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 videostreaming 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. Social media enable people to access a virtually unlimited quantity of information from a virtually unlimited set of sources. This often results in our continuous exposure to a barrage of text messages, videos and sound. The information-processing capacity of humans is, however, limited. The size of the working memory, for example, corresponds to approximately seven discrete items [1] . Likewise, physical constraints in the visual and cognitive system limit our processing speed of written texts [2, 3] . The effect of cognitive limits on information processing is certainly not a recent discovery but, with the recent rise of digital communication technologies, its broader implications on interpersonal communication are less well understood. Information overload can be defined as the state in which one cannot make sense or act upon additional stimuli. Scholars from different fields-neuroscience, cognitive science, information science, management science and computer science-have looked at the issue of information overload from different perspectives. Despite its considerable breadth, the literature on information overload usually focuses on the individual level, and often takes the perspective of the consumption of information. Our work brings two important elements of novelty to this literature: a group-level and production-focused analysis. Although a large literature on the effect of information overload at the individual level exists [4-9], group-level analyses are rare. It is known for example that the number of interpersonal ties an individual can manage, both offline [10] and online [11, 12] , is limited, suggesting the existence of active mechanisms to cope with overload at the individual level. At the group level, however, our understanding of information overload is more sparse. Recent studies on social contagion and e-mail communication have mainly explored information overload from the perspective of information consumption and, to some extent, information production, for example looking at the rate of replies to e-mails and tweets [13] [14] [15] . However, there are still many open questions related to information production. For example, both the structure and the textual contents of a conversation may be affected by a higher information load, potentially degrading the overall production of new information. In this work, we estimate the effects of overload on information production in the groups conversations that take place on a large social media platform. Social media are an excellent setting to understand how information overload affects collective consumption and production of information. Social media offer an environment for virtual public discussion [16] , with the key difference-with respect to offline world-that a potentially limitless number of people can join a single online conversation. This new scale of mass interaction may result in levels of information overload in individuals never explored before [4, 14, 17] . Overload in computer-mediated communication has been previously studied in legacy media such as newsgroups [18] and Internet Relay Chat (IRC) systems [19] . As the number of messages increases, chat participants cannot consume any more information, and their chances of writing new messages decreases-for example, because replying to earlier messages becomes harder. Thus, even though the total number of messages increases, the number of messages per user should actually decrease. Assuming that users tend to avoid this kind of situation by leaving the conversation, it was predicted that the overall group size would reach an equilibrium [18, 19] . Empirical observations are consistent with these predictions. Indeed, it has been found that IRC rooms are typically functional with crowds of up to 300 participants, with a maximum of about 40 of them actively talking [19] . Similar observations hold for Usenet [18] , and recent ethnographic studies are in support of these conclusions too [20] . Some evidence also suggests that information overload can reshape the structure and dynamics of the discourse [21] . For example, overloaded individuals may only reply to certain topics, or reply in a shorter and simpler manner [18, 22, 23] . Even though previous studies show evidence that information overload has macroscopic effects on group communication, it is not clear how the structure and content of these conversations changes, especially over the full spectrum of information loads achievable by modern communication platforms. Unfortunately, the systems studied in the literature on overload do not generally attain such high data loads; samples from IRC or Usenet do not generally reach the scale required to draw strong statistical claims. Overcoming these limitations, here we analyse a large dataset of chat logs from Twitch (http://www.twitch.tv/), a popular video sharing and streaming platform (see Methods for more information about data). On Twitch, people can broadcast a stream-usually a video feed of their screen-to other users, and share videos of past broadcasts (figure 1a). These users are informally called streamers. Both the audience and the streamer can write messages into an interactive and real-time chat room displayed prominently on the side of the stream. As a result, users are exposed to a live flow of messages, as in a traditional IRC channel. Motivated by the prior research discussed above, our research question is twofold. First, the flow of messages exerts on chat participants an information load that needs to be processed to take an active part in the conversation (figure 1b). At low information loads, an increase in the load will correspond to an royalsocietypublishing.org/journal/rsos R. Soc. open sci. 6: 191412
If the communication is unstructured and loosely organized, the amount of information each member of a group has to process can lead to the information overload REF , which trumps the quality of communication and subsequently, the collaboration can be affected.
Information overload in group communication: from conversation to cacophony in the Twitch chat
Royal Society Open Science
Royal Society Open Science
Psychology