  The online spreading of fake news is a major issue threatening entire
societies. Much of this spreading is 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. 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 lay participants to casually read through a social media feed containing
posts with news articles, some of which were fake. In a second run, we asked
the participants to decide on the truthfulness of these articles. We also
describe a follow-up qualitative study with a similar scenario but this time
with 7 expert fake news annotators. We present the description of both studies,
characteristics of the resulting dataset (which we hereby publish) and several
findings.
