VISTX: Experimenting with Interactive Stance and Sentiment Visualisation Usergenerated content online continues to grow rapidly, and with this it becomes more challenging to evaluate the general consensus on a trending topic. Even greater challenges are faced if one wishes to compare opinions across multiple topics, as it can be difficult to make comparisons and reach conclusions with large amounts of data in an intuitive manner. VISTX explores innovative methods of visualising opinion in terms of stance and sentiment using Twitter data from the SemEval2016 Stance Dataset and presents three types of interactive visualisations. We discuss the method of testing the visualisations for effectiveness with a small group of users, where time taken and the number of incorrect interpretations were measured for each user. We discuss the results, which indicate that the xe2x80x98circlepackingxe2x80x99 visualisation type may lead to quicker interpretations for five or more topics. Finally, we discuss the work needed to further solidify these initial findings.

Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure 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 SemEval2017 (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 conversationbased and affectivebased features, covering different facets of affect, has been explored. Our classification model outperforms the bestperforming systems for stance classification at SemEval2017 Task 8, showing the effectiveness of the feature set proposed.