Quantifying Information Overload in Social Media and its Impact on Social Contagions (1403.6838v1)

Abstract: Information overload has become an ubiquitous problem in modern society. Social media users and microbloggers receive an endless flow of information, often at a rate far higher than their cognitive abilities to process the information. In this paper, we conduct a large scale quantitative study of information overload and evaluate its impact on information dissemination in the Twitter social media site. We model social media users as information processing systems that queue incoming information according to some policies, process information from the queue at some unknown rates and decide to forward some of the incoming information to other users. We show how timestamped data about tweets received and forwarded by users can be used to uncover key properties of their queueing policies and estimate their information processing rates and limits. Such an understanding of users' information processing behaviors allows us to infer whether and to what extent users suffer from information overload. Our analysis provides empirical evidence of information processing limits for social media users and the prevalence of information overloading. The most active and popular social media users are often the ones that are overloaded. Moreover, we find that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information. Finally, the susceptibility of a social media user to social contagions depends crucially on the rate at which she receives information. An exposure to a piece of information, be it an idea, a convention or a product, is much less effective for users that receive information at higher rates, meaning they need more exposures to adopt a particular contagion.

Quantifying Information Overload in Social Media and Its Impact on Social Contagions

The paper presented in "Quantifying Information Overload in Social Media and Its Impact on Social Contagions" explores the critical problem of information overload in social media, specifically focusing on the Twitter platform. The authors, Manuel Gomez Rodriguez, Krishna Gummadi, and Bernhard Schoelkopf, utilize empirical data to uncover how Twitter users process and manage the vast amount of information they receive, establishing a relationship between information overload and social contagions.

Twitter serves as the primary context for this quantitative analysis. Users are modeled as information processing systems, examining their behavior in terms of queueing policies and processing limits. The researchers reveal important properties of user information processing behaviors by analyzing timestamped tweet data. The core findings illustrate the existence of information processing limits among users and identify the prevalence of overload, especially among those who receive large volumes of information.

The paper highlights several key results:

1. Information Production Limits: Users demonstrate a strong limit on information generation, with few producing more than approximately 40 tweets per day. This production constraint contrasts sharply with the information receipt, which scales linearly with the number of users followed.
2. Processing Capacity Threshold: A threshold exists where users receive approximately 30 tweets per hour. Below this rate, the probability of retweeting remains stable, while above it, the likelihood decreases significantly, indicating overload.
3. Queueing Behavior: Information processing behaviors are significantly influenced by incoming tweet rates. When overloaded, users exhibit slower processing times and selective prioritization of tweets from a limited set of sources.

These findings have profound implications for the dissemination of information in social media networks. As active users become overloaded, they require numerous exposures to adopt an idea or behavior, challenging traditional models of social contagion. The background traffic plays a vital role in these dynamics, as evidenced by the experimentations on social conventions like retweets, hashtags, and technological adoptions such as URL shortening services.

The paper also extends traditional models of information propagation to incorporate background traffic, presenting novel insights into cascade sizes and durations. Larger cascade sizes become infrequent, and cascades with prolonged lifetimes emerge with increased background traffic. This aligns with observed phenomena in social media where few information cascades achieve viral success.

The theoretical and practical implications are vast. It necessitates considering background traffic in models of information diffusion and suggests modifications to social media platforms to mitigate overload effects. Future research directions might include prioritization strategies in feeds and dynamic modeling of user behaviors.

Ultimately, this paper offers a comprehensive analysis of information overload in social media and its critical effects on users and information propagation, providing a foundation for further exploration into optimizing information dissemination methodologies in digital networks.