The information below is an evolving list of data sets (primarily from electronic/social media) that have been used to model mental-health phenomena. The raw data (with additional columns) can be found in data_sources.xlsx
. If you are an author of any of these papers and feel that anything is misrepresented, please do not hesitate to reach out to me at [email protected].
Last Update: 2020-11-09T22:17:33.464844
Paper | Authors | Platform | Year | Target Outcomes |
---|---|---|---|---|
Inferring Social Media Users' Mental Health Status from Multimodal Information | Xu, Pérez-Rosas, Mihalcea | Flickr | 2020 | Mental Health (General) |
Dilated LSTM with attention for Classification of Suicide Notes | Schoene, Lacy, Turner, Dethlefs | Death Row Last Statements, The Kernel, Tumblr | 2019 | Suicide, Imminent Death, Depression, Loneliness |
Detection of Depression-related Posts in Reddit Social Media Forum | Tadesse, Lin, Xu, Yang | Reddit, Online Support Forums | 2019 | Depression |
Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles from a pilot program at Crisis Text Line | Pisani, Kanuri, Filbin, Gallo, Gould, Lehmann, Levine, Marcotte, Pascal, Rousseau, Turner, Yen, Ranney | Crisis Text Line | 2019 | None |
Mental Health Surveillance over Social Media with Digital Cohorts | Amir, Dredze, Ayers | 2019 | Depression, PTSD, Control | |
CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts | Zirikly, Resnik, Uzuner, Hollingshead | 2019 | Suicidal Ideation | |
Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses | Brown, Bendig, Fischer, Goldwich, Baumeister, Plener | 2019 | Non-suicidal Self-Injury | |
Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals | Ernala, Birnbaum, Candan, Rizvi, Sterling, Kane, De Choudury | Twitter, Facebook | 2019 | Schizophrenia |
Suicide Risk Assessment with Multi-level Dual-Context Language and BERT | Matero, Idnani, Son, Giorgi, Vu, Zamani, Limbachiya, Guntuku, Schwartz | 2019 | Regular expression, subreddit participation, and manual annotation (4-levels of risk) | |
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention | Cao, Zhang, Feng, Wei, Wang, Li, He | Sina Weibo | 2019 | Suicidal Ideation |
Automatic detection of eating disorder-related social media posts that could benefit from a mental health intervention | Yan, Fitzsimmons-Craft, Goodman, Krauss, Das, Cavazos-Rehg | 2019 | Eating Disorder | |
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media | Turcan, McKeown | 2019 | Stress | |
Detecting Low Self-Esteem in Youths from Web Search Data | Zaman, Acharyya, Kautz, Silenzio | Google Search | 2019 | Self-esteem |
BioInfo@UAVR at eRisk 2019: delving into social media texts for the early detection of mental and food disorders | Trifan, Luís Oliveira | 2019 | Anorexia, Depression | |
Towards Augmenting Crisis Counselor Training by Improving Message Retrieval | DeMasi, Hearst, Recht | Synthetic Crisis Text Conversations | 2019 | None (Message Retrieval Task) |
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text | Kirinde Gamaarachichige, Inkpen | 2019 | Depression, PTSD, Control | |
Adapting Deep Learning Methods for Mental Health Prediction on Social Media | Sekulic, Strube | 2019 | Depression | |
User Dynamics in Mental Health Forums -- A Sentiment Analysis Perspective | Davcheva, Adam, Benlian | 3 Online mental-health forums | 2019 | Sentiment |
Quick and (maybe not so) Easy Detection of Anorexia in Social Media Posts | Mohammadi, Amini, Kosseim | 2019 | Anorexia | |
Using Topic Modeling to Detect and Describe Self-Injurious and Related Content on a Large- Scale Digital Platform | Franz, Nook, Mair, Nock | TeenHelp.org (Forum) | 2019 | Self-harm |
A Framework for Early Detection of Antisocial Behavior on Twitter Using Natural Language Processing | Singh, Du, Zhang, Wang, Miao, Sianaki, Ulhaq | 2019 | Antisocial Behavior | |
