Project Team:
Hiloko Kato (UZH), Elena Gavagnin (ZHAW) and Johanna Pirker (TU Graz) – lead
Benjamin Kühnis (ZHAW), Jasmine Heierli (ZHAW)

Digital games have become integral to everyday life, with League of Legends (Riot, since 2009) standing out as a prime example of Esports' cultural and economic reach. Its transmedia expansions, such as the Netflix series Arcane (2023/2025) and collaborations with artist like Linkin Park or Lil Nas X, illustrate how digital games foster "affinity spaces" (Gee 2017) and "communities of practice" (Lave & Wenger 1991).

This project examines interaction in League of Legends through a combined multimodal and multifactorial approach. Players coordinate via text chat, visual-auditory pings, and UI elements, exploiting the game's affordances (Gibson 1979) to achieve teamwork. Our qualitative analysis draws on multimodal theory (Kress & van Leeuwen 2010; Wildfeuer & Stamenkovic 2020) and sequential analysis from Conversation Analysis (Sacks 1995), focusing on the unfolding of "interactional jobs" (Hausendorf 2013).
Quantitatively, we use machine learning to extract patterns from multimodal gameplay, examining not only chat and ping behaviour but also latency, avatar positioning, and escalation patterns of toxic communication. Additionally, we evaulate player data collections for gaining insights into player profiles and gamer type progression. This multifactorial approach also enables a deeper understanding of how coordination, miscommunication, and toxicity manifest in live competitive gaming.

Two sub-projects were/are funded by the ZHAW Digital Futures Fund:Multimodal Detection of Toxicity in Video Games (MuMoDeTox)  
Multimodal Anonymization of Gameplay Data

Additional project funding is supported by the Digital Society Initiative  with a seed grant.