Simple Open Stance Classification for Rumour Analysis
This addresses the problem of detecting fake news and extracting attitudes in social media, but it is incremental as it builds on existing stance classification methods.
The paper tackles open stance classification in Twitter for rumour analysis by introducing a simple approach with novel problem-specific features, achieving above state-of-the-art results on benchmark datasets.
Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.