirm bonds with others who are in powerful positions in the mainstream media (Page, 2012; Parmentier & Fischer, 2012). This category incorporates the intimacy and authenticity constructs of the practice of celebrity by creating a sense of insiderness through allowing fans to observe personal relationships between celebrities (Markwick & boyd, 2011). For example, ProAthlete2: Just had dinner with th tomorrow night! P ROFESSIONAL I MAGE S HARING I or profession. Tweets can include information about practices, games, events, travel, and other team related content. The category is derived from brand building practice of affiliating with the high status playing opportunities of professional athletes to contribute to a strong professional image (Parmentier, 2012). For example, ProAthlete3: Great team win toni ght against the @Pacers. About to board this flight and pass out! See you soon #Boston #CantWaitToBeHome D IVERSION N on sports related information provided by professional athletes such as, stories, quotes, pictures, videos, websites, etc. that provides practice of providing publicly visible persona cues to gain media exposure that reaches a wider audience beyond the sport (Parmentier & Fischer, 2012). Tweets can inclu de content that provides a candid and uncensored look into the as a practice constructs through providing intimate, authentic, and sincere content about the professional Jinnah, 2012; Marwick & boyd, 2011). For example, ProAthlete4: Happy 60 th Birthday mom! So glad I got to spend the day with you. Look at P ROMOTIONAL P ublicl y promoting, advertising, and marketing sponsorships, games, events, charitable causes, etc. The category is derived from the brand build practices on Twitter to capitalize on social and economic gain. Professional

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29 athletes may tweet at other Twitter accou nts participating in the event or promotion, or use hashtags to persuade followers to interact, join, watch, or purchase the commodity being shared (Page, 2012; Zappavigna, 2011). For example, ProAthlete5: Come out to @FinishLine in Orlando tonight and su pport my charity for future ballers! #GiveBack F ANSHIP D iscussion of athletes and teams outside of own sport, and other celebrity figures. Hambrick et al. (2010) found that athletes did not spend much time communicating about other sports, either positively or negatively. The current study modified this category to include other celebrity figures based on value in potential networking opportunities b y networking with other powerful public figures. Tweets in this category differ from the affiliation category in that showing fanship, rather than a personal relationship. For example, cheesehead! #GoPack Variables in the first data set also included day of the week, channels of communication, and the specific use of each type of linguistic too ls ( e. g., @ replies, hashtags, retweets, and links). The categories for the second data set consist ed of gender, number of followers, number following, total number of tweets, number of days to accumulate 20 tweets for both of the randomly selected days, a nd total number of linguistic tools (i.e. @ replies, hashtags, retw eets, and links). As mentioned above, it is not just the size of a professional athlete following that matters to sponsors but rather the content they are providing (Pegoraro & Jinnah, 20 12). We contend ed it is the size of the following, the content provided, and the use of linguistics tools to share content that will most effectively capitalize on the marketing and brand building opportunities that Twitter provides.

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30 Intercoder reliability was assessed using both percentage agreement and ata sets, and a co coder was used. Lombard, Snyder Duch, and Bracken (2002) suggest selecting a minimum acceptable level of reliability for the index being used. For this study, we suggest ed .80, which is considered very good agreement (Riffe Lacy, & Fico, 2005). The second coder code d 80 ( 10% ) of the tweets. The two coders agreed on all categories. This study examine d whether NBA play ers and WNBA players differ on: 1. their use of Twitter to practice celebrity 2. their use of Twitter for personal purposes 3. their use of Twitter for personal purposes or to practice celebrity based on number of followers 4. their use of Twitter tools such as hasht ags (#s), @ replies, retweets (RTs) and links Also, this study aimed to expand previous research to determine new celebrity practices on Twitter that NBA players and WNBA players should use to effectively build their brands. The first data set f rom the content analysis was analyzed using Chi S quare tests to determine if NBA players and WNBA players differ in their use of Twitter to practice celebrity. The two nominal variables that will be compared are male athletes and fem ale athletes. If the results we re significant, the findings suggest ed that NBA players and WNBA players use Twitter differently in the practice of celebrity. In the second data set, t t ests were number of followers, number follo wing, tweeting frequency, and use linguistic tools on Twitter. If the use of linguistic tools, number of followers, number following, and/or frequency differed between NBA players and WNBA players, the results suggest ed an opportunity

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31 for the athletes with the lowest averages to use these tools more effectively on Twitter to build their brand. Finally, a series of P earson Correlation Tests were run on all variables for NBA players and WNBA players separately to examine relationships between the variables. T hese tests determine d if any of the variables have a correlation to number of followers within each group.

