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.

本项目的目标是通过使用社交媒体上的公开可用数据来创建和研究识别早期警报信号的新技术，这些信号预示着社会失序事件，如暴动、战争或革命。该研究的一般想法是建立一个二元分类器，预测给定的推文是否与破坏性事件有关。结果表明，基于拓扑的算法稳定性较高，比基于深度学习的异常检测算法表现更好。我们还对方法的普适性进行了测试，结果非常有前途。

推特数据中社会动荡的预警信号