Fake News Reading on Social Media: An Eye-tracking Study

The online spreading of fake news (and misinformation in general) has been recently identified as a major issue threatening entire societies. Much of this spreading was enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterizing the fake news and devising automated methods for detecting them. The detection methods had so far only limited success, mostly due to the complexity of the news content and context and lack of properly annotated datasets. One possible way to boost the efficiency of automated misinformation detection methods, is to imitate the detection work of humans. In a broader sense of dealing with fake news spreading, it is also important to understand the news consumption behavior of online users. In this paper, we present an eye-tracking study, in which we let 44 participants to casually read through a social media feed containing posts with news articles. Some of the presented articles were fake. In a second run, we asked the participants to decide on the truthfulness of these articles. We present the description of the study, characteristics of the resulting dataset (which we hereby publish) and several findings.