Investigating Content-based Fake News Detection using Knowledge Graphs

Abstract: In recent years, fake news has become a pervasive reality of global news consumption. While research on fake news detection is ongoing, smaller languages such as Swedish are often left exposed by an under-representation in research. The biggest challenge lies in detecting news that is continuously shape-shifting to look just like the real thing — powered by increasingly complex generative algorithms such as GPT-2. Fact-checking may have a much larger role to play in the future. To that end, this project considers knowledge graph embedding models that are trained on news articles from the 2016 U.S. Presidential Elections. In this project, we show that incomplete knowledge graphs created from only a small set of news articles can detect fake news with an F-score of 0.74 for previously seen entities and relations. We also show that the model trained on English language data provides some useful insights for labelling Swedish-language news articles of the same event domain and same time horizon.