The DOI matches its Crossref record; 41 of the 44 references it cites were found in a registry or an index.
To stay or leave?: the relationship of emotional and informational support to commitment in online health support groups
Abstract
Today many people with serious diseases use online support groups to seek social support. For these groups to be sustained and effective, member retention and commitment is important. Our study examined how different types and amounts of social support in an online cancer support group are associated with participants' length of membership. We first built machine learning models to automatically identify the extent to which messages contained emotional and informational support. Agreement with human judges was high (r > 0.76). We then used these models to measure the support exchanged in 1.5 million messages. Finally, we applied quantitative event history analysis to assess how exposure to emotional and informational support predicted group members' length of subsequent participation. The results demonstrated that the more emotional support members were exposed to, the lower the risk of dropout. In contrast, informational support did not have the same strong effects on commitment. We speculate that emotional support enhanced members' relationships with one another or the group as a whole, whereas informational support satisfied members' short-term information needs.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“These two forms of support have been found to be most prevalent and effective in several studies of online support and social media [38,62,66,67].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [67]
In the citing paper
“Social media enables individuals to self-disclose and express their emotional and informational needs [67].”
From Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media (2020) · cites this paper as [67]
Citations
Cites
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence
Singer, J. D., & Willett, J. B. (2003) · doi:10.1093/acprof:oso/9780195152968.001.0001 · Cited by 10,647
Witten, I. H. (2008) · Cited by 9,143
Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · doi:10.3115/1220575.1220619 · Cited by 3,388
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
Snow, R., O'Connor, B., Jurafsky, D., & Ng, A. Y. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613751 · Cited by 1,887
Eysenbach, G., Powell, J., Englesakis, M., Rizo, C., & Stern, A. (2004) · BMJ · doi:10.1136/bmj.328.7449.1166 · Cited by 1,268
Social media use in the United States: implications for health communication
Chou, W.-Y. S., Hunt, Y. M., Beckjord, E. B., Moser, R. P., & Hesse, B. W. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1249 · Cited by 1,082
SOCIALIZATION TACTICS: LONGITUDINAL EFFECTS ON NEWCOMER ADJUSTMENT.
Ashforth, B. K., & Saks, A. M. (1996) · Academy of Management Journal · doi:10.2307/256634 · Cited by 995
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
Toutanova, K., & Manning, C. D. (2000) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1117794.1117802 · Cited by 965
Cited by
Sorted by citations
A deep learning model for detecting mental illness from user content on social media
Kim, J., Lee, J., Park, E., & Han, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-68764-y · Cited by 325
Social Barriers Faced by Newcomers Placing Their First Contribution in Open Source Software Projects
Steinmacher, I., Conte, T. U., Gerosa, M. A., & Redmiles, D. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675215 · Cited by 305
Sentiment Analysis in MOOC Discussion Forums: What does it tell us?
Wen, M., Yang, D., & Rosé, C. P. (2014) · Cited by 285
Six ‘biases’ against patients and carers in evidence-based medicine
Greenhalgh, T., Snow, R., Ryan, S., Rees, S., & Salisbury, H. (2015) · BMC Medicine · doi:10.1186/s12916-015-0437-x · Cited by 283
A systematic literature review on the barriers faced by newcomers to open source software projects
Steinmacher, I., Silva, M. A. G., Gerosa, M. A., & Redmiles, D. (2014) · Information and Software Technology · doi:10.1016/j.infsof.2014.11.001 · Cited by 239
The Language of Social Support in Social Media and Its Effect on Suicidal Ideation Risk
De Choudhury, M., & Kiciman, E. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14891 · Cited by 202
Social Support, Reciprocity, and Anonymity in Responses to Sexual Abuse Disclosures on Social Media
Andalibi, N., Haimson, O. L., Choudhury, M. D., & Forte, A. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3234942 · Cited by 200
Wang, X., Zhao, K., & Street, N. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6834 · Cited by 182
Wang, Y., Kraut, R. E., & Levine, J. M. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3558 · Cited by 174
Self-citationPsychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media
Saha, K., Torous, J., Caine, E. D., & De Choudhury, M. (2020) · Journal of Medical Internet Research · doi:10.2196/22600 · Cited by 166
A linguistic signaling model of social support exchange in online health communities
Chen, L., Baird, A. M., & Straub, D. W. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113233 · Cited by 175
Mental Health Support and its Relationship to Linguistic Accommodation in Online Communities
Sharma, E., & De Choudhury, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174215 · Cited by 156
Chen, L., Baird, A. M., & Straub, D. W. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2018.1550547 · Cited by 149
Lin, T., Hsu, J. S., Cheng, H., & Chiu, C. (2015) · Information & Management · doi:10.1016/j.im.2015.01.003 · Cited by 142
