  Analysing how people react to rumours associated with news in social media is
an important task to prevent the spreading of misinformation, which is nowadays
widely recognized as a dangerous tendency. In social media conversations, users
show different stances and attitudes towards rumourous stories. Some users take
a definite stance, supporting or denying the rumour at issue, while others just
comment it, or ask for additional evidence related to the veracity of the
rumour. On this line, a new shared task has been proposed at SemEval-2017 (Task
8, SubTask A), which is focused on rumour stance classification in English
tweets. The goal is predicting user stance towards emerging rumours in Twitter,
in terms of supporting, denying, querying, or commenting the original rumour,
looking at the conversation threads originated by the rumour. This paper
describes a new approach to this task, where the use of conversation-based and
affective-based features, covering different facets of affect, has been
explored. Our classification model outperforms the best-performing systems for
stance classification at SemEval-2017 Task 8, showing the effectiveness of the
feature set proposed.
