Detecting Coordination on Short-Video Platforms: The Challenge of Multimodality and Complex Similarity on TikTok

Research on online coordinated behaviour has predominantly focused on text-based social media platforms, where coordination manifests clearly through the frequent posting of identical hyperlinks or the frequent re-sharing of the same textual content by the same group of users. However, the rise of short-video platforms like TikTok introduces distinct challenges, by supporting integrated multimodality within posts and complex similarity between them. In this paper, we propose 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, including video, audio, and text, and explicitly handles complex forms of similarity inherent in video and audio content. We test this approach on political videos posted on TikTok and extracted via the TikTok researcher API. This application demonstrates the capacity of the approach to identify coordination, while also critically highlighting potential pitfalls and limitations.