Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
  • Progga F and Rubya S. (2025). Women's Perspectives and Challenges in Adopting Perinatal Mental Health Technologies. Proceedings of the ACM on Human-Computer Interaction. 9:1. (1-30). Online publication date: 10-Jan-2025.

    https://doi.org/10.1145/3701217

  • Alvarado Garcia A, Yang T and Miceli M. (2025). What Knowledge Do We Produce from Social Media Data and How?. Proceedings of the ACM on Human-Computer Interaction. 9:1. (1-45). Online publication date: 10-Jan-2025.

    https://doi.org/10.1145/3701216

  • Wang Y, Li Y, Yu K and Yang J. (2024). A semantic structure-based emotion-guided model for emotion-cause pair extraction. Pattern Recognition. 10.1016/j.patcog.2024.111296. (111296). Online publication date: 1-Dec-2024.

    https://linkinghub.elsevier.com/retrieve/pii/S0031320324010471

  • Wang W, Blackburn K, Thompson R, Bajaj K, Pedler R and Fucci K. (2024). Trauma Isn’t One Size Fits All: How Online Support Communities Point to Different Diagnostic Criteria for C-PTSD and PTSD. Health Communication. 10.1080/10410236.2024.2314343. 39:13. (3272-3283). Online publication date: 9-Nov-2024.

    https://www.tandfonline.com/doi/full/10.1080/10410236.2024.2314343

  • Lin Y, Li N, Huang W, Ecsedy K, Feinberg M, Teti D and Carroll J. (2024). "Ultimately We're Together": Understanding New Parents' Experiences of Co-parenting. Proceedings of the ACM on Human-Computer Interaction. 8:CSCW2. (1-25). Online publication date: 7-Nov-2024.

    https://doi.org/10.1145/3687018

  • Pavez J and Allende H. (2024). A Hybrid System Based on Bayesian Networks and Deep Learning for Explainable Mental Health Diagnosis. Applied Sciences. 10.3390/app14188283. 14:18. (8283).

    https://www.mdpi.com/2076-3417/14/18/8283

  • Raturi A, Joshi K, Anupriya , Jain P, Gupta V and Meena J. (2024). Hate Speech Detection System using Machine Learning Algorithms 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT). 10.1109/InCACCT61598.2024.10551015. 979-8-3503-7131-4. (446-451).

    https://ieeexplore.ieee.org/document/10551015/

  • Mangalik S, Eichstaedt J, Giorgi S, Mun J, Ahmed F, Gill G, V. Ganesan A, Subrahmanya S, Soni N, Clouston S and Schwartz H. (2024). Robust language-based mental health assessments in time and space through social media. npj Digital Medicine. 10.1038/s41746-024-01100-0. 7:1.

    https://www.nature.com/articles/s41746-024-01100-0

  • Noh H, Go J, Song S, Kim S and Kang Y. (2024). Investigating the Possibility of Using an AR Mask to Support Online Psychological Counseling. Proceedings of the ACM on Human-Computer Interaction. 8:CSCW1. (1-33). Online publication date: 17-Apr-2024.

    https://doi.org/10.1145/3637355

  • Lu X, Powell J, Agapie E, Chen Y and Epstein D. (2024). Examining the Social Aspects of Pregnancy Tracking Applications. Proceedings of the ACM on Human-Computer Interaction. 8:CSCW1. (1-30). Online publication date: 17-Apr-2024.

    https://doi.org/10.1145/3637328

  • Zhang W, Xie J, Zhang Z and Liu X. (2024). Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach. Journal of Management Information Systems. 10.1080/07421222.2024.2340822. 41:2. (546-580). Online publication date: 2-Apr-2024.

    https://www.tandfonline.com/doi/full/10.1080/07421222.2024.2340822

  • Merhbene G, Puttick A and Kurpicz-Briki M. (2024). Investigating machine learning and natural language processing techniques applied for detecting eating disorders: a systematic literature review. Frontiers in Psychiatry. 10.3389/fpsyt.2024.1319522. 15.

    https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1319522/full

  • Aldkheel A and Zhou L. (2023). Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities. Journal of Healthcare Informatics Research. 10.1007/s41666-023-00152-3. 8:1. (88-120). Online publication date: 1-Mar-2024.

    https://link.springer.com/10.1007/s41666-023-00152-3

  • Cero I, Luo J and Falligant J. (2024). Lexicon-Based Sentiment Analysis in Behavioral Research. Perspectives on Behavior Science. 10.1007/s40614-023-00394-x. 47:1. (283-310). Online publication date: 1-Mar-2024.

    https://link.springer.com/10.1007/s40614-023-00394-x

  • Yom‐Tov E, Navar I, Fraenkel E and Berry J. (2023). Identifying amyotrophic lateral sclerosis through interactions with an internet search engine. Muscle & Nerve. 10.1002/mus.27991. 69:1. (40-47). Online publication date: 1-Jan-2024.

    https://onlinelibrary.wiley.com/doi/10.1002/mus.27991

  • Turjo M, Mundada K, Haque N and Ahmed N. (2023). Beyond Posts: A Protocol to Predict the Transition from Depression to Suicidal Ideation among Indo-Bangladeshi Individuals Using Facebook Data (Preprint). JMIR Research Protocols. 10.2196/55511.

    http://preprints.jmir.org/preprint/55511/accepted

  • Kalantari N, Payandeh A, Zampieri M and Motti V. (2023). Understanding the Language of ADHD and Autism Communities on Social Media 2023 IEEE International Conference on Big Data (BigData). 10.1109/BigData59044.2023.10386833. 979-8-3503-2445-7. (2188-2195).

    https://ieeexplore.ieee.org/document/10386833/

  • García-Noguez L, Tovar-Arriaga S, Paredes-García W, Ramos-Arreguín J and Aceves-Fernandez M. (2023). Automatic classification of depressive users on Twitter including temporal analysis. Network Modeling Analysis in Health Informatics and Bioinformatics. 10.1007/s13721-023-00434-1. 12:1.

    https://link.springer.com/10.1007/s13721-023-00434-1

  • Maah R. (2023). Cross-cultural conceptualisations of schizophrenia in Cameroonian languages. Southern African Linguistics and Applied Language Studies. 10.2989/16073614.2022.2128383. 41:4. (418-432). Online publication date: 2-Oct-2023.

    https://www.tandfonline.com/doi/full/10.2989/16073614.2022.2128383

  • Chancellor S, Feuston J and Chang J. (2023). Contextual Gaps in Machine Learning for Mental Illness Prediction: The Case of Diagnostic Disclosures. Proceedings of the ACM on Human-Computer Interaction. 7:CSCW2. (1-27). Online publication date: 28-Sep-2023.

    https://doi.org/10.1145/3610181

  • Hilty D, Stubbe D, McKean A, Hoffman P, Zalpuri I, Myint M, Joshi S, Pakyurek M and Li S. (2023). A scoping review of social media in child, adolescents and young adults: research findings in depression, anxiety and other clinical challenges. BJPsych Open. 10.1192/bjo.2023.523. 9:5. Online publication date: 1-Sep-2023.

    https://www.cambridge.org/core/product/identifier/S2056472423005239/type/journal_article

  • Paaresha Saraf , Mohini Biradar , Tejaswini Tupe , Tejas Ghorpade , Deepali Rane and Mukta Patil . (2023). A Review on Depression and Stress monitoring System via Social Media Data using Deep learning Framework. International Journal of Advanced Research in Science, Communication and Technology. 10.48175/IJARSCT-12070. (466-474).

    http://ijarsct.co.in/Paper12070.pdf

  • Chatterjee M, Modak S and Sarkar D. (2023). Mental Health Predictions Through Online Social Media Analytics. Cognitive Cardiac Rehabilitation Using IoT and AI Tools. 10.4018/978-1-6684-7561-4.ch004. (44-66).

    https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-7561-4.ch004

  • Cai Y, Wang H, Ye H, Jin Y and Gao W. (2023). Depression detection on online social network with multivariate time series feature of user depressive symptoms. Expert Systems with Applications. 10.1016/j.eswa.2023.119538. 217. (119538). Online publication date: 1-May-2023.

    https://linkinghub.elsevier.com/retrieve/pii/S0957417423000398

  • Mittal J, Belorkar A, Jakhetiya V, Pokuri V and Guntuku S. Language on Reddit Reveals Differential Mental Health Markers for Individuals posting in Immigration Communities. Proceedings of the 15th ACM Web Science Conference 2023. (153-162).