The Role of Features and Context on Suicide Ideation Detection | Wang, Wan, Paris | 2019 | Suicidal Ideation | |
Identifying Depressive Users in Twitter Using Multimodal Analysis | Kang, Yoon, Yi Kim | 2019 | Depression | |
Facebook language predicts depression in medical records | Eichstaedt, Smith, Merchant, Ungar, Crutchley, Pretoiuc-Pietro, Asch, Schwartz | 2018 | Depression | |
A multilevel predictive model for detecting social network users with depression | Wongkoblap, Vadillo, Curcin | 2018 | Life Satisfaction, Depression | |
Deep Learning for Depression Detection of Twitter Users | Husseini Orabi, Buddhitha, Husseini Orabi, Inkpen | 2018 | Depression | |
Suicidal Trend Analysis of Twitter using Machine Learning and Neural Network | Shahreen, Subhani, Mahfuzur Rahman | 2018 | Suicidal Ideation | |
Attention-based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision | Winata, Pepijin Kampman, Fung | Twitter, Interview | 2018 | Stress |
Exploring the utility of community-generated social media content for detecting depression: an analytical study on Instagram | Ricard, Marsch, Crosier, Hassanpour | 2018 | Depression | |
Helping or hurting? predicting changes in users’ risk of self-harm through online community interactions | Soldaini, Walsh, Cohan, Han, Goharian | ReachOut (Online Forum) | 2018 | Change in Distress (Self-harm/Suicidal Ideation) |
Identifying depression on reddit: The effect of training data | Pirina, I. & Çöltekin, Ç | Reddit, Online Support Forums | 2018 | Depression, Breast Cancer Support, Familiar Support, Relationship Support |
Detecting suicidal ideation on forums: proof-of-concept study | Aladağ, Murderrisoglu, Akbas, Zahmacioglu, Bingol | 2018 | Suicidal Ideation | |
Norms matter: contrasting social support around behavior change in online weight loss communities | Chancellor, Hu, De Choudhury | 2018 | Weight loss support vs. Eating-disorder encouragement | |
Measuring the impact of anxiety on online social interactions | Dutta, Ma, De Choudhury | 2018 | Change in social interaction based on inferred anxiety | |
Within and between-person differences in language used across anxiety support and neutral reddit communities | Ireland, Iserman | 2018 | Anxiety | |
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health | Ive, Gkotis, Dutta, Stewart, Velupillai | 2018 | Borderline Personality Disorder, Bipolar Disorder, Schizophrenia, Anxiety, Depression, Self-harm, Suicidality, Addiction, Alcoholism, Opiates, Autism, and Control | |
Measuring the latency of depression detection in social media. | Sadeque, Xu, Bethard | 2018 | Depression | |
Cross-domain depression detection via harvesting social media | Shen, Jia, Shen Feng, He, Luan, Tang, Tiropanis, Chua, Hall | Twitter, Weibo | 2018 | Depression |
Predicting depression from language-based emotion dynamics: longitudinal analysis of Facebook and twitter status updates | Seabrook, Kern, Fulcher, Rickard | Facebook, Twitter | 2018 | Depression |
Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing | Upton Patton, MacBeth, Schoenebeck, Shear, McKeown | 2018 | Grief, Aggression | |
Automatic detection of cyberbullying in social media text | Van Hee, Jacobs, Emmery, Desmet, Lefever, Verhoeven, De Pauw, Daelemans, Hoste | AskFM | 2018 | Cyberbullying |
Benchmarking Aggression Identification in Social Media | Kumar, Ojha, Malmasi, Zampieri | Facebook, Twitter | 2018 | Aggression |
Natural Language Processing of Social Media as Screening for Suicide Risk | Coppersmith | 2018 | Suicide Attempt | |
Not Just Depressed: Bipolar Disorder Prediction on Reddit | Sekulic ́, Gjurković, Šnajder | 2018 | Bipolar Disorder | |
Predictive linguistic features of schizophrenia | Sarioglu Kayi, Diab, Pauselli, Compton, Coppersmith | Twitter, Essays | 2018 | Schizophrenia |
"Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention | Gaur, Kursuncu, Alambo, Sheth, Daniulaityle, Thirunaryan, Pathak | 2018 | Anxiety, Borderline Personality, Bipolar, Opiate Addiction, Self Hard, Addiction, Asperger's, Autism, Alcoholism, Opiate Usage, Schizophrenia, Self-hard, Suicidal Ideation | |
Feature Attention Network: Interpretable Depression Detection from Social Media | Song, You, Chunk, Park | 2018 | Depression | |
Overview of eRisk: Early Risk Prediction on the Internet | Losada, Crestani, Parapar | 2018 | Depression, Anorexia | |
Text-based Detection and Understanding of Changes in Mental Health | Li, Mihalcea, Wilson | 2018 | Change in Mental Health Disorder Communication | |
Can Text Messages Identify Suicide Risk in Real Time? A within-subjects pilot examination of temporally sensitive markers of suicide risk | Glenn, Nobles, Barners, Teachman | SMS | 2018 | Periods of Suicide Attempts, Suicidal Ideation, Depressive Episodes, Positive Mood |
Detecting Linguistic Traces of Depression Topic-Restricted Text: Attending to Self-Stigmatized Depression with NLP | Wolohan, Hirgaga, Mukerjee, Sayyed | 2018 | Depression | |
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses | MacAvaney, Desmet, Cohan, Soldaini, Yates, Zirikly, Goharian | 2018 | Depression Diagnosis Date | |
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions | Cohan, Desmet, Yates, Soldaini, MacAvaney, Goharian | 2018 | ADHD, Anxiety, Autism, Bipolar Disorder, Depression, Eating Disorder, Obsessive Compulsive Disorder, PTSD, Schizophrenia | |
Cross-cultural differences in language markers of depression online | Loveys, Torrez, Fine, Moriarty, Coppersmith | 7 Cups of Tea (Chat-based peer support platform) | 2018 | None |
Detecting Comments Showing Risk for Suicide in YouTube | Gao, Cheng, Yu | YouTube | 2018 | Suicidal Ideation |
Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings | Shing, Nair, Zirikly, Friedenberg, Daumé III, Resnik | 2018 | Suicidal Ideation | |
Identification of Imminent Suicide Risk Among Young Adults using Text Messages | Nobles, Glenn, Kowsari, Teachman, Barnes | SMS, emails, and call history, social media data (i.e., Twitter and Facebook), web browsing history | 2018 | Suicidal Ideation |
#MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter | Lachmar, Wittenborn, Bogen, McCauley | 2017 | Depression | |
Multi-Task Learning for Mental Health using Social Media Text | Benton, Mitchell, Hovy | 2017 | Neuroatypicality, Suicide Attempt, Anxiety, Depression, Eating Disorder, Panic Attacks, Schizophrenia, Bipolar Disorder, PTSD | |
Modeling Stress with Social Media Around Incidents of Gun Violence on College Campuses | Saha, De Choudhury | 2017 | Stress | |
Detecting anxiety on Reddit | Hanwen Shen, Rudzicz | 2017 | Anxiety | |
Assessing suicide risk and emotional distress in Chinese social media: a text mining and machine learning study | Cheng, Li, Kwok, Zhu, Yip | Sina Weibo | 2017 | Suicidal Ideation, Depression, Anxiety, and Stress |
The Language of Social Support in Social Media and its Effect on Suicidal Ideation Risk | De Choudury, Kiciman | 2017 | Suicidal Ideation | |
Detection of Suicide-Related Posts in Twitter Data Streams | Vioulès, Moulahi, Azé, Bringay | 2017 | Suicidal Ideation | |
Characterization of mental health conditions in social media using Informed Deep Learning | Gkotsis, Oellrich, Velupillai, Liakata, Hubbard, Dobson, Dutta | 2017 | Borderline Personality Disorder, Bipolar Disorder, Schizophrenia, Anxiety, Depression, Self-harm, Suicidality, Addiction, Alcoholism, Opiates, Autism, and Control | |
Detecting cognitive distortions through machine learning text analytics | Simms, Ramstedt, Rich, Richards, Martinez, Giraud-Carrier | Tumblr | 2017 | Cognitive Distortion |
