Revealing User Influence in an Online Newspaper - Pochet, Gilberto Flores; Kamienski, Carlos (Anais do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM), 2016)
Web-based online newspapers have become a very popular way to share information as well as to allow users to comment news and each other's comments. In such environments some users play a prominent role and eventually influence other users thus leading discussions as they please. So far, there is no way of identifying and quantifying such influence. This paper proposes and analyzes a methodology for identifying influential users that builds implicit social networks upon users who post comments in the same news, suggests three different ways of identifying influential users and measures influence based on similarity of comments. We applied it to data collected from a Brazilian online newspaper and results confirm its effectiveness by revealing a significant similarity between comments of identified influential users and the remaining ones.