    https://doi.org/10.1145/3578503.3583600

  • Schaadhardt A, Fu Y, Pratt C and Pratt W. “Laughing so I don’t cry”: How TikTok users employ humor and compassion to connect around psychiatric hospitalization. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (1-13).

    https://doi.org/10.1145/3544548.3581559

  • Scott C, Marcu G, Anderson R, Newman M and Schoenebeck S. Trauma-Informed Social Media: Towards Solutions for Reducing and Healing Online Harm. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (1-20).

    https://doi.org/10.1145/3544548.3581512

  • Oguamanam V, Hernandez N, Chandler R, Guillaume D, Mckeever K, Allen M, Mohammed S and Parker A. An Intersectional Look at Use of and Satisfaction with Digital Mental Health Platforms: A Survey of Perinatal Black Women. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (1-20).

    https://doi.org/10.1145/3544548.3581475

  • Progga F, Senthil Kumar A and Rubya S. Understanding the Online Social Support Dynamics for Postpartum Depression. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (1-17).

    https://doi.org/10.1145/3544548.3581311

  • Alvarado Garcia A, Wong-Villacres M, Miceli M, Hernández B and Le Dantec C. Mobilizing Social Media Data: Reflections of a Researcher Mediating between Data and Organization. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (1-19).

    https://doi.org/10.1145/3544548.3580916

  • Jin Y, Cai W, Chen L, Dai Y and Jiang T. (2023). Understanding Disclosure and Support for Youth Mental Health in Social Music Communities. Proceedings of the ACM on Human-Computer Interaction. 7:CSCW1. (1-32). Online publication date: 14-Apr-2023.

    https://doi.org/10.1145/3579629

  • Pater J, Coupe A, Nova F, Pfafman R, Carroll J, Brouwer A, Bohn C, Li J, Todd N, Chang F and Guha S. (2023). Social Media is not a Health Proxy: Differences Between Social Media and Electronic Health Record Reports of Post-COVID Symptoms. Proceedings of the ACM on Human-Computer Interaction. 7:CSCW1. (1-25). Online publication date: 14-Apr-2023.

    https://doi.org/10.1145/3579624

  • Yao X, Mikhelson M, Micheletti M, Choi E, Watkins S, Thomaz E and de Barbaro K. (2023). Understanding Postpartum Parents' Experiences via Two Digital Platforms. Proceedings of the ACM on Human-Computer Interaction. 7:CSCW1. (1-23). Online publication date: 14-Apr-2023.

    https://doi.org/10.1145/3579540

  • Yoo D, Bhatnagar A, Ernala S, Ali A, Birnbaum M, Abowd G and De Choudhury M. (2023). Discussing Social Media During Psychotherapy Consultations: Patient Narratives and Privacy Implications. Proceedings of the ACM on Human-Computer Interaction. 7:CSCW1. (1-24). Online publication date: 14-Apr-2023.

    https://doi.org/10.1145/3579479

  • Ehsan U, Saha K, De Choudhury M and Riedl M. (2023). Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI. Proceedings of the ACM on Human-Computer Interaction. 7:CSCW1. (1-32). Online publication date: 14-Apr-2023.

    https://doi.org/10.1145/3579467

  • Burkhardt H, Ding X, Kerbrat A, Comtois K and Cohen T. (2023). From benchmark to bedside: transfer learning from social media to patient-provider text messages for suicide risk prediction. Journal of the American Medical Informatics Association. 10.1093/jamia/ocad062.

    https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocad062/7116301

  • Zhu J, Li Z, Zhang X, Zhang Z and Hu B. (2023). Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis. Journal of Medical Internet Research. 10.2196/45777. 25. (e45777).

    https://www.jmir.org/2023/1/e45777

  • Allen K, Davis A and Krishnamurti T. Indirect Identification of Perinatal Psychosocial Risks From Natural Language. IEEE Transactions on Affective Computing. 10.1109/TAFFC.2021.3079282. 14:2. (1506-1519).

    https://ieeexplore.ieee.org/document/9428347/

  • Savekar A, Tarai S and Singh M. (2022). Structural and functional markers of language signify the symptomatic effect of depression: A systematic literature review. European Journal of Applied Linguistics. 10.1515/eujal-2022-0022. 11:1. (190-224). Online publication date: 7-Feb-2023.. Online publication date: 1-Mar-2023.

    https://www.degruyter.com/document/doi/10.1515/eujal-2022-0022/html

  • Ansari L, Ji S, Chen Q and Cambria E. Ensemble Hybrid Learning Methods for Automated Depression Detection. IEEE Transactions on Computational Social Systems. 10.1109/TCSS.2022.3154442. 10:1. (211-219).

    https://ieeexplore.ieee.org/document/9733425/

  • Schoenebeck S, Shen Y and Davidson J. (2022). Evaluating the Social Media Profiles of Online Harassers. Proceedings of the ACM on Human-Computer Interaction. 7:GROUP. (1-14). Online publication date: 1-Jan-2023.

    https://doi.org/10.1145/3567557

  • Gopalakrishnan A, Gururajan R, Venkataraman R, Zhou X and Chan K. (2023). A Combined Attribute Extraction Method for Detecting Postpartum Depression Using Social Media. Health Information Science. 10.1007/978-981-99-7108-4_2. (17-29).

    https://link.springer.com/10.1007/978-981-99-7108-4_2

  • Tlachac M, Flores R, Toto E and Rundensteiner E. (2023). Early Mental Health Uncovering with Short Scripted and Unscripted Voice Recordings. Deep Learning Applications, Volume 4. 10.1007/978-981-19-6153-3_4. (79-110).

    https://link.springer.com/10.1007/978-981-19-6153-3_4

  • Mandal S and Saha B. (2023). Forecasting Mental Disorders Through Aspect Identification from Social Media Posts. Cyber Technologies and Emerging Sciences. 10.1007/978-981-19-2538-2_13. (133-141).

    https://link.springer.com/10.1007/978-981-19-2538-2_13

  • Ghosh S, Froelich N and Aragon C. (2023). “I Love You, My Dear Friend”: Analyzing the Role of Emotions in the Building of Friendships in Online Fanfiction Communities. Social Computing and Social Media. 10.1007/978-3-031-35927-9_32. (466-485).

    https://link.springer.com/10.1007/978-3-031-35927-9_32

  • Haq E, Lee L, Tyson G, Mogavi R, Braud T and Hui P. (2022). Exploring Mental Health Communications among Instagram Coaches 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 10.1109/ASONAM55673.2022.10068611. 978-1-6654-5661-6. (218-225).

    https://ieeexplore.ieee.org/document/10068611/

  • Haldar S, Studd H, Wong N, Mohr D, Reddy M and Miller E. (2022). Collaboration Challenges and Technology Opportunities at the Intersection of Perinatal and Mental Health Journeys. Proceedings of the ACM on Human-Computer Interaction. 6:CSCW2. (1-28). Online publication date: 7-Nov-2022.

    https://doi.org/10.1145/3555614

  • Feuston J, DeVito M, Scheuerman M, Weathington K, Benitez M, Perez B, Sondheim L and Brubaker J. (2022). "Do You Ladies Relate?": Experiences of Gender Diverse People in Online Eating Disorder Communities. Proceedings of the ACM on Human-Computer Interaction. 6:CSCW2. (1-32). Online publication date: 7-Nov-2022.

    https://doi.org/10.1145/3555145

  • Mathur V, Lustig C and Kaziunas E. (2022). Disordering Datasets. Proceedings of the ACM on Human-Computer Interaction. 6:CSCW2. (1-33). Online publication date: 7-Nov-2022.

    https://doi.org/10.1145/3555141

  • Malhotra A and Jindal R. (2022). Deep learning techniques for suicide and depression detection from online social media. Applied Soft Computing. 130:C. Online publication date: 1-Nov-2022.

    https://doi.org/10.1016/j.asoc.2022.109713

  • Dimitriadis I, Poiitis M, Faloutsos C and Vakali A. (2022). TG-OUT: temporal outlier patterns detection in Twitter attribute induced graphs. World Wide Web. 10.1007/s11280-021-00986-0. 25:6. (2429-2453). Online publication date: 1-Nov-2022.

    https://link.springer.com/10.1007/s11280-021-00986-0

  • Pérez A, Parapar J and Barreiro Á. (2022). Automatic depression score estimation with word embedding models. Artificial Intelligence in Medicine. 10.1016/j.artmed.2022.102380. 132. (102380). Online publication date: 1-Oct-2022.