A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals | Birnbaum, Kiranmai Ernala, Rizvi, De Choudhury, Kane | 2017 | Schizophrenia | |
Social Media Based Index of Mental Well-Being in College Campuses | Bagroy, Kumaraguru, De Choudhury | 2017 | 14 mental-health related subreddits + small set of control subreddits (e.g. r/AskReddit) | |
Understanding and Discovering Deliberate Self-harm Content in Social Media | Wang, Tang, Li, Li, Wan, Mellina, O'Hare, Chang | Flickr | 2017 | Self-harm |
Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language | Loveys, Crutchley, Wyatt, Coppersmith | 2017 | Suicide Attempt, Schizophrenia, Panic, Eating, Anxiety | |
Detecting and Characterizing Eating-Disorder Communities on Social Media | Wang, Brede, Ianni, Mentzakis | 2017 | Eating Disorder | |
Emotional and Linguistic Cues of Depression from Social Media | Vedula, Parthasarathy | 2017 | Depression | |
Depression detection via harvesting social media: A multimodal dictionary learning solution | Shen, Jia, Feng, Zhang, Hu, Chua, Zhu | 2017 | Depression | |
Identifying Depression on Twitter | Nadeem, Horn, Coppersmith, Sen | 2017 | Depression, PTSD, Control | |
Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments | Saha, Chan, Barbaro, Abowd, De Choudhury | Twitter, Facebook, Ecological Momentary Assessments | 2017 | Mood Instability, Bipolar Disorder, Borderline Personality Disorder |
Depression and Self-Harm Risk Assessment in Online Forums | Yates, Cohan, Goharian | 2017 | Depression | |
Monitoring Tweets for Depression to Detect At-risk Users | Jamil | 2017 | Depression | |
Gender and Cross-Cultural Differences in Social Media Disclosures of Mental Illness | De Choudury, Sharma, Logar, Eekhout, Cluasen Nielsen | 2017 | Suicidal Ideation | |
Quantifying Mental Health from Social Media with Neural User Embeddings | Amir, Coppersmith, Carvalho, Silva, Wallace | 2017 | Depression, PTSD, Control | |
Learning from various labeling strategies for suicide-related messages on social media: An experimental study | Liu, Chen, Homan, Silenzio | 2017 | Suicidal Risk | |
Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study | Mowery, Smith, Cheney, Stoddard, Coppersmith, Bryan, Conway | 2017 | Depression (Symptoms) | |
Content Analysis of Depression-Related Tweets | Cavazos-Reh, Krauss, Sowles, Connolly, Rosasa, Bharadwaj, Bierut | 2017 | Feelings of Depression, Support for Depression, School or Work-related Pressures related to Depression, Substance use to deal with depression, self-hard or suicidal thoughts | |
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media | Hossein Yazdavar, Al-Olimat, Ebrahimi, Bajaj, Banerjee, Thirunarayan, Pathak, Sheth | 2017 | Depression | |
Instagram photos reveal predictive markers of depression | Reece, Danforth | 2017 | Depression | |
Predicting Multiple Risky Behaviors via Multimedia Content | Zhou, Zhang, Luo | 2017 | Depression, Drug Use, Alcohol Use, Sleep Disorder, Eating Disorder | |
Triaging content severity in online mental health forums | Cohan, Young, Yates, Goharian | ReachOut (Online Forum) | 2017 | Self-harm/Suicidal Ideation |
eRISK 2017: CLEF Lab on Early Risk Prediction on the Internet: Experimental Foundation | Losada, Crestani, Parapar | 2017 | Depression | |
Validating machine learning algorithms for twitter data against established measures of suicidality | Braithwaite, Giraud-Carrier, West, Barnes, Lee Hanson | 2016 | Suicidality | |
Quantifying and Predicting Mental Illness Severity in Online Pro-Eating Disorder Communities | Chancellor, Lin, Goodman, Zerwas, De Choudhury | 2016 | Eating Disorder | |
What does social media say about your stress? | Lin, Jia, Nie, Shen, Chua | Sina Weibo | 2016 | Stress, Stress (Stressor and Stress Subject) |
