Disinformation: analysis and identification.

We present an extensive study on disinformation, which is defined as information that is false and misleading and intentionally shared to cause harm. Through this work, we aim to answer the following questions:Can we automatically and accurately classify a news article as containing disinformation?What characteristics of disinformation differentiate it from other types of benign information? We conduct this study in the context of two significant events: the US elections of 2016 and the 2020 COVID pandemic. We build a series of classifiers to (i) examine linguistic clues exhibited by different types of fake news articles, (ii) analyze "clickbaityness" of disinformation headlines, and (iii) finally, perform fine-grained, veracity-based article classification through a natural language inference (NLI) module for automated disinformation verification; this utilizes a manually curated set of evidence sources.