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1
Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.
In: Biological psychiatry. Cognitive neuroscience and neuroimaging, vol 6, iss 9 (2021)
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2
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech ...
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3
Understanding Side Effects of Antidepressants: Large-scale Longitudinal Study on Social Media Data
In: JMIR Ment Health (2021)
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4
Designing a Clinician-Facing Tool for Using Insights From Patients’ Social Media Activity: Iterative Co-Design Approach
In: JMIR Ment Health (2020)
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5
Identifying emerging mental illness utilizing search engine activity: A feasibility study
In: PLoS One (2020)
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6
The Language of LGBTQ+ Minority Stress Experiences on Social Media
In: Proc ACM Hum Comput Interact (2019)
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7
A Social Media Based Examination of the Effects of Counseling Recommendations after Student Deaths on College Campuses
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 12 No. 1 (2018): Twelfth International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2018)
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8
A Social Media Based Examination of the Effects of Counseling Recommendations After Student Deaths on College Campuses
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9
#anorexia, #anarexia, #anarexyia: Characterizing Online Community Practices with Orthographic Variation ...
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10
The Language of Social Support in Social Media and Its Effect on Suicidal Ideation Risk
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2017)
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11
The Language of Social Support in Social Media and its Effect on Suicidal Ideation Risk
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12
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.
Keyword: Article
URL: https://doi.org/10.1145/2998181.2998219
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5565732/
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13
Characterizing Smoking and Drinking Abstinence from Social Media
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14
Identity Management and Mental Health Discourse in Social Media
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15
Predicting Depression via Social Media
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 7 No. 1 (2013): Seventh International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2013)
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16
Not All Moods Are Created Equal! Exploring Human Emotional States in Social Media
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 6 No. 1 (2012): Sixth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2012)
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17
Happy, Nervous or Surprised? Classification of Human Affective States in Social Media
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 6 No. 1 (2012): Sixth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2012)
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