Predicting online extremism, content adopters, and interaction reciprocity

TL;DR

This paper introduces a machine learning framework that uses metadata, network, and temporal features to detect extremist users, predict content adoption, and interaction reciprocity on social media, achieving high accuracy in various scenarios.

Contribution

The study presents a novel predictive framework leveraging diverse features and a unique dataset to forecast extremist activity and user interactions in social media.

Findings

Up to 93% AUC for extremist user detection

Up to 80% AUC for content adoption prediction

Up to 72% AUC for interaction reciprocity forecasting

Abstract

We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.