Predicting Rising Follower Counts on Twitter Using Profile Information
This work addresses the problem of understanding and predicting social media popularity for users and platforms, but it is incremental as it builds on existing studies of name correlations.
The paper tackled the problem of predicting rising follower counts on Twitter by analyzing profile information, specifically focusing on the influence of names and words in the name field, and achieved an AUC score above 0.800 with their proposed classifier.
When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.