Natural Language Processing for Mental Health: Large Scale Discourse Analysis of Counseling Conversations | Althoff, Clark, Leskovec | Crisis Text Line | 2016 | Counseling Outcome |
MIDAS: Mental illness detection and analysis via social media | Saravia, Chang, Jollet De Lorenzo, Chen | 2016 | Borderline Personality Disorder, Bipolar Disorder | |
Discovering shifts to suicidal ideation from mental health content in social media | De Choudhury, Kiciman, Dredze, Coppersmith, Kumar | 2016 | Depression, Mental Health (General), Trauma, Bipolar, Borderline Personality, PTSD, Psychosis, Eating Disorders, Self Harm, Rape Survivors, Panic, Social Anxiety, Suicidal Ideation | |
Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health | Mowery, Park, Conway, Bryan | 2016 | Depressive Symptoms and Stressors Associated with Depression | |
Recovery Amid Pro-Anorexia: Analysis of Recovery in Social Media | Chancellor, Mitra, De Choudhury | Tumblr | 2016 | Anorexia (Recovery) |
The language of mental health problems in social media | Gkotis, Oellrich, Hubbard, Dobson, Liakata, Velupillai, Dutta | 2016 | Anxiety, Borderline Personality, Bipolar, Opiate Addiction, Self Hard, Addiction, Asperger's, Autism, Alcoholism, Opiate Usage, Schizophrenia, Self-hard, Suicidal Ideation | |
Exploratory Analysis of Social Media Prior to a Suicide Attempt | Coppersmith, Ngo, Leary, Wood | 2016 | Suicide Attempt | |
CLPsych 2016 Shared Task: Triaging Content in Online Peer Support Forums | Milne, Pink, Hachey, Calvo | ReachOut (Online Forum) | 2016 | Self-harm |
Forecasting the Onset and Course of Mental Illness with Twitter Data | Reece, Reagan, Lix, Dodds, Danforth, Langer | 2016 | Depression, PTSD | |
The role of personality, age, and gender in tweeting about mental illnesses | Preotiuc-Pietro, Eichstaedt, Park, Sap, Smith, Toblosky, Schwartz, Ungar | 2015 | Depression, PTSD | |
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses | Coppersmith, Dredze, Harman, Hollingshead | 2015 | ADHD, Anxiety, Bipolar Disorder, Borderline Personality Disorder, Depression, Eating, OCD, PTSD, Schizophrenia, Seasonal Affective Disorder | |
Recognizing Depression From Twitter Activity | Tsugawa, Kikuchi, Kishino, Nakajimi, Itoh, Ohsaki | 2015 | Depression | |
Detecting Suicidality on Twitter | O'Dea, Wan, Batterham, Calear, Paris, Christensen | 2015 | Suicidal Ideation | |
CLPsych 2015 Shared Task: Depression and PTSD on Twitter | Coppersmith, Dredze, Harman, Hollingshead, Mitchell | 2015 | Depression, PTSD, Control | |
Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter | Resnik, Armstrong, Claudino, Nguyen, Nguyen, Boyd-Graber | Twitter, Essays | 2015 | Depression |
Towards Developing an Annotation Scheme for Depressive Disorder Symptoms: A Preliminary Study using Twitter Data | Mowery, Bryan, Conway | 2015 | Major Depressive Disorder | |
Topic Model for Identifying Suicidal Ideation in Chinese Microblog | Huang, Li, Zhang, Liu, Chiu, Zhu | Sina Weibo | 2015 | Suicidal Ideation |
Mixed-Initiative Real-Time Topic Modeling & Visualization for Crisis Counseling | Dinakar, Chen, Lieverman, Picard, Fill-in | Crisis Text Line | 2015 | None |
Teenagers’ stress detection based on time-sensitive microblog comment/response actions | Zhao, Jia, Feng | Tencent Weibo | 2015 | Stress |
Anorexia on Tumblr: A Characterization Study on Anorexia | De Choudhury | Tumblr | 2015 | Anorexia (Recovery), Anorexia |
Machine Classification and analysis of suicide-related communication on Twitter | Burnap, Colombo, Scourfield | 2015 | Suicidal Ideation | |
Understanding and Fighting Bullying with Machine Learning | Junming Sui | 2015 | Cyberbullying | |
Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides | Kumar, Dredze, Coppersmith, De Choudury | Reddit, Wikipedia | 2015 | Suicidal Ideation |