    https://linkinghub.elsevier.com/retrieve/pii/S093336572200135X

  • Kabatnik S. (2022). Twitter as a helping medium. Relationships in Organized Helping. 10.1075/pbns.331.13kab. (287-314). Online publication date: 7-Sep-2022.

    https://benjamins.com/catalog/pbns.331.13kab

  • Rehman A, Naz S and Razzak I. (2022). Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems. 28:4. (1339-1371). Online publication date: 1-Aug-2022.

    https://doi.org/10.1007/s00530-020-00736-8

  • Tlachac M, Flores R, Reisch M, Kayastha R, Taurich N, Melican V, Bruneau C, Caouette H, Lovering J, Toto E and Rundensteiner E. (2022). StudentSADD. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 6:2. (1-32). Online publication date: 4-Jul-2022.

    https://doi.org/10.1145/3534604

  • Skorburg J and Yam J. (2021). Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19. AJOB Neuroscience. 10.1080/21507740.2021.1918284. 13:3. (177-190). Online publication date: 3-Jul-2022.

    https://www.tandfonline.com/doi/full/10.1080/21507740.2021.1918284

  • Fukazawa Y. (2022). Estimating Mental Health Using Human-generated Big Data and Machine Learning人が生み出すビッグデータと機械学習によるメンタルヘルスの推定. The Brain & Neural Networks. 10.3902/jnns.29.78. 29:2. (78-94). Online publication date: 5-Jun-2022.

    https://www.jstage.jst.go.jp/article/jnns/29/2/29_78/_article/-char/ja/

  • Tai C, Fang Y and Chang Y. (2017). SOS-DR: a social warning system for detecting users at high risk of depression. Personal and Ubiquitous Computing. 10.1007/s00779-017-1092-3. 26:3. (837-848). Online publication date: 1-Jun-2022.

    https://link.springer.com/10.1007/s00779-017-1092-3

  • Du T, Wu K, Ma P, Wah S, Spielberg A, Rus D and Matusik W. (2021). DiffPD: Differentiable Projective Dynamics. ACM Transactions on Graphics. 41:2. (1-21). Online publication date: 30-Apr-2022.

    https://doi.org/10.1145/3490168

  • Rixen J, Colley M, Askari A, Gugenheimer J and Rukzio E. Consent in the Age of AR: Investigating The Comfort With Displaying Personal Information in Augmented Reality. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. (1-14).

    https://doi.org/10.1145/3491102.3502140

  • Lustig C, Konrad A and Brubaker J. Designing for the Bittersweet: Improving Sensitive Experiences with Recommender Systems. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. (1-18).

    https://doi.org/10.1145/3491102.3502049

  • Davalos S and Feroz E. (2022). A textual analysis of the US Securities and Exchange Commission's accounting and auditing enforcement releases relating to the Sarbanes–Oxley Act. International Journal of Intelligent Systems in Accounting and Finance Management. 29:1. (19-40). Online publication date: 25-Apr-2022.

    https://doi.org/10.1002/isaf.1506

  • Bilal A, Fransson E, Bränn E, Eriksson A, Zhong M, Gidén K, Elofsson U, Axfors C, Skalkidou A and Papadopoulos F. (2022). Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol. BMJ Open. 10.1136/bmjopen-2021-059033. 12:4. (e059033). Online publication date: 1-Apr-2022.

    https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2021-059033

  • Shibuya Y, Hamm A and Cerratto Pargman T. (2022). Mapping HCI research methods for studying social media interaction. Computers in Human Behavior. 129:C. Online publication date: 1-Apr-2022.

    https://doi.org/10.1016/j.chb.2021.107131

  • Hovy D. (2022). Text Analysis in Python for Social Scientists

    https://www.cambridge.org/core/product/identifier/9781108960885/type/element

  • Stupinski A, Alshaabi T, Arnold M, Adams J, Minot J, Price M, Dodds P and Danforth C. (2022). Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study. JMIR Mental Health. 10.2196/33685. 9:3. (e33685).

    https://mental.jmir.org/2022/3/e33685

  • Burgess E, Reddy M and Mohr D. (2022). "I Just Can't Help But Smile Sometimes": Collaborative Self-Management of Depression. Proceedings of the ACM on Human-Computer Interaction. 6:CSCW1. (1-32). Online publication date: 30-Mar-2022.

    https://doi.org/10.1145/3512917

  • Kelley S, Mhaonaigh C, Burke L, Whelan R and Gillan C. (2022). Machine learning of language use on Twitter reveals weak and non-specific predictions. npj Digital Medicine. 10.1038/s41746-022-00576-y. 5:1.

    https://www.nature.com/articles/s41746-022-00576-y

  • Ahmad H, Nasir F, Faisal C and Ahmad S. (2022). Depression Detection in Online Social Media Users Using Natural Language Processing Techniques. Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media. 10.4018/978-1-7998-9594-7.ch013. (323-347).

    https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-9594-7.ch013

  • Oduru T, Jordan A and Park A. (2022). Healthy vs. Unhealthy Food Images: Image Classification of Twitter Images. International Journal of Environmental Research and Public Health. 10.3390/ijerph19020923. 19:2. (923).

    https://www.mdpi.com/1660-4601/19/2/923

  • Patil S, More N, Bhawtankar A, Jagtap V and Jadhav A. (2022). Artificial Intelligence and Machine Learning-Based Advanced Depression Detection System. Handbook of Research on Applied Intelligence for Health and Clinical Informatics. 10.4018/978-1-7998-7709-7.ch006. (92-114).

    http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-7709-7.ch006

  • Krishnamurti T, Allen K, Hayani L, Rodriguez S and Davis A. (2022). Identification of maternal depression risk from natural language collected in a mobile health app. Procedia Computer Science. 206:C. (132-140). Online publication date: 1-Jan-2022.

    https://doi.org/10.1016/j.procs.2022.09.092

  • Biswas S and Hasija Y. (2022). Predicting Depression Through Social Media. Predictive Analytics of Psychological Disorders in Healthcare. 10.1007/978-981-19-1724-0_6. (109-127).

    https://link.springer.com/10.1007/978-981-19-1724-0_6

  • Trübner M and Mühlichen A. (2022). Big Data. Handbuch Methoden der empirischen Sozialforschung. 10.1007/978-3-658-37985-8_10. (179-193).

    https://link.springer.com/10.1007/978-3-658-37985-8_10

  • Raisa J, Kaiser M and Mahmud M. (2022). A Machine Learning Approach for Early Detection of Postpartum Depression in Bangladesh. Brain Informatics. 10.1007/978-3-031-15037-1_20. (241-252).

    https://link.springer.com/10.1007/978-3-031-15037-1_20

  • Hilty D, Ahuja S, Naslund J and Crawford A. (2022). Approaches to Virtual Care in Underserved Communities and Settings: Bridging the Behavioral Health-Care Gap. Virtual Mental Health Care for Rural and Underserved Settings. 10.1007/978-3-031-11984-2_7. (101-129).

    https://link.springer.com/10.1007/978-3-031-11984-2_7

  • Mothe J, Ramiandrisoa F and Ullah M. (2022). Comparison of Machine Learning Models for Early Depression Detection from Users’ Posts. Early Detection of Mental Health Disorders by Social Media Monitoring. 10.1007/978-3-031-04431-1_5. (111-139).

    https://link.springer.com/10.1007/978-3-031-04431-1_5

  • Uban A, Chulvi B and Rosso P. (2022). Explainability of Depression Detection on Social Media: From Deep Learning Models to Psychological Interpretations and Multimodality. Early Detection of Mental Health Disorders by Social Media Monitoring. 10.1007/978-3-031-04431-1_13. (289-320).

    https://link.springer.com/10.1007/978-3-031-04431-1_13

  • Samtani S, Li W, Benjamin V and Chen H. (2021). Informing Cyber Threat Intelligence through Dark Web Situational Awareness: The AZSecure Hacker Assets Portal. Digital Threats: Research and Practice. 2:4. (1-10). Online publication date: 31-Dec-2022.

    https://doi.org/10.1145/3450972

  • Aksoy S, Purvine E and Young S. (2021). Directional Laplacian Centrality for Cyber Situational Awareness. Digital Threats: Research and Practice. 2:4. (1-28). Online publication date: 31-Dec-2022.

    https://doi.org/10.1145/3450286

  • Sakib A, Mukta M, Huda F, Islam A, Islam T and Ali M. (2021). Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets. Journal of Medical Internet Research. 10.2196/27613. 23:12. (e27613).

