D $i$ E$v$D: Disruptive Event Detection from Dynamic Datastreams using Continual Machine Learning: A Case Study with Twitter

Identifying disruptive events (riots, protests, natural calamities) from social media is important for maintaining social order and addressing geopolitical concerns. Existing works on identifying disruptive events use classical machine learning (ML) models on static datasets. However, social networks are dynamic entities and cannot be practically modeled using static techniques. A viable alternative is the emerging Continual Machine Learning (CML) approach which applies the knowledge acquired from the past to learn future tasks. However, existing CML techniques are trained and tested on static data and are incapable of handling real-time data obtained from dynamic environments. This paper presents a novel D $i$ E $v$ D framework for disruptive event detection using Continual Machine Learning (CML) specifically for dynamic data streams. We have used Twitter social media as a case study of the real-time and dynamic data provider. To the best of our knowledge, this is the first attempt to use CML for socially disruptive event detection. Comprehensive performance analysis show that our framework effectively identifies disruptive events with 98% accuracy and can classify them with an average incremental accuracy of 76.8%. Moreover, computational analysis is performed to establish the effectiveness of D $i$ E $v$ D by applying various language models and statistical tests.