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32 CHAPTER 4 RESULTS In the first data set, the unit of analysis was the tweets (n = 797) and in the second data set, the unit of analysis was th e athletes (n = 20; 10 NBA players and 10 WNBA players). A total of 797 tweets were analyzed due to one of the NBA athletes only tweeting 17 times from the second randomly selected date till the date the statistics were calculated. Of the 797 tweets, 397 w ere tweets from NBA players and 400 tweets were from WNBA players. A series of Chi Square Tests were run in the first data set to examine the differences in tweeting habits between NBA players and WNBA payers. Variables included weekdays, weekends, catego ries, channels, and use of the linguistic tools: @ replies, hashtags (#), retweets (RT), and links. A series of t test s and Pearson Correlation tests were used in the second data set to compare the mean scores of Twitter habits between NBA players and WNBA players, as well as examine correlations between different variables. Variables included number of followers, number following, total number of tweets, number of days to accumulate twenty tweets for each of the four randomly selected dates, and the total number of linguistic tools used. Data Set 1 Weekdays and Weekends The results were significant for the Chi Square Test examining tweets for both NBA players and WNBA players during the week (x 2 = 21,600, p = .001), but not the weekend. Of the 545 weekday t weets, NBA players tweeted 266 times (49%) on weekdays compared to 279 times (51%) by WNBA players. Of the 252 weekend tweets,

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33 NBA and WNBA players tweet similarly with 131 (52%) NBA and 121 (48%) WNBA player tweets. Overall, out of the 797 analyzed tweets 545 (68%) were during the week and 252 (32%) were on the weekends. NBA players tweeted nearly twice as many times on Tuesdays compared to WNBA players. In contrast, WNBA players tweeted nearly twice as many times on Wednesdays compared to NBA players. Bo th NBA players and WNBA players tweeted consistently on Mondays, Thursdays, and Fridays. Table 4 1: Weekday (V4) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Female Weekday Tweets (V4) 0 = Not Present 131 121 252 1 = Monday 62 76 138 2 = Tuesday 44 23 67 3 = Wednesday 49 88 137 4 = Thursday 45 41 86 5 = Friday 66 51 117 Total 397 400 797 Chi Square = 21.600, p = .001 Categories The results were significant for the Chi Square Test examining the different categories (e.g., Interactivity, Affiliation, Professional Image Sharing, Diversion, Promotional, and Fanship) used by NBA players and WNBA players (x 2 = 50.712, p < .001). All si x categories had significant differences between NBA and WNBA players. The categories were operationalized as: I NTERACTIVITY P A FFILIATION P ublicly displaying connections with other public figures in the sports, entertainment, or political industries.

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34 P ROFESSIONAL I MAGE S HARING I or profession. D IVERSION N on sports related information provided by professional athletes such as, stories, quotes, pictures videos, websites, etc. P ROMOTIONAL P ublicly promoting, advertising, and marketing sponsorships, games, events, charitable causes, etc. F ANSHIP D iscussion of athletes and teams outside of own sport, and other celebrity figures. Table 4 2: Categories (V7) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Female Categories (V7) 0 = Unknown 13 1 14 1 = Interactivity 112 135 247 2 = Affiliation 27 1 28 3 = Professional Image Sharing 78 98 176 4 = Diversion 113 85 198 5 = Promotional 42 51 93 6 = Fanship 12 29 41 Total 397 400 797 Chi Square = 50.712, p < .001 NBA players tweeted more frequently in the Affiliation and Diversion categories. Of the 28 tweets that fell in the Affiliation category, 27 (96%) were by NBA players compared to 1 (4%) by WNBA players. Of the 198 tweets that fell in the Diversion category, 113 (57%) were by NBA players compared to 85 (43%) by WNBA players. Thus NBA players displayed their publication connections with other celebrities and tweet ed about content other than basketball more than WNBA players. WNBA players tweeted more frequently in the Interactivity, Professional Image Sharing, Promotional, and Fanship categories. Of the 247 tweets that fell in the Interactivity category, 135 (55%) were by WNBA players compared to 112 (45%) by

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35 NBA players. Of the 176 tweets that fell in the Professional Image Sharing category, 98 (56%) were by WNBA players compared to 78 (44%) by NBA players. Of the 93 tweets that fell in the Promotional category, 51 (55%) were by WNBA players compared to 42 (45%) by NBA players. Of the 41 tweets that fell in the Fanship category, 29 (71%) were by WNBA players compared to 12 (29%) by NBA pl ayers. Thus WNBA players discussed their profession and promoted affiliated bra nds more than NBA players. WNBA players also showed their fanship for other celebrities and interacted with fans more frequently than NBA players. Overall, the most frequently used categories of both NBA player and WNBA player tweets were: 1) Interactivit y (31%), 2) Diversion (25%), 3) Professional Image Sharing (22%), 4) Promotional (12%), 5) Fanship (5%), and 6) Affiliation (2%). A total of 14 tweets (1%) combined by NBA players and WNBA players were unable to be determined. Specifically for NBA players the most frequently to least frequently used categories were: 1) Interactivity (28%), 2) Diversion (28%), 3) Professional Image Sharing (20%), 4) Promotional (11%), 5) Affiliation (7%), 6) Fanship (3%), and 7) Unknown (3%). Specifically for WNBA Players the most frequently to least frequently used categories were: 1) Interactivity (34%), 2) Professional Image Sharing (25%), 3) Diversion (21%), 4) Promotional (13%), 5) Fanship (7%), 6) Affiliation (0%), and 7) Unknown (0%). Channels The results were sig nificant for the Chi Square Test examining the different channels (e.g., one to one, one to many, and many to many) used by NBA and WNBA players (x 2 = 46.960, p < .001). All three channels had significant differences between NBA players and WNBA players.

