Early Warning Signals of Social Instabilities in Twitter Data

• URL: http://arxiv.org/abs/2303.05401v1
• Date: Fri, 3 Mar 2023 11:18:02 GMT
• Title: Early Warning Signals of Social Instabilities in Twitter Data
• Authors: Vahid Shamsaddini, Henry Kirveslahti, Raphael Reinauer, Wallyson Lemes de Oliveira, Matteo Caorsi, Etienne Voutaz
• Abstract summary: We study novel techniques to identify early warning signals for socially disruptive events using only publicly available data on social media. We build a binary classifier that predicts if a given tweet is related to a disruptive event or not. The results indicate that the persistent-gradient approach is stable and even more performant than deep-learning-based anomaly detection algorithms.
• Score: 0.42816770420595307
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The goal of this project is to create and study novel techniques to identify early warning signals for socially disruptive events, like riots, wars, or revolutions using only publicly available data on social media. Such techniques need to be robust enough to work on real-time data: to achieve this goal we propose a topological approach together with more standard BERT models. Indeed, topology-based algorithms, being provably stable against deformations and noise, seem to work well in low-data regimes. The general idea is to build a binary classifier that predicts if a given tweet is related to a disruptive event or not. The results indicate that the persistent-gradient approach is stable and even more performant than deep-learning-based anomaly detection algorithms. We also benchmark the generalisability of the methodology against out-of-samples tasks, with very promising results.

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