Information overload in group communication: from conversation to cacophony in the Twitch chat

Abstract

As social media replace traditional communication channels, we are often exposed to too much information to process. The presence of too many participants, for example, can turn online public spaces into noisy, overcrowded fora where no meaningful conversation can be held. Here, we analyse a large dataset of public chat logs from Twitch, a popular video-streaming platform, in order to examine how information overload affects online group communication. We measure structural and textual features of conversations such as user output, interaction and information content per message across a wide range of information loads. Our analysis reveals the existence of a transition from a conversational state to a cacophony—a state with lower per capita participation, more repetition and less information per message. This study provides a quantitative basis for further studies of the social effects of information overload, and may guide the design of more resilient online conversation systems.