Celebrity and fandom have been studied extensively in real life. However, with more and more celebrities using social media, the dynamics of interaction between celebrities and fans has changed. Using data from a set of 57,000 fans for the top followed celebrities on Twitter, we define a wide range of features based on their Twitter activity. Using factor analysis we find the most important factors that underlie fan behavior. Using these factors, we conduct analysis on (i) understanding fan behavior by gender & age, and (ii) para-social breakup behavior. We find that (i) fandom is a social phenomenon, (ii) female fans are often more devoted and younger fans are more active & social, and (iii) the most devoted fans are more likely to be involved in a para-social breakup. Our findings confirm existing research on para-social interactions. Given the scale of our study and dependence on non-reactive data, our paper opens new avenues for research in para-social interactions.
: @cite_12 introduce the idea of a para-social breakup, the end of a PSR. They find that a para-social breakup is like a regular romantic relationship breakup but not as intense. Breakups on social media have been studied in the past @cite_3 . We define a para-social breakup on Twitter as an act of unfollowing the celebrity and study the impact of fan behavior on a breakup. Work has been done on reasons for unfollowing others on Twitter @cite_10 . conclude that most users unfollow those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives.'. In our case of a para-social breakup, we find that most unfollowing happens as an act of defiance against the celeb and despair of not receiving the personal attention that fans perceive they deserve.
@cite_3 @cite_10 @cite_12
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We use public data from Twitter to study the breakups of the romantic relationships of 661 couples. Couples are identified through profile references such as @user1 writing “@user2 is the best boyfriend ever!!”. Using this data set we find evidence for a number of existing hypotheses describing psychological processes including (i) pre-relationship closeness being indicative of post-relationship closeness, (ii) “stonewalling”, i.e., ignoring messages by a partner, being indicative of a pending breakup, and (iii) post-breakup depression. We also observe a previously undocumented phenomenon of “batch un-friending and being un-friended” where users who break up experience sudden drops of 15-20 followers and friends. This study examines viewers' reactions to parasocial breakup with mediated characters in light of interpersonal and mass communication theories. Following the airing of the last episode of the television show Friends, 279 students completed surveys assessing their viewing habits, their attitudes toward the show and their favorite character, and their loneliness. The intensity of the parasocial relationship with the favorite character is the strongest predictor of breakup distress. Other predictors include commitment and affinity to the show, the character's perceived popularity, and the participant's loneliness. The results shed light on the similarities and differences between parasocial and social relationships. We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results. They unfollowed those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives. To the best of our knowledge, this work is the first systematic study of the unfollow behavior in Twitter. This study investigates unfollow behavior in Twitter, i.e. people removing others from their Twitter following lists. Considering the interdependency and dynamics of unfollow decisions, we use actor-oriented modeling (SIENA) to examine the impacts of reciprocity, status, embeddedness, homophily, and informativeness on tie dissolution. Focusing on ordinary users in tightly-knitted user groups, the results show that relational properties play key roles in the emergence of unfollow behavior: mutual following relations and common followees reduce the likelihood of unfollowing. And unfollow tends to be reciprocal: when a user is unfollowed by someone, he or she will unfollow back. However, there is no evidence of the impacts of homophily based on common interests and informativeness of interactions. The findings suggest that Twitter has many heterogeneous user groups and relational and informational factors may not be applicable universally.