    https://www.jmir.org/2021/12/e27613

  • Tlachac M, Toto E, Lovering J, Kayastha R, Taurich N and Rundensteiner E. (2021). EMU: Early Mental Health Uncovering Framework and Dataset 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). 10.1109/ICMLA52953.2021.00213. 978-1-6654-4337-1. (1311-1318).

    https://ieeexplore.ieee.org/document/9680143/

  • Silveira B, Silva H, Murai F and da Silva A. (2021). Predicting user emotional tone in mental disorder online communities. Future Generation Computer Systems. 125:C. (641-651). Online publication date: 1-Dec-2021.

    https://doi.org/10.1016/j.future.2021.07.014

  • Skaik R and Inkpen D. (2020). Using Social Media for Mental Health Surveillance. ACM Computing Surveys. 53:6. (1-31). Online publication date: 30-Nov-2021.

    https://doi.org/10.1145/3422824

  • Vinella F, Lykourentzou I and Masthoff J. (2021). Users' Preferences of Profiling Attributes on Crowdsourcing Team Formation Systems 2021 16th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). 10.1109/SMAP53521.2021.9610773. 978-1-6654-4241-1. (1-10).

    https://ieeexplore.ieee.org/document/9610773/

  • Uban A, Chulvi B and Rosso P. (2021). An emotion and cognitive based analysis of mental health disorders from social media data. Future Generation Computer Systems. 124:C. (480-494). Online publication date: 1-Nov-2021.

    https://doi.org/10.1016/j.future.2021.05.032

  • Nanomi Arachchige I, Sandanapitchai P and Weerasinghe R. (2021). Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review. Information. 10.3390/info12110444. 12:11. (444).

    https://www.mdpi.com/2078-2489/12/11/444

  • Caddle X, Razi A, Kim S, Ali S, Popo T, Stringhini G, De Choudhury M and Wisniewski P. MOSafely: Building an Open-Source HCAI Community to Make the Internet a Safer Place for Youth. Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing. (315-318).

    https://doi.org/10.1145/3462204.3481731

  • Freire-Vidal Y, Graells-Garrido E and Rowe F. (2021). A Framework to Understand Attitudes towards Immigration through Twitter. Applied Sciences. 10.3390/app11209689. 11:20. (9689).

    https://www.mdpi.com/2076-3417/11/20/9689

  • Roemmich K and Andalibi N. (2021). Data Subjects' Conceptualizations of and Attitudes Toward Automatic Emotion Recognition-Enabled Wellbeing Interventions on Social Media. Proceedings of the ACM on Human-Computer Interaction. 5:CSCW2. (1-34). Online publication date: 13-Oct-2021.

    https://doi.org/10.1145/3476049

  • Gaeta R and Grangetto M. (2021). Malicious Node Identification in Coded Distributed Storage Systems under Pollution Attacks. ACM Transactions on Modeling and Performance Evaluation of Computing Systems. 6:3. (1-27). Online publication date: 30-Sep-2021.

    https://doi.org/10.1145/3491062

  • Engelmann A and Jukan A. (2021). A Combinatorial Reliability Analysis of Generic Service Function Chains in Data Center Networks. ACM Transactions on Modeling and Performance Evaluation of Computing Systems. 6:3. (1-24). Online publication date: 30-Sep-2021.

    https://doi.org/10.1145/3477046

  • Taghvaei N, Masoumi B and Keyvanpour M. Analytical framework for mental health feature extraction methods in social networks. Intelligent Decision Technologies. 10.3233/IDT-200097. 15:3. (343-356).

    https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/IDT-200097

  • Barron D, Baker J, Budde K, Bzdok D, Eickhoff S, Friston K, Fox P, Geha P, Heisig S, Holmes A, Onnela J, Powers A, Silbersweig D and Krystal J. (2021). Decision Models and Technology Can Help Psychiatry Develop Biomarkers. Frontiers in Psychiatry. 10.3389/fpsyt.2021.706655. 12.

    https://www.frontiersin.org/articles/10.3389/fpsyt.2021.706655/full

  • Wongkoblap A, Vadillo M and Curcin V. (2021). Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study. JMIR Mental Health. 10.2196/19824. 8:8. (e19824).

    https://mental.jmir.org/2021/8/e19824

  • Ramírez-Cifuentes D, Freire A, Baeza-Yates R, Sanz Lamora N, Álvarez A, González-Rodríguez A, Lozano Rochel M, Llobet Vives R, Velazquez D, Gonfaus J and Gonzàlez J. (2021). Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis. Journal of Medical Internet Research. 10.2196/25925. 23:7. (e25925).

    https://www.jmir.org/2021/7/e25925

  • Zogan H, Razzak I, Jameel S and Xu G. (2021). DepressionNet: Learning Multi-modalities with User Post Summarization for Depression Detection on Social Media SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 10.1145/3404835.3462938. 9781450380379. (133-142). Online publication date: 11-Jul-2021.

    https://dl.acm.org/doi/10.1145/3404835.3462938

  • Trifan A and Oliveira J. (2021). Cross-evaluation of social mining for classification of depressed online personas. Journal of Integrative Bioinformatics. 10.1515/jib-2020-0051. 18:2. (101-110). Online publication date: 22-Jun-2021.. Online publication date: 1-Jun-2021.

    https://www.degruyter.com/document/doi/10.1515/jib-2020-0051/html

  • Saha K, Seybolt J, Mattingly S, Aledavood T, Konjeti C, Martinez G, Grover T, Mark G and De Choudhury M. What Life Events are Disclosed on Social Media, How, When, and By Whom?. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. (1-22).

    https://doi.org/10.1145/3411764.3445405

  • Saqib K, Khan A and Butt Z. (2021). Machine Learning Methods for Predicting Postpartum Depression: A Scoping Review (Preprint). JMIR Mental Health. 10.2196/29838.

    http://preprints.jmir.org/preprint/29838/accepted

  • Wang L, Wang D, Tian F, Peng Z, Fan X, Zhang Z, Yu M, Ma X and Wang H. (2021). CASS. Proceedings of the ACM on Human-Computer Interaction. 5:CSCW1. (1-31). Online publication date: 13-Apr-2021.

    https://doi.org/10.1145/3449083

  • Proferes N, Jones N, Gilbert S, Fiesler C and Zimmer M. (2021). Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics. Social Media + Society. 10.1177/20563051211019004. 7:2. Online publication date: 1-Apr-2021.

    https://journals.sagepub.com/doi/10.1177/20563051211019004

  • Saha K, Grover T, Mattingly S, swain V, Gupta P, Martinez G, Robles-Granda P, Mark G, Striegel A and De Choudhury M. (2021). Person-Centered Predictions of Psychological Constructs with Social Media Contextualized by Multimodal Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 5:1. (1-32). Online publication date: 19-Mar-2021.

    https://doi.org/10.1145/3448117

  • Sigman M, Slezak D, Drucaroff L, Ribeiro S and Carrillo F. (2021). Artificial and Human Intelligence in Mental Health. AI Magazine. 42:1. (39-46). Online publication date: 1-Mar-2021.

    https://doi.org/10.1002/j.2371-9621.2021.tb00009.x

  • Kruzan K, Whitlock J and Bazarova N. (2021). Examining the Relationship Between the Use of a Mobile Peer-Support App and Self-Injury Outcomes: Longitudinal Mixed Methods Study. JMIR Mental Health. 10.2196/21854. 8:1. (e21854).

    http://mental.jmir.org/2021/1/e21854/

  • Farka F, Nanevski A, Banerjee A, Delbianco G and Fábregas I. (2021). On algebraic abstractions for concurrent separation logics. Proceedings of the ACM on Programming Languages. 5:POPL. (1-32). Online publication date: 4-Jan-2021.

    https://doi.org/10.1145/3434286

  • HUANG G and ZHOU X. (2021). The linguistic patterns of depressed patients. Advances in Psychological Science. 10.3724/SP.J.1042.2021.00838. 29:5. (838).

    http://journal.psych.ac.cn/xlkxjz/CN/10.3724/SP.J.1042.2021.00838

  • Lara J, Aragón M, González F and Montes-y-Gómez M. (2021). Deep Bag-of-Sub-Emotions for Depression Detection in Social Media. Text, Speech, and Dialogue. 10.1007/978-3-030-83527-9_5. (60-72).

