Modeling topic specific credibility on twitter

Abstract

This paper presents and evaluates three computational models for recommending credible topic-specific information in Twitter. The first model focuses on credibility at the user level, harnessing various dynamics of information flow in the underlying social graph to compute a rating. The second model applies a content-based strategy to compute a finer-grained credibility score for individual tweets. Lastly, we discuss a third model which combines facets from both models in a hybrid method, using both averaging and filtering hybrid strategies. To evaluate our novel credibility models, we perform an evaluation on 7 topic specific data sets mined from the Twitter streaming API, with specific focus on a data set of 37K users who tweeted about the topic "Libya". Results show that the social model outperfoms hybrid and content-based prediction models in terms of predictive accuracy over a set of manually collected credibility ratings on the "Libya" dataset.