Early Warning Signals of Social Instabilities in Twitter Data

Autor:	Shamsaddini, Vahid, Kirveslahti, Henry, Reinauer, Raphael, de Oliveira, Wallyson Lemes, Caorsi, Matteo, Voutaz, Etienne
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning Computer Science - Social and Information Networks 68
Druh dokumentu:	Working Paper
Popis:	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. Comment: 22 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.05401 Zobrazit plný text záznamu View this record from Arxiv