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36 Table 4 3: Channels (V8) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Female Channels (V8) 0 = Unknown 6 0 6 1 = One to One 79 138 217 2 = One to Many 275 192 467 3 = Many to Many 37 70 107 Total 397 400 797 Chi Square = 46.960, p < .001 NBA players tweeted most frequently using the one to many channel compared to WNBA players tweeted most frequently in the one to one and many to many channels. Of the 217 tweets that fell into the one to one channel, 138 (64%) were by WNBA playe rs compared to 79 (36%) by NBA players. Of the 467 tweets that fell into the one to many channel, 275 (59%) were by NBA players compared to 192 (41%) by WNBA players. Of the 107 tweets that fell into the many to many channel, 70 (65%) were by WNBA players compared to 37 (35%) by NBA players. Overall, the most frequently to least frequently used channels combined of both NBA players and WNBA player tweets were: 1) one to many (60%), 2) one to one (27%), many to many (13%). A total of 6 tweets (0%) combined by NBA players and WNBA players were unable to be determined. Specifically for NBA players, the most frequently to least frequently used channels were: one to many (69%), one to one (20%), many to many (9%), and Unknown (2%). Specifically for WNBA players the most frequently to least frequently u sed channels were: 1) one to many (48%), 2) one to one (34%), and many to many (18%).

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37 Linguistic Tools The results were significant for all Chi Square T ests examining the different linguistic tools (e.g., @ reply, hashtag (#), retweet (RT), and links) used by NBA players and WNBA players (@ reply x 2 = 7.369, p = .007; hashtag x 2 = 44.657, p < .001 ; retweet x 2 = 8.516, p = .004; and links x 2 = 11.259, p = .001). @ Replies Table 4 4: At Reply (V10) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Female @ Replies (V10) 0 = Not Present 168 132 300 1 = Present 229 268 497 Total 397 400 797 Chi Square = 7.369, p = .007 Of the 797 tweets analyzed, 497 (62%) contained an @ reply. Of the 400 WNBA player tweets, 268 (67%) contained @ replies compared to 229 (58%) of the 397 NBA player tweets. Hashtags Table 4 5: Hashtag (V11) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Female Hashtag (V11) 0 = Not Present 298 370 668 1 = Present 99 30 129 Total 397 399 796 Chi Square = 44.657, p < .001 Of the 797 tweets analyzed, only 129 (16%) contained hashtags. Of the 397 NBA player tweets, 99 (25%) contained hashtags compared to only 30 (8%) of the 400 WNBA player tweets. The majority of total tweets (84%) did not contain hashtags.

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38 Retweets Table 4 6: Retweet (V12) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Female Retweets (V12) 0 = Not Present 269 308 577 1 = Present 128 92 220 Total 397 400 797 Chi Square = 8.516, p = .004 Of the 797 tweets analyzed, only 220 (28%) were retweets. Of the 397 NBA player tweets, 128 (32%) were retweets compared to only 92 (23%) of the 400 WNBA player tweets. The majority of total tweets (72%) were not retweets. Links Table 4 7: Links (V13) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Females Links (V13) 0 = Not Present 271 315 586 1 = Present 126 85 211 Total 397 400 797 Chi Square = 11.259, p = .001 Of the 797 tweets analyzed, only 211 (26%) contained links. Of the 397 NBA player tweets, 126 (32%) contained links compared to only 85 (21%) of the 400 WNBA tweets. The majority of total tweets (74%) did not contain links. Data Set 2 In the second data set, t test s were used to compare mean scores between NBA players and WNBA players for different variables (e.g., number of followers, number following, number of tweets, num ber of days to accumulate 20 tweets for each of the four randomly selected dates, and the total number of linguistic tools used). Of the six variables, only number of followers (t = 10.301, p < .001) and number of following (t =

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39 3.039, p = .007) were significant. However, there are important notes on some of the non significant variables. Table 4 8 : Means (Standard Deviation) and T Test Results SPSS Column Variable NBA Mean (Std. Deviation) WNBA Mean (Std. Deviation) t Sig. (2 tailed) nufollow1 Number of Followers 5.4000 (.96609) 1.5000 (.70711) 10.301 .000 V4 Number Following 590.40 (313.930) 262.00 (134.893) 3.039 .007 V5 Number of Tweets 6614.10 (1858.575) 10238.20 (10047.248) 1.122 .277 V6 Number of Days to Accumulate 20 Tweets Date 1 15.10 (20.792) 9.80 (9.987) .727 .477 V7 Number of Days to Accumulate 20 Tweets Date 2 16.70 (14.538) 10.70 (11.672) 1.018 .322 V8 Total Number of Linguistic Tools 58.50 (23.609) 47.50 (15.204) 1.239 .231 Number of Followers The initial results of the t test between gender and number of followers were not significant (t = 1.803, p = .088). Upon closer examination, the mean scores for NBA players were 749,802.50 and the mean scores for WNBA players were 6,129.60. Actual data sh owed NBA players had significantly more followers than WNBA players that caused statistical problems when running the t test Due to the substantial different actual means, the number of followers were condensed into a variable that ranged between 1 and 7 to lessen the variance (see Table 4 10 ).

