Extracting Personal Behavioral Patterns from Geo-Referenced Tweets

This paper presents an exploratory study of the potential of geo-referenced Twitter data for extracting knowledge about significant personal places, behaviors and potential interests of people. The study was done analysing two months’ worth of tweets from residents of the greater Seattle area

Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Change in BMI Accurately Predicted by Social Exposure to Acquaintances

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R[superscript 2]. This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.MIT Masdar ProgramMIT Media Lab Consortiu

Go Together: Bridging the Gap between Learners and Teachers

After the pandemic, humanity has been facing different types of challenges. Social relationships, societal values, and academic and professional behavior have been hit the most. People are shifting their routines to social media and gadgets, and getting addicted to their isolation. This sudden change in their lives has caused an unusual social breakdown and endangered their mental health. In mid-2021, Pakistan's first Human Library was established under HelpingMind to overcome these effects. Despite online sessions and webinars, HelpingMind needs technology to reach the masses. In this work, we customized the UI or UX of a Go Together Mobile Application (GTMA) to meet the requirements of the client organization. A very interesting concept of the book (expert listener or psychologist) and the reader is introduced in GTMA. It offers separate dashboards, separate reviews or rating systems, booking, and venue information to engage the human reader with his or her favorite human book. The loyalty program enables the members to avail discounts through a mobile application and its membership is global where both the human-reader and human-books can register under the platform. The minimum viable product has been approved by our client organization

Identifying Close Friendships in a Sensed Social Network

Studies have suggested that propinquity; social, cultural, physical and psychological similarities are major factors in close friendship ties. These studies were subject to human recall of interactions with no details of length or time of interactions. Recently, advancements in mobile technology have enabled the measurement of complex systems of interactions. This study uses social network analysis of data comprising of time-resolved sensed interactions to predict and explain close friendship ties via interactions at different periods, residence (floor) similarity and gender similarity. Results indicate residence (floor) proximity and duration of weekend night interactions have the potential of explaining close friendship ties.MIT Masdar Progra