    https://link.springer.com/10.1007/978-3-030-83527-9_5

  • Uban A, Chulvi B and Rosso P. (2021). On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data. Natural Language Processing and Information Systems. 10.1007/978-3-030-80599-9_27. (301-314).

    https://link.springer.com/10.1007/978-3-030-80599-9_27

  • Ghosh A and Dey S. (2021). “Sensing the Mind”: An Exploratory Study About Sensors Used in E-Health and M-Health Applications for Diagnosis of Mental Health Condition. Efficient Data Handling for Massive Internet of Medical Things. 10.1007/978-3-030-66633-0_12. (269-292).

    https://link.springer.com/10.1007/978-3-030-66633-0_12

  • Zhang W, Liu L, Cheng Q, Chen Y, Xu D and Gong W. (2020). The Relationship Between Images Posted by New Mothers on WeChat Moments and Postpartum Depression: Cohort Study. Journal of Medical Internet Research. 10.2196/23575. 22:11. (e23575).

    http://www.jmir.org/2020/11/e23575/

  • Magami F and Digiampietri L. Automatic detection of depression from text data. Proceedings of the XVI Brazilian Symposium on Information Systems. (1-8).

    https://doi.org/10.1145/3411564.3411603

  • Kamite S and Kamble V. (2020). Detection of Depression in Social Media via Twitter Using Machine learning Approach 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC). 10.1109/ICSIDEMPC49020.2020.9299641. 978-1-7281-5970-6. (122-125).

    https://ieeexplore.ieee.org/document/9299641/

  • Arora A, Chakraborty P, Bhatia M and Mittal P. (2020). Role of Emotion in Excessive Use of Twitter During COVID-19 Imposed Lockdown in India. Journal of Technology in Behavioral Science. 10.1007/s41347-020-00174-3.

    http://link.springer.com/10.1007/s41347-020-00174-3

  • Lee S, Oh H, Shi C and Doh Y. (2020). Life Review Using a Life Metaphoric Game to Promote Intergenerational Communication. Proceedings of the ACM on Human-Computer Interaction. 4:CSCW2. (1-21). Online publication date: 14-Oct-2020.

    https://doi.org/10.1145/3415169

  • Yom-Tov E and Cherlow Y. (2020). Ethical Challenges and Opportunities Associated With the Ability to Perform Medical Screening From Interactions With Search Engines: Viewpoint. Journal of Medical Internet Research. 10.2196/21922. 22:9. (e21922).

    http://www.jmir.org/2020/9/e21922/

  • Cesare N, Oladeji O, Ferryman K, Wijaya D, Hendricks‐Muñoz K, Ward A and Nsoesie E. (2020). Discussions of miscarriage and preterm births on Twitter. Paediatric and Perinatal Epidemiology. 10.1111/ppe.12622. 34:5. (544-552). Online publication date: 1-Sep-2020.

    https://onlinelibrary.wiley.com/doi/10.1111/ppe.12622

  • Shatte A, Hutchinson D, Fuller-Tyszkiewicz M and Teague S. (2020). Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach. Cyberpsychology, Behavior, and Social Networking. 10.1089/cyber.2019.0746. 23:9. (611-618). Online publication date: 1-Sep-2020.

    https://www.liebertpub.com/doi/10.1089/cyber.2019.0746

  • Yoo D, Birnbaum M, Van Meter A, Ali A, Arenare E, Abowd G and De Choudhury M. (2020). Designing a Clinician-Facing Tool for Using Insights From Patients’ Social Media Activity: Iterative Co-Design Approach. JMIR Mental Health. 10.2196/16969. 7:8. (e16969).

    http://mental.jmir.org/2020/8/e16969/

  • Qiao J. (2020). A Systematic Review of Machine Learning Approaches for Mental Disorder Prediction on Social Media 2020 International Conference on Computing and Data Science (CDS). 10.1109/CDS49703.2020.00091. 978-1-7281-7106-7. (433-438).

    https://ieeexplore.ieee.org/document/9276033/

  • Kim J, Lee J, Park E and Han J. (2020). A deep learning model for detecting mental illness from user content on social media. Scientific Reports. 10.1038/s41598-020-68764-y. 10:1.

    https://www.nature.com/articles/s41598-020-68764-y

  • Vasquez-Henriquez P, Graells-Garrido E and Caro D. (2020). Tweets on the Go: Gender Differences in Transport Perception and Its Discussion on Social Media. Sustainability. 10.3390/su12135405. 12:13. (5405).

    https://www.mdpi.com/2071-1050/12/13/5405

  • Parrott S, Britt B, Hayes J and Albright D. (2020). Social Media and Suicide: A Validation of Terms to Help Identify Suicide-related Social Media Posts. Journal of Evidence-Based Social Work. 10.1080/26408066.2020.1788478. (1-11).

    https://www.tandfonline.com/doi/full/10.1080/26408066.2020.1788478

  • Morris M. (2022). Enhancing relationships through technology: directions in parenting, caregiving, romantic partnerships, and clinical practice
. Dialogues in Clinical Neuroscience. 10.31887/DCNS.2020.22.2/mmorris. 22:2. (151-160). Online publication date: 30-Jun-2020.

    https://www.tandfonline.com/doi/full/10.31887/DCNS.2020.22.2/mmorris

  • Dimitriadis I, Poiitis M, Faloutsos C and Vakali A. TRIAGE. Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics. (44-53).

    https://doi.org/10.1145/3405962.3405998

  • Kaur J and Singh P. Understanding Engagement of Parents In Online Health Communities for Early Childhood. Proceedings of the 2020 International Conference on Information and Communication Technologies and Development. (1-5).

    https://doi.org/10.1145/3392561.3397582

  • Burleson G, Naseem M and Toyama K. An Exploration of African-American Pregnant Women's Information-Seeking Behavior in Detroit. Proceedings of the 2020 International Conference on Information and Communication Technologies and Development. (1-12).

    https://doi.org/10.1145/3392561.3394647

  • Chang M and Tseng C. (2020). Detecting Social Anxiety with Online Social Network Data 2020 21st IEEE International Conference on Mobile Data Management (MDM). 10.1109/MDM48529.2020.00073. 978-1-7281-4663-8. (333-336).

    https://ieeexplore.ieee.org/document/9162194/

  • Song S, Yamashita N and Kim J. Bodeum. Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare. (38-49).

    https://doi.org/10.1145/3421937.3421973

  • Eschler J, Burgess E, Reddy M and Mohr D. Emergent Self-Regulation Practices in Technology and Social Media Use of Individuals Living with Depression. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. (1-13).

    https://doi.org/10.1145/3313831.3376773

  • Peng Z, Guo Q, Tsang K and Ma X. Exploring the Effects of Technological Writing Assistance for Support Providers in Online Mental Health Community. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. (1-15).

    https://doi.org/10.1145/3313831.3376695

  • Andalibi N and Buss J. The Human in Emotion Recognition on Social Media: Attitudes, Outcomes, Risks. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. (1-16).

    https://doi.org/10.1145/3313831.3376680

  • Chancellor S and De Choudhury M. (2020). Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine. 10.1038/s41746-020-0233-7. 3:1.

    https://www.nature.com/articles/s41746-020-0233-7

  • Ramírez-Cifuentes D, Freire A, Baeza-Yates R, Vidal J, Medina-Bravo P, Velazquez D, Gonfaus J and Gonzalez J. (2020). Suicide Risk Detection on Social Media: A Multi-modal Approach (Preprint). Journal of Medical Internet Research. 10.2196/17758.

    http://preprints.jmir.org/preprint/17758/accepted

  • Alghamdi N, Hosni Mahmoud H, Abraham A, Alanazi S and Garcia-Hernandez L. Predicting Depression Symptoms in an Arabic Psychological Forum. IEEE Access. 10.1109/ACCESS.2020.2981834. 8. (57317-57334).

    https://ieeexplore.ieee.org/document/9040556/

  • Mazuz K and Yom-Tov E. (2019). Analyzing trends of loneliness through large-scale analysis of social media postings: Observational study (Preprint). JMIR Mental Health. 10.2196/17188.

    http://preprints.jmir.org/preprint/17188/accepted

  • Zhang J, Tan C and Lv Q. (2019). Intergroup Contact in the Wild. Proceedings of the ACM on Human-Computer Interaction. 3:CSCW. (1-35). Online publication date: 7-Nov-2019.