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40 Table 4 9 : Number of Followers (V3) vs. Gender (V2) T Test Gender (V2) N Mean Std. Deviation Std. Error Mean Number of Actual Followers (V3) 0 = Male 10 749802.50 1304439.630 412500.030 1 = Female 10 6129.60 5294.620 1674.306 t t est = 1.803, p = .088 Table 4 10 : Condensed Number of Followers Variable (nufollow1) Variable Category Number of Actual Followers 1 1 5,000 2 5,001 10,000 3 10,001 50,000 4 50,001 100,000 5 100,001 200,000 6 200,001 500,000 7 x > 500,000 Table 4 11 : Number of Followers (nufollow1) vs. Gender (V2) T Test Gender (V2) N Mean Std. Deviation Std. Error Mean Number of Follower Variable Category (nufollow1) 0 = Male 10 5.4000 .96609 .30551 1 = Female 10 1.5000 .70711 .22361 t t est = 10.301, p < .001 After condensing the number of followers into a range d 1 7 variable, results of the t t est between gender and number of followers were significant (t = 10.301, p < .001). The mean score for NBA players was 5.4 compared to 1.5 for WNBA players. This result shows that NBA players are followed 3.6 times more than WNBA players on average. In actual data, NBA players had 122 times more followers than WNBA players on average.

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41 Number Following The results of the t t est between gender and number following were significant (t = 3.039, p = .007). On average NBA players (mean = 590.40) followed more accounts than WNBA players (mean = 262.00). The results showed that NBA players follow other Twitters users 2.25 times more th an WNBA players on average. Table 4 12 : Number Following (V4) vs. Gender (V2) T Test Gender (V2) N Mean Std. Deviation Std. Error Mean Number Following (V4) 0 = Male 10 590.40 313.930 99.273 1 = Female 10 262.00 134.893 42.657 t t est = 3.039, p = .007 Number of Tweets The results of the t t est between gender and number of tweets were not significant (t = 8.516, p = .277). However, the mean score for WNBA players was 10,328 and the mean score for NBA players was 6,614. Upon closer examination a significant variance was noticed between the most frequent and least fre quent tweeters. Out of the top 8 tweeters, 6 were WNBA players and the 3 lowest tweeters were also WNBA players (see Table 4 14 ). The results showed that WNBA players tended to either tweet a lot or rarely in comparison to NBA players. Table 4 13 : Number of Tweets (V5) vs. Gender (V2) T Test Gender (V2) N Mean Std. Deviation Std. Error Mean Number of Tweets (V5) 0 = Male 10 6614.10 1858.575 587.733 1 = Female 10 10238.20 10047.248 3177.219 t t est = 8.516, p = .277

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42 Table 4 14 : Number of Actual Tweets (V5) vs. Gender (V2) Chi Square Gender (V2) Total 0 = Male 1 = Female Number of Actual Tweets (V5) 139 0 1 1 1499 0 1 1 3614 0 1 1 3760 1 0 1 4404 1 0 1 4691 0 1 1 5276 1 0 1 5504 1 0 1 6884 1 0 1 7151 1 0 1 7316 1 0 1 7672 1 0 1 7833 0 1 1 8442 1 0 1 9317 0 1 1 9721 0 1 1 9732 1 0 1 11095 0 1 1 21321 0 1 1 33152 0 1 1 Total 10 10 20 Total Number of Linguistic Tools The results of th e t t est between gender and number of linguistic tools were not significant (t = 1.239, p = .231). However, as the results from first data set showed, NBA players and WNBA players had significant differences in all four linguistic tools. The results from the Chi Square Tests in the first data set comp ared with the results with the t t est in the second data set show that while NBA players and WNBA players use different linguistic tools, the frequency of linguistic tools used in tweets is simila r.

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43 Table 4 15 : Total Number of Linguistic Tools (V8) vs. Gender (V2) T Test Gender (V2) N Mean Std. Deviation Std. Error Mean Total Number of Linguistic Tools (V8) 0 = Male 10 58.50 23.609 7.466 1 = Female 10 47.50 15.204 4.808 t t est = 1.239 p = .231 Tables 4 16 and 4 17 provide overall descriptive statistics for both NBA players and WNBA players in the second data set. Minimum, maximum, and mean values are presented for all variables and include standard deviation values. Table 4 16 : NBA Play er Descriptive Statistics N Minimum Maximum Mean Std. Deviation Number of Actual Followers (V3) 10 71806 4075502 749802.50 1304439.630 Number of Follower Variable Category (nufollow1) 10 4.00 7.00 5.4000 .96609 Number Following (V4) 10 243 1309 590.40 313.930 Number of Tweets (V5) 10 3760 9732 6614.10 1858.575 Number of Days to Accumulate 20 Tweets Date 1 (V6) 10 1 73 15.10 20.792 Number of Days to Accumulate 20 Tweets Date 2 (V7) 10 4 50 16.70 14.538 Total Number of Linguistic Tools (V8) 10 31 103 58.50 23.609

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44 Table 4 17 : WNBA Players Descriptive Statistics N Minimum Maximum Mean Std. Deviation Number of Actual Followers (V3) 10 1700 19639 6129.60 5294.620 Number of Follower Variable Category (nufollow1) 10 1.00 3.00 1.5000 .70711 Number Following (V4) 10 64 510 262.00 134.893 Number of Tweets (V5) 10 139 33152 10238.20 10047.248 Number of Days to Accumulate 20 Tweets Date 1 (V6) 10 1 35 9.80 9.987 Number of Days to Accumulate 20 Tweets Date 2 (V7) 10 2 33 10.70 11.672 Total Number of Linguistic Tools (V8) 10 27 80 47.50 15.204 NBA Players and WNBA Players Pearson Correlation Tests Finally, a series of Pearson Correlation Tests were run of all variables for NBA players and WNBA players separately to examine relationships between the variables. Only one correlation was statistically significant between the variables of the second data set. The number of days to accumulate 20 tweets on the first randomly selected date and the second randomly selected date were correlated for both NBA players and WNBA players (NBA players r = .793, p = .006; WNBA players r = .625, p = .053). The results suggest that tweeting habits for both NBA players and WNBA players are similar across time.

