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 satisfaction with their work group Full details on data collection and variable construction are available in the SI Text. We will hereafter refer to data collected purely from mobile phones as ''behavioral'' data as opposed to ''self-report'' data. We conducted three analyses of these data. First, we examined the relationship between self-report and behavioral data. Second, we analyzed whether there were behaviors identified in the mobile phone data that were characteristic of friendship. Third, we studied the relationship between behavioral data and individual satisfaction. Behavioral Versus Self-Report Data. The reliability of existing measures for relationships has been the subject of sharp debate over the last 30 years, starting with a series of landmark studies in which it was found that behavioral observations were surprisingly weakly related to reported interactions (8-10). There are multiple layers of cognitive filters that influence whether a subject reports a behavior (11). Existing research suggests that people are good at recalling long-term, but not short-term, social structures (12). We examine whether there are systematic biases in recall that have been observed in other areas with respect to human memory (13), specifically, whether there are recency and salience biases in recall of physical proximity. A recency bias is one where memories are biased toward recent events. A salience bias is one where memories are biased toward more vivid events. Here, we capture recency by the quantity of interactions in a fixed period preceding the survey, and salience by whether the individual in question is a friend or nonfriend. We test for recency and salience biases by comparing selfreported proximity to observed proximity, examining whether self-reports were biased toward recent and salient proximity. Specifically, subjects were asked about their typical proximity to the other individuals in the study. These self-reports were compared with average daily proximity based on the Bluetooth scans. Although most (69%) observed proximity Ͼ0 was reported as nonproximity, when proximity was reported, it was typically overestimated: The average reported proximity was 87 min per day whereas the average observed proximity was only 33 min per day. We also found that for proximity Ͼ0, friends were much more accurate at reporting proximity than nonfriends. In this case, there was a statistically significant but small correlation between observed and reported proximity among individuals who worked together but did not consider each other friends (r ϭ .155, P Ͻ 0.001), whereas there was a substantially stronger relationship for friends (r ϭ .412, P Ͻ 0.001). Additionally, we found that when subjects were asked about long-term proximity patterns, recent proximity had a large and significant (P Ͻ 0.001)
In REF , a study of mobile phone data proves that similar behavior pattern in cell phone data can provide inference of user relationship.
Inferring friendship network structure by using mobile phone data
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Medicine Computer Science