    https://doi.org/10.1145/3359295

  • Chancellor S, Baumer E and De Choudhury M. (2019). Who is the "Human" in Human-Centered Machine Learning. Proceedings of the ACM on Human-Computer Interaction. 3:CSCW. (1-32). Online publication date: 7-Nov-2019.

    https://doi.org/10.1145/3359249

  • Antoniak M, Mimno D and Levy K. (2019). Narrative Paths and Negotiation of Power in Birth Stories. Proceedings of the ACM on Human-Computer Interaction. 3:CSCW. (1-27). Online publication date: 7-Nov-2019.

    https://doi.org/10.1145/3359190

  • Pater J, Farrington B, Brown A, Reining L, Toscos T and Mynatt E. (2019). Exploring Indicators of Digital Self-Harm with Eating Disorder Patients. Proceedings of the ACM on Human-Computer Interaction. 3:CSCW. (1-26). Online publication date: 7-Nov-2019.

    https://doi.org/10.1145/3359186

  • Burgess E, Ringland K, Nicholas J, Knapp A, Eschler J, Mohr D and Reddy M. (2019). "I think people are powerful". Proceedings of the ACM on Human-Computer Interaction. 3:CSCW. (1-29). Online publication date: 7-Nov-2019.

    https://doi.org/10.1145/3359143

  • Horvitz E and Mulligan D. (2019). Data, Privacy, and the Greater Good. Next-Generation Ethics. 10.1017/9781108616188.007. (81-89).

    https://www.cambridge.org/core/product/identifier/9781108616188%23CN-bp-7/type/book_part

  • Modrek S and Chakalov B. (2019). The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations. Journal of Medical Internet Research. 10.2196/13837. 21:9. (e13837).

    https://www.jmir.org/2019/9/e13837/

  • Kruzan K and Won A. (2019). Embodied well-being through two media technologies: Virtual reality and social media. New Media & Society. 10.1177/1461444819829873. 21:8. (1734-1749). Online publication date: 1-Aug-2019.

    https://journals.sagepub.com/doi/10.1177/1461444819829873

  • Fatima I, Abbasi B, Khan S, Al‐Saeed M, Ahmad H and Mumtaz R. (2019). Prediction of postpartum depression using machine learning techniques from social media text. Expert Systems. 10.1111/exsy.12409. 36:4. Online publication date: 1-Aug-2019.

    https://onlinelibrary.wiley.com/doi/10.1111/exsy.12409

  • Veletsianos G, Johnson N and Belikov O. (2019). Academics' social media use over time is associated with individual, relational, cultural and political factors. British Journal of Educational Technology. 10.1111/bjet.12788. 50:4. (1713-1728). Online publication date: 1-Jul-2019.

    https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.12788

  • Zaman A, Acharyya R, Kautz H and Silenzio V. Detecting Low Self-Esteem in Youths from Web Search Data. The World Wide Web Conference. (2270-2280).

    https://doi.org/10.1145/3308558.3313557

  • Pater J, Reining L, Miller A, Toscos T and Mynatt E. "Notjustgirls". Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. (1-13).

    https://doi.org/10.1145/3290605.3300881

  • Feuston J and Piper A. Everyday Experiences. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. (1-14).

    https://doi.org/10.1145/3290605.3300495

  • Sanches P, Janson A, Karpashevich P, Nadal C, Qu C, Daudén Roquet C, Umair M, Windlin C, Doherty G, Höök K and Sas C. HCI and Affective Health. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. (1-17).

    https://doi.org/10.1145/3290605.3300475

  • Ernala S, Birnbaum M, Candan K, Rizvi A, Sterling W, Kane J and De Choudhury M. Methodological Gaps in Predicting Mental Health States from Social Media. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. (1-16).

    https://doi.org/10.1145/3290605.3300364

  • Rosa R, Schwartz G, Ruggiero W and Rodriguez D. A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning. IEEE Transactions on Industrial Informatics. 10.1109/TII.2018.2867174. 15:4. (2124-2135).

    https://ieeexplore.ieee.org/document/8445585/

  • Chancellor S, Birnbaum M, Caine E, Silenzio V and De Choudhury M. A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media. Proceedings of the Conference on Fairness, Accountability, and Transparency. (79-88).

    https://doi.org/10.1145/3287560.3287587

  • Trübner M and Mühlichen A. (2019). Big Data. Handbuch Methoden der empirischen Sozialforschung. 10.1007/978-3-658-21308-4_9. (143-158).

    http://link.springer.com/10.1007/978-3-658-21308-4_9

  • Martinez-Martin N, Insel T, Dagum P, Greely H and Cho M. (2018). Data mining for health: staking out the ethical territory of digital phenotyping. npj Digital Medicine. 10.1038/s41746-018-0075-8. 1:1.

    https://www.nature.com/articles/s41746-018-0075-8

  • Ricard B, Marsch L, Crosier B and Hassanpour S. (2018). Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram. Journal of Medical Internet Research. 10.2196/11817. 20:12. (e11817).

    http://www.jmir.org/2018/12/e11817/

  • Golestani A, Masli M, Shami N, Jones J, Menon A and Mondal J. (2018). Real-Time Prediction of Employee Engagement Using Social Media and Text Mining 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). 10.1109/ICMLA.2018.00225. 978-1-5386-6805-4. (1383-1387).

    https://ieeexplore.ieee.org/document/8614250/

  • Teague S and Shatte A. (2018). Exploring the Transition to Fatherhood: Feasibility Study Using Social Media and Machine Learning. JMIR Pediatrics and Parenting. 10.2196/12371. 1:2. (e12371).

    http://pediatrics.jmir.org/2018/2/e12371/

  • Feuston J and Piper A. (2018). Beyond the Coded Gaze. Proceedings of the ACM on Human-Computer Interaction. 2:CSCW. (1-21). Online publication date: 1-Nov-2018.

    https://doi.org/10.1145/3274320

  • Andalibi N, Morris M and Forte A. (2018). Testing Waters, Sending Clues. Proceedings of the ACM on Human-Computer Interaction. 2:CSCW. (1-23). Online publication date: 1-Nov-2018.

    https://doi.org/10.1145/3274288

  • Eichstaedt J, Smith R, Merchant R, Ungar L, Crutchley P, Preoţiuc-Pietro D, Asch D and Schwartz H. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences. 10.1073/pnas.1802331115. 115:44. (11203-11208). Online publication date: 30-Oct-2018.

    https://pnas.org/doi/full/10.1073/pnas.1802331115

  • Dunn A, Mandl K and Coiera E. (2018). Social media interventions for precision public health: promises and risks. npj Digital Medicine. 10.1038/s41746-018-0054-0. 1:1.

    https://www.nature.com/articles/s41746-018-0054-0

  • De Choudhury M and Kiciman E. (2018). Integrating Artificial and Human Intelligence in Complex, Sensitive Problem Domains. AI Magazine. 39:3. (69-80). Online publication date: 1-Sep-2018.

    https://doi.org/10.1609/aimag.v39i3.2815

  • Archer C and Kao K. (2018). Mother, baby and Facebook makes three: does social media provide social support for new mothers?. Media International Australia. 10.1177/1329878X18783016. 168:1. (122-139). Online publication date: 1-Aug-2018.

    https://journals.sagepub.com/doi/10.1177/1329878X18783016

  • Sonne J and Erickson I. The Expression of Emotions on Instagram. Proceedings of the 9th International Conference on Social Media and Society. (380-384).

    https://doi.org/10.1145/3217804.3217949

  • Garcia-Mancilla J, Ramirez-Marquez J, Lipizzi C, Vesonder G and Gonzalez V. (2018). Characterizing negative sentiments in at-risk populations via crowd computing: a computational social science approach. International Journal of Data Science and Analytics. 10.1007/s41060-018-0135-9.

    http://link.springer.com/10.1007/s41060-018-0135-9

  • Wongkoblap A, Vadillo M and Curcin V. (2018). A Multilevel Predictive Model for Detecting Social Network Users with Depression 2018 IEEE International Conference on Healthcare Informatics (ICHI). 10.1109/ICHI.2018.00022. 978-1-5386-5377-7. (130-135).

    https://ieeexplore.ieee.org/document/8419355/

  • Coşkun M and Ozturan M. (2018). #europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study). Information. 10.3390/info9050102. 9:5. (102).

    https://www.mdpi.com/2078-2489/9/5/102

  • Michie L, Balaam M, McCarthy J, Osadchiy T and Morrissey K. From Her Story, to Our Story. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. (1-15).

