Detecting Coordinated Behaviour on Video-First Platforms: The Challenge of Multimodality and Complex Similarity on TikTok

TL;DR

This paper presents a multilayer network analysis approach to detect coordinated behaviour on TikTok, addressing multimodality and complex similarity challenges inherent in video content, with application to German political posts for the 2024 European Elections.

Contribution

It introduces a novel multilayer network methodology tailored for multimodal video content to identify coordination on TikTok, a platform with unique challenges compared to text-based social media.

Findings

Successfully identified coordination in TikTok political posts

Highlighted limitations and potential pitfalls of the approach

Demonstrated applicability to real-world political content

Abstract

Research on online coordinated behaviour has predominantly focused on text-based social media platforms. However, the rise of video-first platforms such as TikTok introduces distinct challenges. The multimodal nature of video posts, combining visuals, audio, and text, allows for coordination across various modalities and complicates comparison between posts. This paper proposes an approach to detecting coordination that addresses these characteristic challenges. Our methodology, based on multilayer network analysis, is tailored to capture coordination across multiple modalities, and explicitly handles complex forms of similarity inherent in video and audio content. We test this approach on German political posts regarding the 2024 European Elections retrieved via the TikTok Research API. Our results demonstrate the ability of our approach to identify coordination within the constraints of the API, while also critically highlighting potential pitfalls and limitations.