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.

A Taxonomy for Classifying User Group Activity in Online Political Discourse Twitter has become a popular platform for political conversation. Within the TwitterSphere, multiple users contribute to the discourse through different modalities and with varying degrees of engagement. In this paper, we seek to profile the different groups of users present in online political discourse. We provide a taxonomy that can be used to classify the various users, determine each groupu0027s representation and their respective share of the online conversation. Along with core information foraging concepts, the taxonomy aims to reduce the foraging costs associated with exploratory searching. This work provides a practical approach for analyzing and preprocessing social media data and offers valuable recommendation for future expansion into sentiment analysis.