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Mrinal Kumar 0004

dblp:40/9185-4 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Health and well-being technologies · 64% Wearable and physiological sensing · 19% Collaborative and social computing · 17%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies
behavior change
0.312017
Computational Approaches Toward Integrating Quantified Self Sensing and Social Media · CSCW 2017
Medical and health informatics
mental health
0.212016
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media · CHI 2016
Wearable and physiological sensing
quantified self
0.112017
Computational Approaches Toward Integrating Quantified Self Sensing and Social Media · CSCW 2017
Collaborative and social computing
social media
0.112016
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media · CHI 2016

Methods — techniques the papers use, named apart from their topics

statistical modeling · 0.5propensity score matching · 0.5language measures · 0.5granger causality · 0.3feature engineering · 0.3
YearPublicationVenuePosition
2017 Computational Approaches Toward Integrating Quantified Self Sensing and Social Media
abstract
The growing amount of data collected by quantified self tools and social media hold great potential for applications in personalized medicine. Whereas the first includes health-related physiological signals, the latter provides insights into a user's behavior. However, the two sources of data have largely been studied in isolation. We analyze public data from users who have chosen to connect their MyFitnessPal and Twitter accounts. We show that a user's diet compliance success, measured via their self-logged food diaries, can be predicted using features derived from social media: linguistic, activity, and social capital. We find that users with more positive affect and a larger social network are more successful in succeeding in their dietary goals. Using a Granger causality methodology, we also show that social media can help predict daily changes in diet compliance success or failure with an accuracy of 77%, that improves over baseline techniques by 17%. We discuss the implications of our work in the design of improved health interventions for behavior change.
Munmun De Choudhury, Mrinal Kumar 0004, Ingmar Weber
CSCW2
2016 Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
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.
Munmun De Choudhury, Emre Kiciman, Mark Dredze, Glen A. Coppersmith, Mrinal Kumar 0004
CHI5