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An Approach to Relevancy Detection: Contributions to the Automatic Detection of Relevance in Social Networks

  • Alvaro Figueira
  • Miguel Sandim
  • Paula Fortuna
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 444)

Abstract

In 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.

Keywords

Automatic Relevance detection Social networks 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CRACS/INESC TEC and University of PortoPortoPortugal

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