Unraveling the Web of Disinformation: Exploring the Larger Context of State-Sponsored Influence Campaigns on Twitter

TL;DR

This paper develops a generalized machine learning system to detect state-sponsored disinformation accounts on Twitter, capable of identifying accounts from unseen campaigns by leveraging common strategies used across different state actors.

Contribution

The work introduces a campaign-agnostic detection system that generalizes across multiple disinformation campaigns, unlike previous campaign-specific approaches.

Findings

Achieves up to 94% accuracy in identifying accounts from unseen campaigns.

Identifies additional potential state-backed accounts in real-world deployment.

Provides case studies showing similarity to Twitter-identified troll accounts.

Abstract

Social media platforms offer unprecedented opportunities for connectivity and exchange of ideas; however, they also serve as fertile grounds for the dissemination of disinformation. Over the years, there has been a rise in state-sponsored campaigns aiming to spread disinformation and sway public opinion on sensitive topics through designated accounts, known as troll accounts. Past works on detecting accounts belonging to state-backed operations focus on a single campaign. While campaign-specific detection techniques are easier to build, there is no work done on developing systems that are campaign-agnostic and offer generalized detection of troll accounts unaffected by the biases of the specific campaign they belong to. In this paper, we identify several strategies adopted across different state actors and present a system that leverages them to detect accounts from previously unseen campaigns. We study 19 state-sponsored disinformation campaigns that took place on Twitter, originating from various countries. The strategies include sending automated messages through popular scheduling services, retweeting and sharing selective content and using fake versions of verified applications for pushing content. By translating these traits into a feature set, we build a machine learning-based classifier that can correctly identify up to 94% of accounts from unseen campaigns. Additionally, we run our system in the wild and find more accounts that could potentially belong to state-backed operations. We also present case studies to highlight the similarity between the accounts found by our system and those identified by Twitter.