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.
We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally. On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources. We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70 to 80 .