Modeling topic specific credibility on twitter

  • Authors:

  • Byungkyu Kang;John O'Donovan;Tobias Höllerer

  • Affiliations:

  • University of California, Santa Barbara, Santa Barbara, California, United States;University of California, Santa Barbara, California, United States;University of California Santa Barbara, Santa Barbara, California, United States

  • Venue:
  • Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

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.