    https://doi.org/10.1145/3173574.3173931

  • Research Methodology. Assessing Social Support and Stress in Autism-Focused Virtual Communities. 10.4018/978-1-5225-4020-5.ch005. (46-64).

    http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4020-5.ch005

  • Social Support and Stress. Assessing Social Support and Stress in Autism-Focused Virtual Communities. 10.4018/978-1-5225-4020-5.ch002. (13-25).

    http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4020-5.ch002

  • Boll S, Meyer J and O'Connor N. Health Media: From Multimedia Signals to Personal Health Insights. IEEE MultiMedia. 10.1109/MMUL.2018.011921235. 25:1. (51-60).

    https://ieeexplore.ieee.org/document/8337830/

  • Gaurav K, Sinha A, Singh J and Kumar P. (2018). Facebook Like: Past, Present and Future. Data Engineering and Intelligent Computing. 10.1007/978-981-10-3223-3_59. (617-625).

    http://link.springer.com/10.1007/978-981-10-3223-3_59

  • Yusof N, Lin C and Guerin F. (2018). Assessing the Effectiveness of Affective Lexicons for Depression Classification. Natural Language Processing and Information Systems. 10.1007/978-3-319-91947-8_7. (65-69).

    https://link.springer.com/10.1007/978-3-319-91947-8_7

  • Ramírez-Cifuentes D, Mayans M and Freire A. (2018). Early Risk Detection of Anorexia on Social Media. Internet Science. 10.1007/978-3-030-01437-7_1. (3-14).

    http://link.springer.com/10.1007/978-3-030-01437-7_1

  • Li Y, Mihalcea R and Wilson S. (2018). Text-Based Detection and Understanding of Changes in Mental Health. Social Informatics. 10.1007/978-3-030-01159-8_17. (176-188).

    http://link.springer.com/10.1007/978-3-030-01159-8_17

  • Gui X, Chen Y, Kou Y, Pine K and Chen Y. (2017). Investigating Support Seeking from Peers for Pregnancy in Online Health Communities. Proceedings of the ACM on Human-Computer Interaction. 1:CSCW. (1-19). Online publication date: 6-Dec-2017.

    https://doi.org/10.1145/3134685

  • Reece A and Danforth C. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science. 10.1140/epjds/s13688-017-0110-z. 6:1. Online publication date: 1-Dec-2017.

    http://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0110-z

  • Saha A and Das M. (2017). Impact of social networking sites on post-partum depression in women: An analysis in the context of Bangladesh 2017 20th International Conference of Computer and Information Technology (ICCIT). 10.1109/ICCITECHN.2017.8281831. 978-1-5386-1150-0. (1-6).

    http://ieeexplore.ieee.org/document/8281831/

  • Messias J, Diniz J, Soares E, Ferreira M, Araújo M, Bastos L, Miranda M and Benevenuto F. (2017). An evaluation of sentiment analysis for mobile devices. Social Network Analysis and Mining. 10.1007/s13278-017-0437-2. 7:1. Online publication date: 1-Dec-2017.

    http://link.springer.com/10.1007/s13278-017-0437-2

  • Devineni P, Koutra D, Faloutsos M and Faloutsos C. (2017). Facebook wall posts: a model of user behaviors. Social Network Analysis and Mining. 10.1007/s13278-017-0422-9. 7:1. Online publication date: 1-Dec-2017.

    http://link.springer.com/10.1007/s13278-017-0422-9

  • Reece A, Reagan A, Lix K, Dodds P, Danforth C and Langer E. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific Reports. 10.1038/s41598-017-12961-9. 7:1.

    https://www.nature.com/articles/s41598-017-12961-9

  • DeMasi O, Kording K, Recht B and Jan Y. (2017). Meaningless comparisons lead to false optimism in medical machine learning. PLOS ONE. 10.1371/journal.pone.0184604. 12:9. (e0184604).

    https://dx.plos.org/10.1371/journal.pone.0184604

  • Paul M and Dredze M. (2017). Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services. 10.2200/S00791ED1V01Y201707ICR060. 9:5. (1-183). Online publication date: 31-Aug-2017.

    http://www.morganclaypool.com/doi/10.2200/S00791ED1V01Y201707ICR060

  • Devineni P, Papalexakis E, Koutra D, Doğruöz A and Faloutsos M. One Size Does Not Fit All. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. (331-340).

    https://doi.org/10.1145/3110025.3110050

  • Ting I and Kao H. Spatial Analysis of Social Volume of Crime Issues Social Media Data. Proceedings of the 4th Multidisciplinary International Social Networks Conference. (1-7).

    https://doi.org/10.1145/3092090.3092102

  • Natarajan S, Prabhakar A, Ramanan N, Baglione A, Connelly K and Siek K. Boosting for postpartum depression prediction. Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies. (232-240).

    https://doi.org/10.1109/CHASE.2017.82

  • Tamersoy A, Chau D and De Choudhury M. Analysis of Smoking and Drinking Relapse in an Online Community. Proceedings of the 2017 International Conference on Digital Health. (33-42).

    https://doi.org/10.1145/3079452.3079463

  • Wongkoblap A, Vadillo M and Curcin V. (2017). Researching Mental Health Disorders in the Era of Social Media: Systematic Review. Journal of Medical Internet Research. 10.2196/jmir.7215. 19:6. (e228).

    http://www.jmir.org/2017/6/e228/

  • Peng Z, Hu Q and Dang J. (2017). Multi-kernel SVM based depression recognition using social media data. International Journal of Machine Learning and Cybernetics. 10.1007/s13042-017-0697-1.

    http://link.springer.com/10.1007/s13042-017-0697-1

  • Straton N, Mukkamala R and Vatrapu R. (2017). Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning 2017 IEEE International Congress on Big Data (BigData Congress). 10.1109/BigDataCongress.2017.21. 978-1-5386-1996-4. (89-96).

    http://ieeexplore.ieee.org/document/8029313/

  • Hautasaari A, Yamashita N and Kudo T. Role of CMC in Emotional Support for Depressed Foreign Students in Japan. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. (2614-2621).

    https://doi.org/10.1145/3027063.3053197

  • Manikonda L and De Choudhury M. Modeling and Understanding Visual Attributes of Mental Health Disclosures in Social Media. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. (170-181).

    https://doi.org/10.1145/3025453.3025932

  • MacLeod H, Bastin G, Liu L, Siek K and Connelly K. "Be Grateful You Don't Have a Real Disease". Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. (1660-1673).

    https://doi.org/10.1145/3025453.3025796

  • Park A and Conway M. (2017). Longitudinal Changes in Psychological States in Online Health Community Members: Understanding the Long-Term Effects of Participating in an Online Depression Community. Journal of Medical Internet Research. 10.2196/jmir.6826. 19:3. (e71).

    http://www.jmir.org/2017/3/e71/

  • Liu J, Weitzman E and Chunara R. Assessing Behavior Stage Progression From Social Media Data. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. (1320-1333).

    https://doi.org/10.1145/2998181.2998336

  • Sachdeva N and Kumaraguru P. Call for Service. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. (336-352).

    https://doi.org/10.1145/2998181.2998292

  • Andalibi N, Ozturk P and Forte A. Sensitive Self-disclosures, Responses, and Social Support on Instagram. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. (1485-1500).

    https://doi.org/10.1145/2998181.2998243

  • De Choudhury M, Sharma S, Logar T, Eekhout W and Nielsen R. Gender and Cross-Cultural Differences in Social Media Disclosures of Mental Illness. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. (353-369).

    https://doi.org/10.1145/2998181.2998220

  • De Choudhury M, Kumar M and Weber I. Computational Approaches Toward Integrating Quantified Self Sensing and Social Media. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. (1334-1349).

    https://doi.org/10.1145/2998181.2998219

  • Wang T, Brede M, Ianni A and Mentzakis E. Detecting and Characterizing Eating-Disorder Communities on Social Media. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. (91-100).

    https://doi.org/10.1145/3018661.3018706

  • Lim S, Tucker C and Kumara S. (2017). An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of Biomedical Informatics. 10.1016/j.jbi.2016.12.007. 66. (82-94). Online publication date: 1-Feb-2017.

    https://linkinghub.elsevier.com/retrieve/pii/S1532046416301812

  • Rabbany R, Eswaran D, Dubrawski A and Faloutsos C. (2017). Beyond Assortativity: Proclivity Index for Attributed Networks (ProNe). Advances in Knowledge Discovery and Data Mining. 10.1007/978-3-319-57454-7_18. (225-237).

