Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the informa-tion. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/ her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social net-works have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.

Social media systems have increasingly become digi-tal information marketplaces, where users produce, con-sume and share information and ideas, often of public interest. In this context, social media users are their own curators of information – however, they can only select their information sources, who they follow, but cannot choose the information they are exposed to, which con-tent they receive. A natural question is thus to assess how efficient are users at selecting their information sources. In this work, we model social media users as information processing systems whose goal is acquiring a set of (unique) pieces of information. We then define a computational framework, based on minimal set co-vers, that allows us to evaluate every user’s performance as information curators within the system. Our frame-work is general and applicable to any social media sys-tem where every user follows others within the system to receive the information they produce. We leverage our framework to investigate the efficiency of Twitter users at acquiring information. We find that user’s efficiency typically decreases with respect to the number of people she follows. A more efficient user tends to be less overloaded and, as a consequence, any particular piece of information lives longer in the top of her timeline, thus facilitating her to actually read the information. Finally, while most unique information a user receives could have been acquired through a few users, less popular information requires following many different users.

Email is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In this paper, we report results of a large-scale study of more than 2 million users ex-changing 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email man-agement applications for tasks like ranking unread emails.