Ranking Tweets Considering Trust and Relevance
This work addresses the need for more trustworthy and relevant tweet rankings for users of microblogs like Twitter, but it appears incremental as it builds on existing graph-based methods.
The authors tackled the problem of ranking tweets by proposing a method that considers trustworthiness and content-based popularity, modeling the microblog ecosystem as a three-layer graph to propagate scores, and preliminary evaluations showed improvements in precision and trustworthiness over baseline methods with acceptable computation times.
The increasing popularity of Twitter and other microblogs makes improved trustworthiness and relevance assessment of microblogs evermore important. We propose a method of ranking of tweets considering trustworthiness and content based popularity. The analysis of trustworthiness and popularity exploits the implicit relationships between the tweets. We model microblog ecosystem as a three-layer graph consisting of : (i) users (ii) tweets and (iii) web pages. We propose to derive trust and popularity scores of entities in these three layers, and propagate the scores to tweets considering the inter-layer relations. Our preliminary evaluations show improvement in precision and trustworthiness over the baseline methods and acceptable computation timings.