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45 Table 4 18 : All Variables for NBA Pl ayers Pearson Correlation Test Number of Actual Followers (V3) Number of Follower Variable Category (nufollow1) Number Following (V4) Number of Tweets (V5) Number of Days to Accumulate 20 Tweets Date 1 (V6) Number of Days to Accumulate 20 Tweets Date 2 (V7) Total Number of Linguistic Tools (V8) Number of Actual Followers (V3) Pearson Correlation 1 .686 .115 .396 .024 .069 .309 Sig. (2 tailed) .028 .752 .257 .948 .850 .385 N 10 10 10 10 10 10 10 Number of Follower Variable Category (nufollow1) Pearson Correlation .686 1 .555 .341 .313 .373 .487 Sig. (2 tailed) .028 .096 .335 .378 .288 .153 N 10 10 10 10 10 10 10 Number Following (V4) Pearson Correlation .115 .555 1 .240 .228 .261 .282 Sig. (2 tailed) .752 .096 .505 .527 .467 .430 N 10 10 10 10 10 10 10 Number of Tweets (V5) Pearson Correlation .396 .341 .240 1 .046 .124 .271 Sig. (2 tailed) .257 .335 .505 .899 .732 .449 N 10 10 10 10 10 10 10 Number of Days to Accumulate 20 Tweets Date 1 (V6) Pearson Correlation .024 .313 .228 .046 1 .793 ** .044 Sig. (2 tailed) .948 .378 .527 .899 .006 .903 N 10 10 10 10 10 10 10 Number of Days to Accumulate 20 Tweets Date 2 (V7) Pearson Correlation .069 .373 .261 .124 .793 ** 1 .180 Sig. (2 tailed) .850 .288 .467 .732 .006 .618 N 10 10 10 10 10 10 10 Total Number of Linguistic Tools (V8) Pearson Correlation .309 .487 .282 .271 .044 .180 1 Sig. (2 tailed) .385 .153 .430 .449 .903 .618 N 10 10 10 10 10 10 10

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46 Table 4 19 : All Variables for WNBA Players Pearson Correlation Test Number of Actual Followers (V3) Number of Follower Variable Category (nufollow1) Number Following (V4) Number of Tweets (V5) Number of Days to Accumulate 20 Tweets Date 1 (V6) Number of Days to Accumulate 20 Tweets Date 2 (V7) Total Number of Linguistic Tools (V8) Number of Actual Followers (V3) Pearson Correlation 1 .914 ** .095 .126 .157 .122 .132 Sig. (2 tailed) .000 .794 .729 .665 .737 .717 N 10 10 10 10 10 10 10 Number of Follower Variable Category (nufollow1) Pearson Correlation .914 ** 1 .218 .056 .031 .061 .109 Sig. (2 tailed) .000 .545 .877 .931 .868 .765 N 10 10 10 10 10 10 10 Number Following (V4) Pearson Correlation .095 .218 1 .313 .390 .408 .119 Sig. (2 tailed) .794 .545 .379 .265 .242 .742 N 10 10 10 10 10 10 10 Number of Tweets (V5) Pearson Correlation .126 .056 .313 1 .494 .227 .195 Sig. (2 tailed) .729 .877 .379 .146 .528 .590 N 10 10 10 10 10 10 10 Number of Days to Accumulate 20 Tweets Date 1 (V6) Pearson Correlation .157 .031 .390 .494 1 .625 .241 Sig. (2 tailed) .665 .931 .265 .146 .053 .503 N 10 10 10 10 10 10 10 Number of Days to Accumulate 20 Tweets Date 2 (V7) Pearson Correlation .122 .061 .408 .227 .625 1 .440 Sig. (2 tailed) .737 .868 .242 .528 .053 .203 N 10 10 10 10 10 10 10 Total Number of Linguistic Tools (V8) Pearson Correlation .132 .109 .119 .195 .241 .440 1 Sig. (2 tailed) .717 .765 .742 .590 .503 .203 N 10 10 10 10 10 10 10

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47 CHAPTER 5 CONCLUSIONS Summary The results from the study showed NBA players and WNBA players used Twitter similarly in some ways and differently in others. While examining both user generated content and the use of linguistic tools, almost all variables tested in the first and second data sets had significant differences between the two groups. Time and Frequency NBA players and WNBA players had similar tweeting habits on weekdays and weekends. Of all the tweets analyzed, 68% were on weekdays and 32% were on weekends. While the results showed that NBA players tweet more frequently on Tuesdays and Saturdays, and WNBA players tweet more frequently on Wednesdays and Sundays, these results do not suggest any major impact differences. It is more important to note that both groups twee t more frequently during the weekday than the weekend. Finally, this study found similarities in the tweeting frequency of NBA players and WNBA players across time. However, differences were found in the overall total tweets between NBA players and WNBA pl ayers. Of the 20 professional athletes analyzed, six out of the top eight tweeters were WNBA players and the three lowest tweeters were also WNBA players. This result suggests that WNBA players either tweet a lot or a little in comparison to NBA players. C ategories The user generated content of tweets was the most important variable in the study to examine the similarities and differences of practicing celebrity on Twitter between NBA players and WNBA players. According to Marwick and boyd (2011),

