Datasets related to the CSCW 2019 paper 'Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media'

@inproceedings{A2019,
  title={Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media},
  author={Aldayel, Abeer and Magdy, Walid},
  booktitle={Proc. ACM Hum.-Comput. Interact., Vol. 3, No. CSCW, Article 205. Publication date: November 2019},
  year={2019},
  organization={CSCW}
}

If you have any questions about the paper or want to get access to code/dataset that's not already public, please contact Abeer ALDayel ([email protected])

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%.

This folder contains the Semeval2016 stance dataset with the timeline information related to the user id. The full Semeval stance dataset can be accessed from https://saifmohammad.com/WebPages/StanceDataset.htm