# Information Is Not a Virus, and Other Consequences of Human Cognitive Limits
## 1 Introduction  
The spread of information in online social networks is often likened to the spread of a contagious disease. According to this analogy, information--whether a trending topic, a news story, a song, or a video--behaves much like a virus, \"infecting\" individuals, who then \"expose\" their naive followers by mentioning the topic, sharing the video, or recommending the news story, _etc._ These followers may, in turn, become \"infected\" by sharing the information, \"exposing\" their own followers, and so on. If each person \"infects\" at least one other person, information will keep spreading through the network, resulting in a \"viral\" outbreak, similar to how a spreading virus can create an epidemic that sickens a large portion of the population. The analogy between the spread of a disease and information is the basis of computational methods that attempt to amplify the spread of information in networks by identifying influential \"superspreaders\" [1; 2; 3; 4; 5], and those that make inferences about the network from observations of how information spreads in it [6; 7; 8; 9; 10]. One of the simplest and most widely used models of the spread of epidemics in networks is the Independent Cascade Model (ICM) [1; 6; 11; 12; 13]. It describes a process wherein each exposure of a healthy but susceptible individual to a disease by an infected friend results in an independent chance of disease transmission: the more infected friends an individual has, the more likely he or she is to become infected. The model predicts the size of an outbreak (number of individuals infected) in any network for a given value of disease transmissibility (_i.e._, how easily the disease is transmitted upon exposure). Figure 1 shows the size of outbreaks simulated using the ICM on a social media follower graph (red dots) [14]. The black symbols give the size of simulated outbreaks on a randomly-generated graph with the same degree distribution as the follower graph. The simulated outbreaks are close in size to the theoretically predicted values [15], given by the golden line in Figure 1. There exists a criticalvalue of transmissibility--the epidemic threshold [16]--below which the contagion dies out, but above which it spreads to a finite portion of the network. The epidemic threshold depends only on structural properties of the network, and not the details of the disease or its transmissibility [17]: specifically, the epidemic threshold is given by the inverse of the largest eigenvalue of the adjacency matrix representing the network [16; 18]. Note that even above the epidemic threshold, contagions starting in isolated corners of the network may die out. However, in general, the higher the transmissibility, the farther the contagion spreads, reaching a non-negligible fraction of the network above the epidemic threshold, for example, 10%, 20%, _etc._ of the network. How well does the _social contagion_ analogy hold for social media? In this review of empirical studies of information diffusion in social media, I first present evidence that information fails to spread widely in online social networks. The vast majority of outbreaks are very small (see Figure 2), in stark contrast to the predictions of the epidemic model. To explain these findings, I present studies examining the mechanisms of information diffusion, specifically how people respond to multiple exposures to information. The key finding of these studies--that central individuals in an online social network are less susceptible to becoming \"infected\"--is sufficient to explain why social contagions fail to propagate. The studies also link the reduced susceptibility of central individuals to information overload and human reliance on cognitive heuristics to compensate for the brain's limited capacity to process information. Accounting for how people use cognitive heuristics to decide what information to pay attention to in social media dramatically simplifies dynamics of social contagion and allows for more accurate predictions of how far information will spread online.