    http://link.springer.com/10.1007/978-3-319-57454-7_18

  • Saha A and Agarwal N. (2016). Modeling social support in autism community on social media. Network Modeling Analysis in Health Informatics and Bioinformatics. 10.1007/s13721-016-0115-8. 5:1. Online publication date: 1-Dec-2016.

    http://link.springer.com/10.1007/s13721-016-0115-8

  • Seabrook E, Kern M and Rickard N. (2016). Social Networking Sites, Depression, and Anxiety: A Systematic Review. JMIR Mental Health. 10.2196/mental.5842. 3:4. (e50).

    http://mental.jmir.org/2016/4/e50/

  • Muller M, Guha S, Baumer E, Mimno D and Shami N. Machine Learning and Grounded Theory Method. Proceedings of the 2016 ACM International Conference on Supporting Group Work. (3-8).

    https://doi.org/10.1145/2957276.2957280

  • Zhu C, Li B, Li A and Zhu T. (2016). Predicting Depression from Internet Behaviors by Time-Frequency Features 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). 10.1109/WI.2016.0060. 978-1-5090-4470-2. (383-390).

    http://ieeexplore.ieee.org/document/7817077/

  • Lee J, Efstratiou C and Bai L. OSN mood tracking. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. (1171-1179).

    https://doi.org/10.1145/2968219.2968304

  • Lee S, Kim I, Yoo J, Park S, Jeong B and Cha M. (2016). Insights from an expressive writing intervention on Facebook to help alleviate depressive symptoms. Computers in Human Behavior. 62:C. (613-619). Online publication date: 1-Sep-2016.

    https://doi.org/10.1016/j.chb.2016.04.034

  • Messias J, Diniz J, Soares E, Ferreira M, Araujo M, Bastos L, Miranda M and Benevenuto F. Towards sentiment analysis for mobile devices. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. (1390-1391).

    /doi/10.5555/3192424.3192687

  • Messias J, Diniz J, Soares E, Ferreira M, Araujo M, Bastos L, Miranda M and Benevenuto F. (2016). Towards sentiment analysis for mobile devices 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 10.1109/ASONAM.2016.7752426. 978-1-5090-2846-7. (1390-1391).

    http://ieeexplore.ieee.org/document/7752426/

  • Tai C, Tan Z and Chang Y. Systematical Approach for Detecting the Intention and Intensity of Feelings on Social Network. IEEE Journal of Biomedical and Health Informatics. 10.1109/JBHI.2016.2535721. 20:4. (987-995).

    http://ieeexplore.ieee.org/document/7421935/

  • Spiliotopoulos D, Antonakaki D, Ioannidis S and Fragopoulou P. Motivation Effect of Social Media Posts about Well-being and Healthy Living. Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments. (1-4).

    https://doi.org/10.1145/2910674.2910688

  • Chancellor S, Mitra T and De Choudhury M. Recovery Amid Pro-Anorexia. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. (2111-2123).

    https://doi.org/10.1145/2858036.2858246

  • De Choudhury M, Kiciman E, Dredze M, Coppersmith G and Kumar M. Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. (2098-2110).

    https://doi.org/10.1145/2858036.2858207

  • Andalibi N, Haimson O, De Choudhury M and Forte A. Understanding Social Media Disclosures of Sexual Abuse Through the Lenses of Support Seeking and Anonymity. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. (3906-3918).

    https://doi.org/10.1145/2858036.2858096

  • Chancellor S, Lin Z, Goodman E, Zerwas S and De Choudhury M. Quantifying and Predicting Mental Illness Severity in Online Pro-Eating Disorder Communities. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. (1171-1184).

    https://doi.org/10.1145/2818048.2819973

  • De Choudhury M, Sharma S and Kiciman E. Characterizing Dietary Choices, Nutrition, and Language in Food Deserts via Social Media. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. (1157-1170).

    https://doi.org/10.1145/2818048.2819956

  • Li T, Mueen A, Faloutsos M and Hang H. (2016). Comment-Profiler: Detecting Trends and Parasitic Behaviors in Online Comments. Social Informatics. 10.1007/978-3-319-47880-7_5. (75-91).

    https://link.springer.com/10.1007/978-3-319-47880-7_5

  • Tai C, Tan Z, Lin Y and Chang Y. (2015). Mental Disorder Detection and Measurement Using Latent Dirichlet Allocation and SentiWordNet 2015 IEEE International Conference on Systems, Man and Cybernetics (SMC). 10.1109/SMC.2015.217. 978-1-4799-8697-2. (1215-1220).

    http://ieeexplore.ieee.org/document/7379349/

  • Devineni P, Koutra D, Faloutsos M and Faloutsos C. If walls could talk. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. (367-374).

    https://doi.org/10.1145/2808797.2808880

  • Tamersoy A, De Choudhury M and Chau D. Characterizing Smoking and Drinking Abstinence from Social Media. Proceedings of the 26th ACM Conference on Hypertext & Social Media. (139-148).

    https://doi.org/10.1145/2700171.2791247

  • Kumar M, Dredze M, Coppersmith G and De Choudhury M. Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides. Proceedings of the 26th ACM Conference on Hypertext & Social Media. (85-94).

    https://doi.org/10.1145/2700171.2791026

  • Burnap P, Colombo W and Scourfield J. Machine Classification and Analysis of Suicide-Related Communication on Twitter. Proceedings of the 26th ACM Conference on Hypertext & Social Media. (75-84).

    https://doi.org/10.1145/2700171.2791023

  • Doran D, Severin K, Gokhale S and Dagnino A. Social media enabled human sensing for smart cities. AI Communications. 10.3233/AIC-150683. 29:1. (57-75).

    https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/AIC-150683

  • Bjork-James S. (2015). Feminist Ethnography in Cyberspace: Imagining Families in the Cloud. Sex Roles. 10.1007/s11199-015-0507-8. 73:3-4. (113-124). Online publication date: 1-Aug-2015.

    http://link.springer.com/10.1007/s11199-015-0507-8

  • De Choudhury M. Anorexia on Tumblr. Proceedings of the 5th International Conference on Digital Health 2015. (43-50).

    https://doi.org/10.1145/2750511.2750515

  • Pavalanathan U and De Choudhury M. Identity Management and Mental Health Discourse in Social Media. Proceedings of the 24th International Conference on World Wide Web. (315-321).

    https://doi.org/10.1145/2740908.2743049

  • Shami N, Muller M, Pal A, Masli M and Geyer W. Inferring Employee Engagement from Social Media. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. (3999-4008).

    https://doi.org/10.1145/2702123.2702445

  • Fourney A, White R and Horvitz E. Exploring Time-Dependent Concerns about Pregnancy and Childbirth from Search Logs. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. (737-746).

    https://doi.org/10.1145/2702123.2702427

  • Tsugawa S, Kikuchi Y, Kishino F, Nakajima K, Itoh Y and Ohsaki H. Recognizing Depression from Twitter Activity. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. (3187-3196).

    https://doi.org/10.1145/2702123.2702280

  • Abdullah S, Murnane E, Costa J and Choudhury T. Collective Smile. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. (361-374).

    https://doi.org/10.1145/2675133.2675186

  • MacLean D, Gupta S, Lembke A, Manning C and Heer J. Forum77. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. (1511-1526).

    https://doi.org/10.1145/2675133.2675146

  • Park S, Kim I, Lee S, Yoo J, Jeong B and Cha M. Manifestation of Depression and Loneliness on Social Networks. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. (557-570).

    https://doi.org/10.1145/2675133.2675139

  • Valdez R, Guterbock T, Thompson M, Reilly J, Menefee H, Bennici M, Williams I and Rexrode D. (2014). Beyond Traditional Advertisements: Leveraging Facebook’s Social Structures for Research Recruitment. Journal of Medical Internet Research. 10.2196/jmir.3786. 16:10. (e243).

    http://www.jmir.org/2014/10/e243/

  • De Choudhury M, Monroy-Hernández A and Mark G. "Narco" emotions. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. (3563-3572).

    https://doi.org/10.1145/2556288.2557197

  • De Choudhury M. Can social media help us reason about mental health?. Proceedings of the 23rd International Conference on World Wide Web. (1243-1244).

    https://doi.org/10.1145/2567948.2580064

  • Kumar A, Sharma A and Arora A. Anxious Depression Prediction in Real-time Social Data. SSRN Electronic Journal. 10.2139/ssrn.3383359.

    https://www.ssrn.com/abstract=3383359