  Rumour stance classification, defined as classifying the stance of specific
social media posts into one of supporting, denying, querying or commenting on
an earlier post, is becoming of increasing interest to researchers. While most
previous work has focused on using individual tweets as classifier inputs, here
we report on the performance of sequential classifiers that exploit the
discourse features inherent in social media interactions or 'conversational
threads'. Testing the effectiveness of four sequential classifiers -- Hawkes
Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured
Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM)
-- on eight datasets associated with breaking news stories, and looking at
different types of local and contextual features, our work sheds new light on
the development of accurate stance classifiers. We show that sequential
classifiers that exploit the use of discourse properties in social media
conversations while using only local features, outperform non-sequential
classifiers. Furthermore, we show that LSTM using a reduced set of features can
outperform the other sequential classifiers; this performance is consistent
across datasets and across types of stances. To conclude, our work also
analyses the different features under study, identifying those that best help
characterise and distinguish between stances, such as supporting tweets being
more likely to be accompanied by evidence than denying tweets. We also set
forth a number of directions for future research.
