Inferring social ties from geographic coincidences.

We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals' physical locations over time.

Psychological Distress and Emotional Expression on Facebook.

Social network sites (SNS) are a novel social environment for college students with psychological distress to connect with their peers, but the nature and effects of these interactions are not well understood. This study reports findings from a Facebook study among 238 college students reporting nonspecific psychological distress using the K-6 scale. Behavioral data included Facebook status updates containing affect words written by participants within the past 60 days and the number of responses (comments and likes) each update received. The updates were also coded for depression symptoms. Self-report data included participants' self-presentational concerns, the affective valence of each post, effects of responses on mood, and satisfaction with the responses to and outcome of each status update. Higher psychological distress was associated with displaying depression language on Facebook, with higher self-presentational concerns, and with less satisfaction with audiences' responses and less overall satisfaction with the outcome of the interaction. These results offer a unique glimpse into the social world of college students with psychological distress through their everyday use of Facebook, and how the interplay of this novel environment and students' mental health impacts their social behaviors and interaction meaning-making on Facebook.

Automatic Archiving versus Default Deletion: What Snapchat Tells Us About Ephemerality in Design.

Unlike most social media, where automatic archiving of data is the default, Snapchat defaults to ephemerality: deleting content shortly after it is viewed by a receiver. Interviews with 25 Snapchat users show that ephemerality plays a key role in shaping their practices. Along with friend-adding features that facilitate a network of mostly close relations, default deletion affords everyday, mundane talk and reduces self-consciousness while encouraging playful interaction. Further, although receivers can save content through screenshots, senders are notified; this selective saving with notification supports complex information norms that preserve the feel of ephemeral communication while supporting the capture of meaningful content. This dance of giving and taking, sharing and showing, and agency for both senders and receivers provides the basis for a rich design space of mechanisms, levels, and domains for ephemerality.

Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: implications for the design of technologies to manage mental health.

OBJECTIVE: To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS: Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS: Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION: Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION: This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.

Mobile Manifestations of Alertness: Connecting Biological Rhythms with Patterns of Smartphone App Use.

Our body clock causes considerable variations in our behavioral, mental, and physical processes, including alertness, throughout the day. While much research has studied technology usage patterns, the potential impact of underlying biological processes on these patterns is under-explored. Using data from 20 participants over 40 days, this paper presents the first study to connect patterns of mobile application usage with these contributing biological factors. Among other results, we find that usage patterns vary for individuals with different body clock types, that usage correlates with rhythms of alertness, that app use features such as duration and switching can distinguish periods of low and high alertness, and that app use reflects sleep interruptions as well as sleep duration. We conclude by discussing how our findings inform the design of biologically-friendly technology that can better support personal rhythms of performance.