Analysis of Peer Group Behavior Among University Students
20 Apr 2018-
TL;DR: The preliminary results from this investigation reveal that students social interactions are not limited to one but several groups, and the satisfaction levels associated with each type of group are indicative of the average time spent engaging with said group(s).
Abstract: Satisfactory peer group interactions within a university, through the formation of close associations, define a student's personality and help in deterring the rise of depression caused by academic, financial or emotional troubles. In this work, we conduct a pre-study survey of 177 students in a University setting to assess the requirement for a smartphone-based study to detect and monitor group formation, evolution and engagement. The preliminary results from this investigation reveal that students social interactions are not limited to one but several groups, and the satisfaction levels associated with each type of group are indicative of the average time spent engaging with said group(s). Intra-group bond strength took precedence as a satisfaction determinant over the location or activity engaged in. Further, we present design recommendations for a minimally invasive smartphone-based study.
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TL;DR: The approach is the first to detect any unusual behaviour in crowd with non-visual data, which is simple to train and easy to deploy and presented for public research as there is no such dataset available to perform experiments on crowds for detecting unusual behaviours.
Abstract: This paper presents, a system capable of detecting unusual activities in crowds from real-world data captured from multiple sensors. The detection is achieved by classifying the distinct movements of people in crowds, and those patterns can be different and can be classified as normal and abnormal activities. Statistical features are extracted from the dataset collected by applying sliding time window operations. A model for classifying movements is trained by using Random Forest technique. The system was tested by using two datasets collected from mobile phones during social events gathering. Results show that mobile data can be used to detect anomalies in crowds as an alternative to video sensors with significant performances. Our approach is the first to detect any unusual behaviour in crowd with non-visual data, which is simple to train and easy to deploy. We also present our dataset for public research as there is no such dataset available to perform experiments on crowds for detecting unusual behaviours.
3 citations
Cites background from "Analysis of Peer Group Behavior Amo..."
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TL;DR: I, Sahiti Kunchay, is a student of the College of Information Sciences and Technology at The Pennsylvania State University, enrolled in the IST's PhD program's Fall 2018 cohort, with the expected date of graduation being May 2023.
Abstract: I, Sahiti Kunchay, am a student of the College of Information Sciences and Technology (IST) at The Pennsylvania State University, enrolled in the IST's PhD program's Fall 2018 cohort, with the expected date of graduation being May 2023. My undergraduate background in Computer Science and Sociology offered me the unique perspective of identifying and investigating pressing issues through the combined lens of research methodologies in computing as well as social sciences. As a part of my research career, I have been involved in multiple projects, which yielded publications accepted at workshops co-located with conferences such as ACM CHI, ACM SenSys and ACM MobiSys. Along with being awarded the Student Travel Grant by SIGMOBILE, I have also been awarded the ACM-W Scholarship to present my work at the aforementioned venues.
References
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TL;DR: A Dartmouth term lifecycle is identified in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns, while stress appreciably rises while positive affect, sleep, conversation and activity drops off.
Abstract: Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.
698 citations
"Analysis of Peer Group Behavior Amo..." refers background or methods in this paper
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TL;DR: This paper develops a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation, and utilizes semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts.
Abstract: History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.
379 citations
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TL;DR: Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health and creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies.
Abstract: Objective
Optimal mental health care is dependent upon sensitive and early detection of mental health problems. The current study introduces a state-of-the-art method for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was examine whether the information captured with multi-modal smartphone sensors can serve as behavioral markers for one’s mental health. We hypothesized that: a) unobtrusively collected smartphone sensor data would be associated with individuals’ daily levels of stress, and b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time.
234 citations
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TL;DR: The feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health is shown and different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community are derived.
Abstract: The idea of continuously monitoring well-being using mobile-sensing systems is gaining popularity. In-situ measurement of human behavior has the potential to overcome the short comings of gold-standard surveys that have been used for decades by the medical community. However, current sensing systems have mainly focused on tracking physical health; some have approximated aspects of mental health based on proximity measurements but have not been compared against medically accepted screening instruments. In this paper, we show the feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health. By continuously capturing fine-grained motion and privacy-sensitive audio data, we are able to derive different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community. In addition, we present a case study that highlights how errors in assessment due to the subjective nature of the responses could potentially be avoided by continuous mobile sensing.
180 citations
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