Detecting fake news over online social media via domain reputations and content understanding

Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebook from two perspectives: domain reputations and content understanding. Our domain reputation analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors, registration timing, domain rankings, and domain popularity. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency-inverse document frequency (tf-idf) and Latent Dirichlet Allocation (LDA) topic modeling is inefficient in detecting fake news, while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study domain reputations and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.