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48 practicin g celebrity on Twitter is the appearance and performance of backstage access by celebrity practitioners, referred to as professional athletes in this study. Similarly, Hambrick et al. (2010) found that athletes with the most followers had more interacti vity with fans. The results of this study found WNBA players leading four (e.g., Interactivity, Professional Image Sharing, Promotional, and Fanship) of the six categories, while NBA players led two (e.g., Affiliation and Diversion) categories. WNBA player s tended to practice celebrity in tweets by interacting with fans, showing fanship of other celebrities, and through promoting their professional image and affiliated brands more than NBA players. In contrast, NBA players practiced celebrity on Twitter by displaying public connections with other celebrities and by providing backstage access into their personal lives. This may be plausible due to the possibility that NBA players knowing more celebrities compared to WNBA players. For example, Kobe Bryant, of the Los Angeles Lakers is arguably one of the most famous and best players in the NBA. Several celebrities frequently attend his games including season ticket holder Jack Nicholson. Also, Dwayne Wade, of the Miami Heat is in high profile relationship with celebrity actress Gabrielle Union. Therefore, NBA players may tweet to other celebrities more frequently simply because they know a lot of them. While it is important to know which of the two groups led each category, it is also important to examine the ov erall similarities between the user generated content of NBA players and WNBA players. Of all tweets, the most to least frequently tweeted categories were; 1) Interactivity (31%), 2) Diversion (25%), 3) Professional Image Sharing (22%), 4) Promotional (12% ), 5) Fanship (5%), and 6) Affiliation (2%).

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49 Interactivity was the top tweeted category for both NBA players and WNBA players. Both groups had Diversion (NBA second most, WNBA third most) and Professional Image Sharing (NBA third most, WNBA second most) in their top second and third categories, and Promotional ranked fourth for both groups. These results suggest both NBA players and WNBA players use Twitter to practice celebrity by sharing personal information, publicly acknowledging fans, and promoting the ir professional image and affiliated brands. Channels Twitter has also been credited with providing direct access -or at least the perception of direct access to professional athletes without having traditional filters of managers, public relations tea ms, and the mainstream media. Therefore, this study examined and found differences in the channels (e.g., one to one, one to many, and many to many) used by NBA players and WNBA players to communicate on Twitter. The study found WNBA players leading the on e to one and many to many channels while NBA players led the one to many channels. NBA players most frequently tweeting the one to many channel suggest the recognition of power differentials between themselves and their followers. This recognition of power differentials allows NBA players to treat their followers as a fan base which Marwick and boyd (2011) found as a crucial component in practicing celebrity on Twitter. It also is plausible they have bigger egos and are more self centered compared to lesser known WNBA players. WNBA players also tweeted most frequently using the one to many channel, but also tweeted the one to one channel almost twice as often as NBA players. These results suggest that WNBA players, similar to NBA players, treat their followe rs as a fan base while also taking the time to interact with fans on a personal level. Similar to the Hambrick et al.

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50 (2010) study, this study also found Twitter to be a valuable platform to shorten the distance between fans and the game, and proved partic ularly useful for lesser known professional athletes. Linguistics Tools Similarities and differences between NBA players and WNBA players were also found in the use of linguistic tools. Linguistic tools have become increasingly important due to a cultural linguistic tools used between NBA players and WNBA players, however, significant differences were foun d in what linguistic tools were used (e.g., @ reply, hashtag, retweet, and links). WNBA players led in @ replies over NBA players, while NBA players led in hashtags, retweets, and links over WNBA players. Interestingly, of all tweets analyzed, 62% containe d an @ reply, which is contrary to the Page (2012) study that found 63% of celebrity practitioners tweets did not contain an @ reply. This finding suggests both NBA players and WNBA players practice celebrity on Twitter, in regards to interactivity, more e ffectively than celebrities in other industries. It is also plausible that NBA players and WNBA players have learned to use linguistic tools more effectively because they are more common now than when the Page (2012) study was conducted. In contrast, only 28% of tweets were retweets, 26% contained links, and 16% contained hashtags. These findings indicate a missed opportunity by NBA players and WNBA players with a number of recent studies (Page, 2012; Zappavigna, 2011; boyd et al., 2010; Honeycutt & Herring 2009) suggesting the use of linguistic tools aids professional athletes in practicing celebrity on Twitter.

