Interactively Learning Social Media Representations Improves News Source Factuality Detection

摘要

One concept underlying methods that aim to exploit social information for identifying the facThe rise of social media has enabled the tuality of news sources is the social homophily widespread propagation of fake news, text that principle (McPherson et al., 2001), which capis published with an intent to spread misinformation and sway beliefs. Rapidly detecting tures the tendency of members of the same social fake news, especially as new events arise, is group to hold similar views and content preferimportant to prevent misinformation. ences. This often leads to the formation of “echo While prior works have tackled this problem uschambers” (Jamieson and Cappella, 2008; Quating supervised learning systems, automatedly trociocchi et al., 2016), tightly-knit “information modeling the complexities of the social media communities” that have little interaction with other landscape that enables the spread of fake news communities holding different views. Prior work is challenging. On the contrary, having humans shows how similar news, particularly misinformafact check all news is not scalable. Thus, in this tion, tends to spread more in some of these tightlypaper, we propose to approach this problem inknit information communities (Bessi et al., 2016). teractively, where humans can interact to help an automated system learn a better social media Thus, identifying them can provide the needed inrepresentation quality. On real world events, formation for capturing the factuality of sources our experiments show performance improve(communities spreading mostly low factuality conments in detecting factuality of news sources, tent in the past are likely to spread low factuality even after few human interactions. content in the future). In this work, we first capture