Mining Twitter data to improve detection of schizophrenia | McManus, Mallory, Goldfelder, Haynes, Tatum | 2015 | Schizophrenia | |
Quantifying the language of schizophrenia in social media | Mitchell, Hollingshead, Coppersmith | 2015 | Schizophrenia | |
Identifying Chinese Microblog Users with High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model | Guan, Hao, Cheng, Yip, Zhu | Sina Weibo | 2015 | Suicidal Ideation |
User-level psychological stress detection from social media using deep neural network | Lin, Jia, Guo, Xue, Li, Huang, Cai, Feng | Sina Weibo, Tencent Weibo, Twitter | 2014 | Stress |
Tracking Suicide Risk Factors Through Twitter in the US | Jashinky, Burton, Hanson, West, Giraud-Carrier, Barnes, Argyle | 2014 | Suicidal Ideation | |
Towards Assessing Changes in Degree of Depression through Facebook | Schwartz, Eichstaedt, Kern, Park, Sap, Stillwell, Kosinski, Ungar | 2014 | Continuous Depression Score | |
Quantifying Mental Health Signals in Twitter | Coppersmith, Dredze, Harman | 2014 | Bipolar Disorder, Depression, PTSD, Seasonal Affective Disorder (SAD) | |
Characterizing and Predicting Postpartum Depression from Shared Facebook Data | De Choudhury, Counts, Horvitz, Hoff | 2014 | Post Partum Depression | |
Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users | Zhang, Huang, Liu, Chen, Zhu | Sina Weibo | 2014 | Suicidal Ideation |
Measuring post traumatic stress disorder in Twitter | Coppersmith, Harman, Dredze | 2014 | PTSD | |
Psychological stress detection from cross-media microblog data using deep sparse neural network | Lin, Jia, Guo, Xue, Li, Huang, Cai, Feng | Sina Weibo | 2014 | Stress |
Twitter: a good place to detect health conditions | Prieto, Matos, Alvarez, Cacheda, Oliveira | 2014 | Depression, Eating Disorders | |
Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons | Huang, Zhang, Liu, Chiu, Li, Zhu | Sina Weibo | 2014 | Suicidal Ideation |
Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale | Homan, Johar, Liu, Lytle, Silenzio, Alm | 2014 | Distress Level | |
Affective and content analysis of online depression communities | Nguyen, Phung, Dao, Venkatesh, Berk | LiveJournal | 2014 | Depression, Self-Harm, Suicide, Bipolar Disorder, Grief |
Defining patients with depressive disorder by using textual information | Nakamura, Kubo, Usuda, Aramaki | TOBYO Toshoshitsu (Disease Survivor Blogs) | 2014 | Depression |
A depression detection model based on sentiment analysis in micro-blog social network | Wang, Zhang, Ji, Sun, Wu, Bao | Sina Weibo | 2013 | Depression |
Activities on Facebook Reveal the Depressive State of Users | Park, Lee, Kwak, Cha, Jeong | 2013 | Depression | |
An improved model for depression detecting in micro-blog social network | Wang, Zhang, Sun | Sina Weibo | 2013 | Depression |
Perception Differences between the Depressed and Non-Depressed Users in Twitter | Park, McDonald, Cha | 2013 | Depression | |
Predicting Depression via Social Media | De Choudhury, Gamon, Counts, Horvitz | 2013 | Depression | |
Social Media As a Measurement Tool of Depression in Populations | De Choudhury, Counts, Horvitz | 2013 | Depression | |
Suicide Ideation of Individuals in Online Social Networks | Masuda, Kurahashi, Onari | Mixi | 2013 | Suicidal Ideation |
Exploiting Temporal Information in a Two-Stage Classification Framework for Content-Based Depression | Shen, Kuo, Chen, Lin | PTT (Bulletin Board System) | 2013 | Depression |
On estimating depressive tendency of twitter users from their tweet data | Tsugawa, Mogi, Kikuchi, Kishino, Fujita, Itoh, Ohsaki | 2013 | Depression | |
Predicting postpartum changes in emotion and behavior via social media | De Choudhury, Counts, Horvitz | 2013 | Behavioral Change for New Mothers (re: Postpartum Depression | |
Depressive Moods of Users Portrayed in Twitter | Park, Cha, Cha | 2012 | Depression, Control |