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Brand Messages on Twitter: Predicting Diffusion with Textual CORE Metadata, citation and similar papers at core.ac.uk Provided by Carolina Digital Repository BRAND MESSAGES ON TWITTER: PREDICTING DIFFUSION WITH TEXTUAL CHARACTERISTICS Chris J. Vargo A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the School of Journalism and Mass Communication. Chapel Hill 2014 Approved by: Joe Bob Hester Donald Lewis Shaw Francesca Carpentier Justin H. Gross Jaime Arguello © 2014 Chris J. Vargo ALL RIGHTS RESERVED ii ABSTRACT Chris J. Vargo: Brand Messages On Twitter: Predicting Diffusion With Textual Characteristics (Under the direction of Joe Bob Hester) This dissertation assesses brand messages (i.e. tweets by a brand) on Twitter and the characteristics that predict the amount of engagement (a.k.a. interaction) a tweet receives. Attention is given to theories that speak to characteristics observable in text and how those characteristics affect retweet and favorite counts. Three key concepts include sentiment, arousal and concreteness. For positive sentiment, messages appeared overly positive, but still a small amount of the variance in favorites was explained. Very few tweets had strong levels of arousal, but positive arousal still explained a small amount of the variance in retweet counts. Despite research suggesting that concreteness would boost sharing and interest, concrete tweets were retweeted and shared less than vague tweets. Vagueness explained a small amount of the variance in retweet and favorite counts. The presence of hashtags and images boosted retweet and favorite counts, and also explained variance. Finally, characteristics of the brand itself (e.g. the number of followers the brand had, the number of users it followed and its overall reputation of the brand) boosted retweet and favorite counts, and also explained variance. iii To God, my wife Laura, my family and my friends. Thank you for giving me the energy to pursue the career of my dreams. iv ACKNOWLEDGEMENTS There are several people that have made this dissertation possible outside of my family and friends. First, I thank Dr. Joe Bob Hester. Without his intelligence, diligence and responsiveness, I do not know how long this may have taken. I am extremely grateful. Second, Dr. Donald Lewis Shaw has been a true mentor. I owe him more than words. Third, Scott Bradley contributed to Python code used in scoring tweets for concreteness, sentiment and arousal. Moreover, he helped put out fires in the data collection process. He is a brilliant coder. Finally, I thank Donald Sizemore “cat great friend|awk great sys admin” and Mike Sharpe. I was extremely fortunate to have the best technology specialists there are. v PREFACE This work was inspired primarily by two books by which I was intrigued, “Made to Stick” by Chip Heath and Dan Heath and “Contagious” by Jonah Berger. You will see some of the core concepts of these books tested here. The idea of why certain things catch on (get shared) on social media is a puzzle with an infinite amount of pieces. This dissertation offers two or three pieces to this puzzle. I hope other scholars will contribute more. vi TABLE OF CONTENTS LIST OF TABLES ....................................................................................................................... xi LIST OF FIGURES .................................................................................................................... xii Chapter 1: Literature Review ...................................................................................................... 1 The Origin of Brands on Social Media ................................................................................... 1 Brand Behavior and Goals on Social Media ........................................................................... 6 Virality .................................................................................................................................... 8 Viral Marketing is Not Viral ................................................................................................. 10 Influential Followers vs. a Ton of Followers: Which is Better? ........................................... 12 Brand Differences by Type ................................................................................................... 16 Brand Differences by Connection Type ............................................................................... 17 Message Content and Virality ............................................................................................... 19 Emotion ................................................................................................................................. 21 Emotions and Advertising ..................................................................................................... 22 Emotion as a Measured Characteristic of Text ..................................................................... 25 Emotion as a Measured Characteristic of Text: Arousal ...................................................... 28 Defining Concreteness .......................................................................................................... 32 Concreteness and Advertising ............................................................................................... 36 From Concreteness to Interest to Virality ............................................................................. 39 vii Other Characteristics of a Social Media Message ................................................................ 43 Summary of Literature Review ............................................................................................. 46 Chapter 2: Method ...................................................................................................................... 48 Selection of the Brands ......................................................................................................... 48 Variables ............................................................................................................................... 49 Retrieving Tweets from Twitter ............................................................................................ 50 Human Reliability Check: Sentiment, Arousal and Concreteness ........................................ 53 Computer Automated Measure of Arousal ........................................................................... 55 Computer Automated Measure of Concreteness .................................................................. 57 Establishing Reliability for Computer Coded Variables ...................................................... 57 Creating Interactions for Concreteness ................................................................................. 60 Preparation for Regression .................................................................................................... 60 Chapter 3: Results ....................................................................................................................... 61 RQ1: Retweet and Favorite Counts ...................................................................................... 61 RQ2: Number of Followers .................................................................................................. 61 RQ3: Differences by Brand .................................................................................................. 62 RQ4: Following Counts ........................................................................................................ 64 RQ5: Sentiment ..................................................................................................................... 65 RQ6: Arousal ........................................................................................................................ 65 RQ7: Concreteness ................................................................................................................ 66 RQ8: Favorites Predicting Retweets ..................................................................................... 69 RQ9: URLs ........................................................................................................................... 69 RQ10: Hashtags .................................................................................................................... 69 viii RQ11: Images ....................................................................................................................... 70 RQ12: “All in” predictive model .......................................................................................... 70 Chapter 4: Conclusion ................................................................................................................ 73 How Viral are Brand Messages? ........................................................................................... 73 Follower Counts .................................................................................................................... 74 Differences by Brand Type ................................................................................................... 74 Sentiment .............................................................................................................................. 75 Arousal .................................................................................................................................. 76 Concreteness ........................................................................................................................
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