A Multi-Aspect Classification Ensemble Approach for Profiling Fake News Spreaders on Twitter

In this work, we attempt to differentiate authors of fake news and real news as part of the Profiling Fake News Spreaders on Twitter task at PAN. We propose a set of eight different language features to represent tweets. These representations are subsequently used in an ensemble classification model to identify fake news spreaders on Twitter. The approach is confined to the English language.

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