  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.