Physician voice characteristics and patient satisfaction in online health consultation
Liu, S., Zhang, M., Gao, B., & Jiang, G. (2019) · Information & Management · doi:10.1016/j.im.2019.103233 · Cited by 144
Social factors that contribute to attrition in MOOCs
Rosé, C. P., Carlson, R., Yang, D., Wen, M., Resnick, L. Β., Goldman, P., & Sherer, J. Z. (2014) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2556325.2567879 · Cited by 140
Xing, W., & Gao, F. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.08.010 · Cited by 141
Zheng, J., Xing, W., & Zhu, G. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.03.005 · Cited by 144
Amazon Mechanical Turk: A Research Tool for Organizations and Information Systems Scholars
Crowston, K. (2012) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-3-642-35142-6_14 · Cited by 127
Influence of Rapport and Social Presence with an AI Psychotherapy Chatbot on Users’ Self-Disclosure
Lee, J., Lee, D., & Lee, J.-G. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2146227 · Cited by 130
Health Vlogs as Social Support for Chronic Illness Management
Huh, J., Liu, L. S., Neogi, T., Inkpen, K. M., & Pratt, W. (2014) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2630067 · Cited by 125
Social interaction and reflection for behaviour change
Ploderer, B., Reitberger, W., B, H. O.-K. A., & van Gemert-Pijnen, J. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0779-y · Cited by 119
Exploring the Effect of Confusion in Discussion Forums of Massive Open Online Courses
Yang, D., Wen, M., Howley, I. K., Kraut, R. E., & Rosé, C. P. (2015) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2724660.2724677 · Cited by 129
Self-citationXing, W., Tang, H., & Pei, B. (2019) · The Internet and Higher Education · doi:10.1016/j.iheduc.2019.100690 · Cited by 123
Rennick‐Egglestone, S., Morgan, K., Llewellyn‐Beardsley, J., Ramsay, A., McGranahan, R., Gillard, S., Hui, A., Ng, F., Schneider, J. M., Booth, S., et al. (2019) · The Canadian Journal of Psychiatry · doi:10.1177/0706743719846108 · Cited by 128
Linguistic Reflections of Student Engagement in Massive Open Online Courses
Wen, M., Yang, D., & Rosé, C. P. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14512 · Cited by 107
Chancellor, S., Hu, A., & Choudhury, M. D. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174240 · Cited by 118
Zhang, S., Bantum, E. O., Owen, J. E., Henry, S. B., & Elhadad, N. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw093 · Cited by 107
Mitra, T., Hutto, C. J., & Gilbert, E. (2015) · Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702553 · Cited by 102
Park, A., & Conway, M. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6826 · Cited by 104
Park, A., & Conway, M. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8219 · Cited by 96
Seekers, Providers, Welcomers, and Storytellers
Yang, D., Kraut, R. E., Smith, T. G., Mayfield, E., & Jurafsky, D. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300574 · Cited by 94
Self-citationSocial Support and User Engagement in Online Health Communities
Wang, X., Zhao, K., & Street, W. N. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08416-9_10 · Cited by 88
What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer
Nzali, M. D. T., Bringay, S., Lavergne, C., Mollévi, C., & Opitz, T. (2017) · JMIR Medical Informatics · doi:10.2196/medinform.7779 · Cited by 89
The impact of membership overlap on the survival of online communities
Zhu, H., Kraut, R. E., & Kittur, A. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557213 · Cited by 78
Self-citationSupport matching and satisfaction in an online breast cancer support community
Vlahovic, T. A., Wang, Y., Kraut, R. E., & Levine, J. M. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557108 · Cited by 77
Self-citationClinical Questions in Online Health Communities
Huh, J. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675259 · Cited by 73
Commitment of Newcomers and Old-timers to Online Health Support Communities
Yang, D., Kraut, R. E., & Levine, J. M. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3026008 · Cited by 74
Self-citationExamining Hacker Participation Length in Cybercriminal Internet-Relay-Chat Communities
Benjamin, V., Zhang, B., Nunamaker, J. F., & Chen, H. (2016) · Journal of Management Information Systems · doi:10.1080/07421222.2016.1205918 · Cited by 73
Peng, Z., Guo, Q., Tsang, K. W., & Ma, X. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376695 · Cited by 81
Responding to Sensitive Disclosures on Social Media
Andalibi, N., & Forte, A. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3241044 · Cited by 66
What is Spiritual Support and How Might It Impact the Design of Online Communities?
Smith, C. E., Kaur, A., Gach, K. Z., Terveen, L., Kreitzer, M. J., & O’Conner-Von, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449117 · Cited by 73
Wang, L., Wang, D., Tian, F., Peng, Z., Fan, X., Zhang, Z., Yu, M., Ma, X., & Wang, H. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449083 · Cited by 96
Tixier, M., & Lewkowicz, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858477 · Cited by 65
Emotions under Discussion: Gender, Status and Communication in Online Collaboration
Iosub, D., Laniado, D., Castillo, C., Morell, M. F., & Kaltenbrunner, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104880 · Cited by 72
Ma, H., Smith, C. E., Lu, H., Narayanan, S., Giaquinto, R. A., Evans, R., Hanson, L., & Yarosh, S. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134708 · Cited by 64
Does negatively toned language use on social media lead to attitude polarization?
