  Social media for news consumption is a double-edged sword. On the one hand,
its low cost, easy access, and rapid dissemination of information lead people
to seek out and consume news from social media. On the other hand, it enables
the wide spread of "fake news", i.e., low quality news with intentionally false
information. The extensive spread of fake news has the potential for extremely
negative impacts on individuals and society. Therefore, fake news detection on
social media has recently become an emerging research that is attracting
tremendous attention. Fake news detection on social media presents unique
characteristics and challenges that make existing detection algorithms from
traditional news media ineffective or not applicable. First, fake news is
intentionally written to mislead readers to believe false information, which
makes it difficult and nontrivial to detect based on news content; therefore,
we need to include auxiliary information, such as user social engagements on
social media, to help make a determination. Second, exploiting this auxiliary
information is challenging in and of itself as users' social engagements with
fake news produce data that is big, incomplete, unstructured, and noisy.
Because the issue of fake news detection on social media is both challenging
and relevant, we conducted this survey to further facilitate research on the
problem. In this survey, we present a comprehensive review of detecting fake
news on social media, including fake news characterizations on psychology and
social theories, existing algorithms from a data mining perspective, evaluation
metrics and representative datasets. We also discuss related research areas,
open problems, and future research directions for fake news detection on social
media.
