Abstract
The study of controversy in social media is not new, there are many previous studies aimed at identifying and characterizing controversial issues, mostly around political debates, but also for other topics. In this work, we aim to study the interaction between two ego networks around two influencers having opposite opinions on a given subject and its impact on opinion change and propagation within these two interconnected ego networks. We propose a method for detecting opinion modification in relation to several nodal and topological measures as the users centralities, the opinion of the community to witch belongs the users as well as textual information extracted from tweets. We firstly constructed a propagation network which is the union of 2-level opposite ego networks extracted from a set of collected tweets in relation to a given topic, where nodes are users and edges are tweets or replies. We then apply machine learning models to detect respectively: opinion change over time concerning users who are the authors of replies and opinion modification during the information propagation via an action of reply. The dataset contains nodal and topological information extracted from the propagation network.
We would like to warmly thank Charles-Philippe Frantz, Mohamed Sellami & Yvan Singuina students at CY Tech, speciality Data Science for helping us in the implementation.
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Folly, K., Boughaba, Y., Malek, M. (2023). Leveraging Nodal and Topological Information for Studying the Interaction Between Two Opposite Ego Networks. In: Coman, A., Vasilache, S. (eds) Social Computing and Social Media. HCII 2023. Lecture Notes in Computer Science, vol 14026. Springer, Cham. https://doi.org/10.1007/978-3-031-35927-9_21
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