Scholars who study nonverbal behavior have focused an incredible amount of work on proxemics, how close people stand to one another, and mutual gaze, whether or not they are looking at one another. Moreover, many studies have demonstrated a correlation between gaze and distance, and so-called equilibrium theory posits that people modulate gaze and distance to maintain proper levels of nonverbal intimacy. Virtual reality scholars have also focused on these two constructs, both for theoretical reasons, as distance and gaze are often used as proxies for psychological constructs such as social presence, and for methodological reasons, as head orientation and body position are automatically produced by most VR tracking systems. However, to date, the studies of distance and gaze in VR have largely been conducted in laboratory settings, observing behavior of a small number of participants for short periods of time. In this experimental field study, we analyze the proxemics and gaze of 232 participants over two experimental studies who each contributed up to about 240 minutes of tracking data during eight weekly 30-minute social virtual reality sessions. Participants' non-verbal behaviors changed in conjunction with context manipulations and over time. Interpersonal distance increased with the size of the virtual room; and both mutual gaze and interpersonal distance increased over time. Overall, participants oriented their heads toward the center of walls rather than to corners of rectangularly-aligned environments. Finally, statistical models demonstrated that individual differences matter, with pairs and groups maintaining more consistent differences over time than would be predicted by chance. Implications for theory and practice are discussed.

Scatterplots overlayed with a nonlinear model enable visual estimation of model-data fit. Although statistical fit is calculated using vertical distances, viewers' subjective fit is often based on shortest distances. Our results suggest that adding vertical lines ("lollipops") supports more accurate fit estimation in the steep area of model curves (https://osf.io/fybx5/).

Node-link diagrams enable visual assessment of homophily when viewers can identify and evaluate the relative number of intra-cluster and inter-cluster links. Our online experiment shows that a new design with link type encoded edge color leads to more accurate perception of homophily than a design with same-color edges.

Digital experiences capture an increasingly large part of life, making them a preferred, if not required, method to describe and theorize about human behavior. Digital media also shape behavior by enabling people to switch between different content easily, and create unique threads of experiences that pass quickly through numerous information categories. Current methods of recording digital experiences provide only partial reconstructions of digital lives that weave - often within seconds - among multiple applications, locations, functions and media. We describe an end-to-end system for capturing and analyzing the "screenome" of life in media, i.e., the record of individual experiences represented as a sequence of screens that people view and interact with over time. The system includes software that collects screenshots, extracts text and images, and allows searching of a screenshot database. We discuss how the system can be used to elaborate current theories about psychological processing of technology, and suggest new theoretical questions that are enabled by multiple time scale analyses. Capabilities of the system are highlighted with eight research examples that analyze screens from adults who have generated data within the system. We end with a discussion of future uses, limitations, theory and privacy.

Scatterplots with a model enable visual estimation of model-data fit. In Experiment 1 (N = 62) we quantified the influence of noise-level on subjective misfit and found a negatively accelerated relationship. Experiment 2 showed that decentering of noise only mildly reduced fit ratings. The results have consequences for model-evaluation.

Life experiences are increasingly intertwined with digital devices, suggesting screens as a preferred, if not required, data source for behavioral studies and health interventions. Text Information Extraction from digital screenshots is then a key prerequisite to the overall accuracy of analyses regarding media behaviors. This unique image data set offers the opportunity i) to test existing Image Processing and Text Recognition methods, and ii) to identify and discuss the computational challenges specific to the considered case. Our aim is to assess whether and how state-of-the-art methodologies can be applied to this novel data set. We show how combining OpenCV-based pre-processing with a Long short-term memory (LSTM) based release of Tesseract OCR, without ad hoc training, ensured a 74% text accuracy at the character level. The implications and incidence of different error factors on the resulting quality of text are discussed, prompting the discussion of future research trajectories.

Epidemic monitoring systems engaged in accurate discovery of infected individuals enable better understanding of the dynamics of epidemics and thus may promote effective disease mitigation or prevention. Currently, infection discovery systems require either physical participation of potential patients or provision of information from hospitals and health-care services. While social media has emerged as an increasingly important knowledge source that reflects multiple real world events, there is only a small literature examining how social media information can be incorporated into computational epidemic models. In this paper, we demonstrate how social media information can be incorporated into and improve upon traditional techniques used to model the dynamics of infectious diseases. Using flu infection histories and social network data collected from 264 students in a college community, we identify social network signals that can aid identification of infected individuals. Extending the traditional SIRS model, we introduce and illustrate the efficacy of an Online-Interaction-Aware Susceptible-Infected-Recovered-Susceptible (OIA-SIRS) model based on four social network signals for modeling infection dynamics. Empirical evaluations of our case study, flu infection within a college community, reveal that the OIA-SIRS model is more accurate than the traditional model, and also closely tracks the real-world infection rates as reported by CDC ILINet and Google Flu Trend.

Social media is emerging as a powerful source of communication, information dissemination and mining. Being colloquial and ubiquitous in nature makes it easier for users to express their opinions and preferences in a seamless, dynamic manner. Epidemic surveillance systems that utilize social media to detect the emergence of diseases have been proposed in the literature. These systems mostly employ traditional document classification techniques that represent a document with a bag of N-grams. However, such techniques are not optimal for social media where sparsity and noise are norms. The authors address the limitations posed by the traditional N-gram based methods and propose to use features that represent different semantic aspects of the data in combination with ensemble machine learning techniques to identify health-related messages in a heterogenous pool of social media data. Furthermore, the results reveal significant improvement in identifying health related social media content which can be critical in the emergence of a novel, unknown disease epidemic.