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dc.contributor.authorÁlvaro Figueiraen
dc.contributor.authorMiguel Oliveira Sandimen
dc.contributor.authorPaula Teixeira Fortunaen
dc.description.abstractIn this paper we analyze the information propagated through three social networks. 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. In this paper we focus on the search for automatic methods for assessing the relevance of a given set of posts. We first retrieved from social networks, posts related to trending topics. Then, we categorize them as being news or as being conversational messages, and assessed their credibility. From the gained insights we used features to automatically assess whether a post is news or chat, and to level its credibility. Based on these two experiments we built an automatic classifier. The results from assessing our classifier, which categorizes posts as being relevant or not, lead to a high balanced accuracy, with the potential to be further enhanced.en
dc.titleAn Approach to Relevancy Detection: contributions to the automatic detection of relevance in social networksen
Appears in Collections:CRACS - Articles in International Conferences
CSIG - Articles in International Conferences

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