Uncovering Like-minded Political Communities on Twitter

Ophélie Fraisier, Guillaume Cabanac, Yoann Pitarch, Romaric Besançon, Mohand Boughanem
2017 Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval - ICTIR '17  
Stance detection systems often integrate social clues in their algorithms. While the in uence of social groups on stance is known, there is no evaluation of how well state-of-the-art community detection algorithms perform in terms of detecting like-minded communities, i.e. communities that share the same stance on a given subject. We used Twitter's social interactions to compare the results of community detection algorithms on datasets on the Scottish Independence Referendum and US Midterm
more » ... ions. Our results show that algorithms relying on information di usion perform better for this task and con rm previous observations about retweets being better vectors of stance than mentions.
doi:10.1145/3121050.3121091 dblp:conf/ictir/FraisierCPBB17 fatcat:67xlhu3rsvdj5brdigjckfdqxe