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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 theinformation. In this paper, we conduct a large scale quantitative study ofinformation overload and evaluate its impact on information dissemination inthe Twitter social media site. We model social media users as informationprocessing systems that queue incoming information according to some policies,process information from the queue at some unknown rates and decide to forwardsome of the incoming information to other users. We show how timestamped dataabout tweets received and forwarded by users can be used to uncover keyproperties of their queueing policies and estimate their information processingrates and limits. Such an understanding of users' information processingbehaviors allows us to infer whether and to what extent users suffer frominformation overload.  Our analysis provides empirical evidence of information processing limits forsocial media users and the prevalence of information overloading. The mostactive and popular social media users are often the ones that are overloaded.Moreover, we find that the rate at which users receive information impactstheir processing behavior, including how they prioritize information fromdifferent sources, how much information they process, and how quickly theyprocess information. Finally, the susceptibility of a social media user tosocial contagions depends crucially on the rate at which she receivesinformation. An exposure to a piece of information, be it an idea, a conventionor a product, is much less effective for users that receive information athigher rates, meaning they need more exposures to adopt a particular contagion.

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