Analysis of Retweeting Behavior Using Topic Models
Social networks are nowadays a constant presence in our lives and increasingly have a role in important social and commercial phenomena. Microblogging services such as Twitter appear to play an important role in the process of information dissemination on the Internet making it possible for messages to spread virally in a matter of minutes. In this research work we study the mechanism of re-broadcasting (called “retweeting”) information on Twitter; specifically we use Latent Dirichlet Allocation to analyze users and messages in terms of the topics that compose their text bodies and by means of ANOVA we are able to show that the topical distance between users and messages is shorter for tweets that are retweeted than for those that are not. Using Decision Tree learning we build several models in order to assess the accuracy and usefulness of our topic-based model of retweeting. Our results show that our topic-based model slightly outperforms a baseline prediction measure, so we conclude that such model is indeed a valid option to consider for predicting retweet behavior with possibilities open for improvement.
Graduation Thesis language
Graduation Thesis type
Master - Information Technology