Addressing fake news requires a multidisciplinary effort

We live life in the network. When we wake up in the morning, we check our e-mail, make a quick phone call, walk outside (our movements captured by a high definition video camera), get on the bus (swiping our RFID mass transit cards) or drive (using a transponder to zip through the tolls). We arrive at the airport, making sure to purchase a sandwich with a credit card before boarding the plane, and check our BlackBerries shortly before takeoff. Or we visit the doctor or the car mechanic, generating digital records of what our medical or automotive problems are. We post blog entries confiding to the world our thoughts and feelings, or maintain personal NIH Public Access

Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data.

Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, selfreports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.engineering-social systems ͉ relational inference ͉ social network analysis ͉ reality mining ͉ relational scripts T he field devoted to the study of the system of human interactions-social network analysis-has been constrained in accuracy, breadth, and depth because of its reliance on self-report data. Social network studies relying on self-report relational data typically involve both limited numbers of people and a limited number of time points (usually one). As a result, social network analysis has generally been limited to examining small, well-bounded populations, involving a small number of snapshots of interaction patterns (1). Although important work has been done over the last 30 years to analyze the relationship between self-reported and observed behavior, much of the social network literature is written as if self-report data are behavioral data.There is, however, a small but emerging thread of research examining social communication patterns based on directly observable data such as e-mail (2, 3) and call logs (4,5). Here, we demonstrate the power of collecting not only communication information but also location and proximity data from mobile phones over an extended period, and compare the resulting behavioral social network to self-reported relationships from the same group. We show that pairs of individuals that report themselves as friends demonstrate distinctive behavioral signatures as measured only by the mobile phone data. Further, these purely objective measures of behavior show powerful relationships with key outcomes of interest at the individual levelnotably, satisfaction.The Reality Mining study followed 94 subjects using mobile phones preinstalled with several pieces of software that recorded and sent the researcher data about call logs, Bluetooth devices in proximity of approximately five meters, cell tower IDs, application usage, and phone status (6, 7). Subjects were observed using these measurements over the course of nine months and included students and faculty from two programs within a major research institution. We also collected self-report relational data from each individual, where subjects were asked about their proximity to, and friendship with, others. Subjects were also asked about their satisf...