Multi-Modal Embeddings for Isolating Cross-Platform Coordinated Information Campaigns on Social Media

TL;DR

This paper introduces a multi-modal method combining text, timing, and network data to detect coordinated social media campaigns across platforms, exemplified by the White Helmets case study.

Contribution

It presents a novel multi-modal approach for identifying cross-platform coordinated campaigns, addressing the lack of ground truth datasets.

Findings

Identifies posts linking to similar factuality news channels

Detects unusual coordination patterns across platforms

Effective in analyzing campaigns like the White Helmets

Abstract

Coordinated multi-platform information operations are implemented in a variety of contexts on social media, including state-run disinformation campaigns, marketing strategies, and social activism. Characterized by the promotion of messages via multi-platform coordination, in which multiple user accounts, within a short time, post content advancing a shared informational agenda on multiple platforms, they contribute to an already confusing and manipulated information ecosystem. To make things worse, reliable datasets that contain ground truth information about such operations are virtually nonexistent. This paper presents a multi-modal approach that identifies the social media messages potentially engaged in a coordinated information campaign across multiple platforms. Our approach incorporates textual content, temporal information and the underlying network of user and messages posted to identify groups of messages with unusual coordination patterns across multiple social media platforms. We apply our approach to content posted on four platforms related to the Syrian Civil Defence organization known as the White Helmets: Twitter, Facebook, Reddit, and YouTube. Results show that our approach identifies social media posts that link to news YouTube channels with similar factuality score, which is often an indication of coordinated operations.