  Proactively identifying misinformation spreaders is an important step towards
mitigating the impact of fake news on our society. In this paper, we introduce
a new contemporary Reddit dataset for fake news spreader analysis, called
FACTOID, monitoring political discussions on Reddit since the beginning of
2020. The dataset contains over 4K users with 3.4M Reddit posts, and includes,
beyond the users' binary labels, also their fine-grained credibility level
(very low to very high) and their political bias strength (extreme right to
extreme left). As far as we are aware, this is the first fake news spreader
dataset that simultaneously captures both the long-term context of users'
historical posts and the interactions between them. To create the first
benchmark on our data, we provide methods for identifying misinformation
spreaders by utilizing the social connections between the users along with
their psycho-linguistic features. We show that the users' social interactions
can, on their own, indicate misinformation spreading, while the
psycho-linguistic features are mostly informative in non-neural classification
settings. In a qualitative analysis, we observe that detecting affective mental
processes correlates negatively with right-biased users, and that the openness
to experience factor is lower for those who spread fake news.