Buder, J., Rabl, L., Feiks, M., Badermann, M., & Zurstiege, G. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106663 · Cited by 73
Zhang, S., Grave, É., Sklar, E. I., & Elhadad, N. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.03.012 · Cited by 63
Online Support Groups for Depression in China: Culturally Shaped Interactions and Motivations
Zhang, R., Eschler, J., & Reddy, M. C. (2018) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-018-9322-4 · Cited by 63
It’s going to be okay: Measuring Access to Support in Online Communities
Wang, Z., & Jurgens, D. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1004 · Cited by 65
Liu, Y., Kornfield, R., Shaw, B. R., Shah, D. V., Mctavish, F. M., & Gustafson, D. H. (2019) · Patient Education and Counseling · doi:10.1016/j.pec.2019.12.015 · Cited by 67
Understanding the sustained use of online health communities from a self‐determination perspective
Zhang, Y. (2015) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23560 · Cited by 57
Li, G., Zhou, X., Lu, T., Yang, J., & Gu, N. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819994 · Cited by 55
Understanding Moderation in Online Mental Health Communities
Saha, K., Ernala, S. K., Dutta, S., Sharma, E., & Choudhury, M. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49576-3_7 · Cited by 49
Identifying Emotional and Informational Support in Online Health Communities
Biyani, P., Caragea, C., Mitra, P., & Yen, J. (2014) · University of North Texas Digital Library (University of North Texas) · Cited by 53
Chen, X., Faviez, C., Schück, S., Louët, A. L., Texier, N., Badisse Dahamna, A., Huot, C., Foulquié, P., Pereira, S., Leroux, V., et al. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00541 · Cited by 52
VisOHC: Designing Visual Analytics for Online Health Communities
Kwon, B. C., Kim, S.-H., Lee, S., Choo, J., Huh, J., & Yi, J. S. (2015) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2467555 · Cited by 47
Xing, W., Goggins, S. P., & Introne, J. E. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.04.042 · Cited by 48
Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text
Park, A., Hartzler, A. L., Huh, J., McDonald, D. W., & Pratt, W. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4612 · Cited by 48
On participation in group chats on Twitter
Budak, C., & Agrawal, R. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488404 · Cited by 47
Myneni, S., Cobb, N. K., & Cohen, T. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4671 · Cited by 51
Ruthven, I. G., Buchanan, S., & Jardine, C. (2018) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24037 · Cited by 47
Role of empowerment and sense of community on online social health support group
Sharma, S., & Khadka, A. (2019) · Information Technology and People · doi:10.1108/itp-09-2018-0410 · Cited by 48
Zhou, T., Wang, Y., Yan, L., & Tan, Y. (2023) · Information Systems Research · doi:10.1287/isre.2022.1191 · Cited by 52
Self-citationShifting dynamics or breaking sacred traditions?
Yarosh, S. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2466468 · Cited by 46
Leveraging cues from person-generated health data for peer matching in online communities
Hartzler, A. L., Taylor, M. N., Park, A., Griffiths, T., Backonja, U., McDonald, D. W., Wahbeh, S., Brown, C. S., & Pratt, W. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv175 · Cited by 46
Exploring the Effect of Student Confusion in Massive Open Online Courses
Yang, D., Kraut, R. E., & Rosé, C. P. (2016) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554605 · Cited by 44
Self-citationBeyond reciprocity: The bystander effect of knowledge response in online knowledge communities
Yan, B., & Jian, L. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.06.040 · Cited by 45
Overcoming Social Barriers When Contributing to Open Source Software Projects
Steinmacher, I., Gerosa, M. A., Conte, T. U., & Redmiles, D. (2018) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-018-9335-z · Cited by 54
Yang, D., Wen, M., Kumar, A., Xing, E. P., & Rosé, C. P. (2014) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v15i5.1853 · Cited by 43
"On Finsta, I can say 'Hail Satan'": Being Authentic but Disagreeable on Instagram
Taber, L., & Whittaker, S. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376182 · Cited by 51
Wang, X., High, A. C., Wang, X., & Zhao, K. (2020) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24436 · Cited by 42
The Long Road Ahead: Ongoing Challenges in Contributing to Large OSS Organizations and What to Do
Guizani, M., Chatterjee, A., Trinkenreich, B., May, M. E., Noa-Guevara, G. J., Russell, L. J., Cuevas Zambrano, G. G., Izquierdo-Cortazar, D., Steinmacher, I., Gerosa, M. A., et al. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479551 · Cited by 39
Bateman, D. R., Brady, E. L., Wilkerson, D., Yi, E.-H. G., Karanam, Y., & Callahan, C. M. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6904 · Cited by 47
Canhilal, K., Canboy, B., & Bakici, T. Y. (2020) · The International Journal of Human Resource Management · doi:10.1080/09585192.2020.1752283 · Cited by 49
A comparison of social, learning, and financial strategies on crowd engagement and output quality
Yu, L., André, P., Kittur, A., & Kraut, R. E. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531729 · Cited by 40
Self-citationFine-Grained Emotion Detection in Health-Related Online Posts
Khanpour, H., & