  Fake news detection is a critical yet challenging problem in Natural Language
Processing (NLP). The rapid rise of social networking platforms has not only
yielded a vast increase in information accessibility but has also accelerated
the spread of fake news. Thus, the effect of fake news has been growing,
sometimes extending to the offline world and threatening public safety. Given
the massive amount of Web content, automatic fake news detection is a practical
NLP problem useful to all online content providers, in order to reduce the
human time and effort to detect and prevent the spread of fake news. In this
paper, we describe the challenges involved in fake news detection and also
describe related tasks. We systematically review and compare the task
formulations, datasets and NLP solutions that have been developed for this
task, and also discuss the potentials and limitations of them. Based on our
insights, we outline promising research directions, including more
fine-grained, detailed, fair, and practical detection models. We also highlight
the difference between fake news detection and other related tasks, and the
importance of NLP solutions for fake news detection.
