Prediction and Analysis of Rumours Impact on Social Media Rumour, as an important form of social communication, has been run through the whole evolutionary history of mankind. People maliciously disseminate rumours in order to increase awareness, slander others or cause panic, etc. To eliminate this issue, many researchers resort to detecting rumours on social media. However, rumour detection is not sufficient to eliminate the negative impact, which also requires official institutions to provide the refutations. In practice, the number of rumours on social media is too large, there is no need to refute some rumours with little or no concern. Therefore, we need to evaluate the impact of the rumours in advance. In this paper, we devise a rumour influence prediction model RISM (Rumour Impact on Social Media) based on a popular rumour intensity formula to predict the impact of a newborn rumour. Extensive numerical experiments are carried out on the real rumour data that are collected from Toutiao.com, which demonstrate the effectiveness of our proposed RISM model.

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.