Information Overload in Group Communication: From Conversation to Cacophony in the Twitch Chat

TL;DR

This paper analyzes how information overload in Twitch chat transitions conversations into noisy, less meaningful exchanges, revealing structural and textual changes as overload increases.

Contribution

It provides a large-scale quantitative analysis of chat dynamics under overload, identifying a transition from meaningful conversation to cacophony in online group communication.

Findings

Overload leads to decreased user participation.

Messages become more copy-pasted during overload.

Information content per message diminishes with overload.

Abstract

Online communication channels, especially social web platforms, are rapidly replacing traditional ones. Online platforms allow users to overcome physical barriers, enabling worldwide participation. However, the power of online communication bears an important negative consequence --- we are exposed to too much information to process. Too many participants, for example, can turn online public spaces into noisy, overcrowded fora where no meaningful conversation can be held. Here we analyze 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 of overload with lower user participation, more copy-pasted messages, and less information per message. These results hold both on average and at the individual level for the majority of users. 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 communication systems.