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51 Number of Followers The mean score for NBA players (749,802.50) was significantly higher than the mean score for WNBA players (6,129.60). Using ra w data, NBA players had 122 times more followers than WNBA players on average, with the top NBA player having 4,075,502 followers compared to the top WNBA player having 19,639 followers. Due the extreme variance, Pearson Correlations Tests were run separat ely for NBA players and WNBA players to examine if the different variables within each group positively affected number of followers. Interestingly for both NBA players and WNBA players, no correlation was found between number of followers versus the numbe r following, number of tweets, tweeting frequency, and total number of linguistic tools. This finding suggests that while Twitter has shortened the distance between fans and the game, there are still extreme differences in the amount of reach between NBA p layers and WNBA players. However, while reach may be different based on the number of followers, total impressions Finally, it was expected to find NBA players having more followers than WNBA players because of the difference in popularity and coverage in the mainstream media, however the extreme variance of 122 times more was unexpected. Pegoraro and Jihnnah (2012) suggest that sponsors not only look to capitalize on professional WNBA players can control content, the results from the current study highlight the existing popularity differential between male and female athletes that puts them at a

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52 disadvantage of being considered for sponsorship opportunities compared to NBA players. Overall Conclusions and Implications Both NBA players and WNBA players use Twitter to practice celebrity. The study extends previous resea rch on practicing celebrity on Twitter by providing an in depth analysis of both user generated content and the use of linguistic tools. Both NBA players and WNBA players practice celebrity on Twitter through continuous maintenance of fan bases, performed intimacy, authenticity and access, construction of a consumable persona, and recognition of power differentials between themselves and their fans (Marwick and boyd, 2011). Also, both NBA players and WNBA players adjust their tweets to address their follow information about their profession, promoting affiliated brands, showing fanship for other celebrities, and using links to share pictures, videos, and websites (Hambrick et al., 2010). Unlike previous re search, this study is the first to concurrently examine all components of practicing celebrity on Twitter while comparing the differences between male and female athletes. This study concludes that while both NBA players and WNBA players practice celebrity on Twitter, the content in tweets and the use of specific linguistics tools in tweets may vary. This variance did not indicate that one group practiced celebrity more effectively than the other, but rather they practiced celebrity differently at times. Fi nally, while Twitter provides an equal platform for NBA players and WNBA players to practice celebrity and build their brands, it still favors NBA players over WNBA players similar to the mainstream media. This favoritism may be plausible due to fans more frequently searching out NBA players on Twitter over WNBA players because they are covered considerably more in the mainstream media. Therefore, the

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53 popularity differential between NBA players and WNBA players still exists in the social media world. Limita tions Twitter only allows the 3,200 most recent tweets to be retrieved for each account. Two of the randomly selected WNBA players had significant followers, however, they could not be used in the study because their tweets during the two randomly selected dates preceded the 3,200 retrievable limit. The study also found limitations in analyzing only 20 tweets on each of the four randomly selected dates. Both groups had players removed from the study due to a lack of tweets, however it may be valuable to exa mine how inactive users still have considerable followers. Finally, WNBA players were removed for having locked accounts that therefore meant their tweets were inaccessible. These WNBA players also had considerable number of followers compared to number fo llowing and could prove valuable in examining if a perceived Recommendations for Future Research Due to limited time and resources only 10 NBA and 10 WNBA accounts were examined. It would be beneficial in futur e research to examine more NBA players and WNBA players, and also cross examine them with other professional athletes and celebrities to determine similarities and differences. followers. Valuable information could be gained by understanding the differences not only in the tweeting habits of male and female professional athletes, but also the tweeting habits of male and female fans. NBA players and WNBA players could

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54 continue to adjust their tweets to meet their fans needs and in return possibly gain followers and increase engagement. Engagement should be a priority for future research. While tweeting habits, user generated content, and use of linguistic tools were examined and co mpared to the total number of followers; engagement is a significant factor in the effectiveness of tweets that was not examined. Grudz et. al (2011) found the power of influence a professional athlete has is indicated by the amount their followers retweet their content. Examining the number of engaged users, impressions, reach, clicks, and virality of tweets can better assist NBA players and WNBA players in understanding what content has the most engagement with fans. A mixed methods study would provide a more in depth analysis by comparing quantitative data with qualitative insights. Interviews with NBA players and WNBA players compared with interviews with selected followers would offer a richer understanding of how practicing celebrity on Twitter correla tes to the likeability and brand building efforts of professional athletes. Without qualitative data examining how fans behaviors are changed through the practice of celebrity on Twitter it may be impossible to truly understand its effectiveness. Finally, the popularity and coverage of NBA players and WNBA players in the mass media should be compared to the popularity and coverage on Twitter. The operationalization of popularity and coverage could prove difficult across the two media, however it would be be neficial to understand how practicing celebrity on Twitter can present ed to them in the mass media.

