TikTok StitchGraph: Characterizing communication patterns on TikTok through a collection of interaction networks

TL;DR

This paper introduces TikTok StitchGraph, a collection of interaction networks based on stitches, revealing communication patterns and structural preferences in TikTok conversations, and compares these with Twitter reply networks.

Contribution

It constructs and analyzes TikTok stitch-based graphs, characterizing communication patterns and comparing them with Twitter reply networks, providing new insights into TikTok's conversation structures.

Findings

Preference for star and star-like structures in communication

Aversion to cyclic conversation patterns

Similarity between TikTok and Twitter conversation networks

Abstract

We present TikTok StitchGraph: a collection of 36 graphs based on TikTok stitches. With its rapid growth and widespread popularity, TikTok presents a compelling platform for study, yet given its video-first nature the network structure of the conversations that it hosts remains largely unexplored. Leveraging its recently released APIs, in combination with web scraping, we construct graphs detailing stitch relations from both a video- and user-centric perspective. Specifically, we focus on user multi-digraphs, with vertices representing users and edges representing directed stitch relations. From the user graphs, we characterize common communication patterns of the stitch using frequent subgraph mining, finding a preference for stars and star-like structures, an aversion towards cyclic structures, and directional disposition favoring in- and out-stars over mixed-direction structures. These structures are augmented with sentiment labels in the form of edge attributes. We then use these subgraphs for graph-level embeddings together with Graph2Vec, we show no clear distinction between topologies for different hashtag topic categories. Lastly, we compare our StitchGraphs to Twitter reply networks and show that a remakable similarity between the conversation networks on the two platforms.