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55 LIST OF REFERENCES boyd, D., & Ellison, N. (2007) Social network sites : Definition, history, andscholarship. Journal of Computer Mediated Communication, 13(1) 210 230. doi: 10.1111/j.1083 6101.2007.00393.x boyd, D., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. Pape r presented at the System Sciences (HICSS), 2010 43rd Hawaii International Conference on System Sciences 1 10. Burton, S., & Soboleva, A. (2011). Interactive or reactive? Marketing with Twitter. The Journal of Consumer Marketing, 28(7) 491 499. doi: 10.1108/0736371111181473 Chen, G. (2010). Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others. Computers in Human Behavior, 27(2) 755 762. Edosomwan, S., Prakasan, S., Kouame, D., Watson, J ., & Seymour, T. (2011). The history of social media and its impact on business. Journal of Applied Management and Entrepreneurship, 16 (3) 79 91. ESPN. (2013). NBA attendance report 2013 Retrieved from http://espn.go.com/nba/attendance Gruzd, A., We llman, B., & Takhteyev, Y. (2011) Imagining Twitter as an imaginedcommunity. American Behavioral Scientist, 55(10) 1294 1318. doi: 10.1177/000276211409378 Hambrick, M., Simmons, J., Greenhalgh, G., & Greenwell, T.C. (2010). Understanding professional at tweets. International Journal of Sport Communication, 3(4) 454 471. Hargittai, E., & Litt, E. (2011) The tweet smell of celebrity success: Explaining variation in Twitter adoption among a diverse group of young adults. New Media Society, 13(1) 824 842. doi: 10.1177/1461444811405805 Honeycutt, C., & Herring, S. C. (2009). Beyond microblogging: Conversation and collaboration via twitter. Paper presented at the System Sciences (HICSS), 2009 42nd H awaii International Conference on System Sciences 1 10. Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? In proceedings of the 19th international conference on World Wide Web (WWW '10). ACM New York, N Y, USA, 591 600. doi: 10.1145/1772690.1772751

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56 Lombard, M., Snyder Duch, J., & Bracken, C.C. (2002). Content analysis in mass communication: Assessment and reporting of intercoder reliability. Human Communication Research, 28(4) 587 604. Marwick, A., & boyd, D. (2011). To see and be seen: Celebrity practice on Twitter. Convergence: The International Journal of Research into New Media Technologies, 17(2) 139 158. doi: 10.1177/1354856510394539 Marwick, A., & boyd, D. (2010). I tweet honestly, I tweet p assionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1) 114 133. doi: 10.1177/1461444810365313 McManus, J. (2012, February). Retrieved from http://espn.go.com/e spnw/commentary/7533063/ athletes tweet their way fans lives Page, R. (2012). The linguistics of self branding and micro celebrity in Twitter: The role of hashtags. Discourse & Communication, 6(2) 181 201. doi: 10.1177/1750481312437441 Paraham N. (2011, March). Twitter & pro athletes: How should sports leagues respond when keeping it real goes wrong? Retrieved from http://www.swishappeal.com/2011/3/13/2047933/twitter professional athletes pro sports leagues Parmentier, M A., & Fischer, E. ( 2012). How athletes build their brands. International Journal of Sport Management and Marketing, 11(1 2) 106 124. ponsorship opportunities. Journal of Brand Strategy, 1(1) 85 97. Riffe, D., Lacy, S., & Fico, F. (2005). Analyzing media messages: Using quantitative content analysis in research (2 nd ed.) Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Riva, R. (2012, August 10). [Web log post]. Retrieved from http://451hea t.com/2012/08/10/professional a thletes branding through twitter/ Roberts, J.J. (2012, September). Pro athletes on Twitter: League exec s say marketing outweighs gaffes. Retrieved from http://gigaom.com/2012/09/24/pro athletes on twitter execs say marketing outweighs gaffes/

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57 Senft, T. (2008) Camgirls: Celebrity and community in the age of social networks. New York: Peter Lang. Skolnick, E. (2011, November). #Not all fun and games 4 athletes Retrieved from http://nbcsports.msnbc.com/id/45060764/ns/sports other_sports/page/3/ Smith, A., Fischer, E., & Yongjian, C. (2012) How does brand related user generated content differ acr oss YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(1) 102 113. doi: 10.1086/378616 Sports Business Daily. (2012, August). WNBA attendance down slightly as league takes month long Olympic b reak. Retrieved from http://www.sportsbusin essdaily.com/Daily/Issues/2012/08/01/Research and Ratings/WNBA gate.aspx Yan, J. (2011). Social media in branding: Fulfilling a need. Journal of Brand Management, 18(9) 688 696. doi: 10.1057/bm.2011.19 Zappavigna, M. (2011) Ambient affiliation: A lingu istic perspective of Twitter. New Media & Society, 13(5) 788 806. doi: 1077/1461444810385097

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58 BIOGRAPHICAL SKETCH Kristina Netzler was raised in the small town of Wautoma, WI. She graduated from Wautoma High School in May of 2003. Following high school graduation, Kristina immediately jumped into her new role as a college student. She received a basketball scholarship and took time to determine which career path to follow. She eventually found the perfect major, Marketing, and graduated in Ma y of 2007 with a Bachelor of Science degree from the University of Florida. Kristina was the first member of the Netzler family to graduate from college. During the summer following her junior year, Kristina received an opportunity to work for the NBA and WNBA Minnesota Timberwolves and Lynx franchise. She spent her senior year interning full time in Minneapolis, Minnesota and commuting weekly to La Crosse, Wisconsin to complete her final year of education. Kristina spent the next six years working for the NBA and WNBA Seattle Sonics and Storm, Fresno State Athletics Department, and the University Athletic Association with focuses in marketing, advertising, and retention. In January 2012, she was accepted into the College of Journalism at the University of F lorida to pursue a Master of Advertising. During that same time, Kristina was offered a New Student and Family Programs Graduate Assistantship position in the Dean of Student Office of the Division of Student Affairs. mple: pursue a career that positively impacts being happy.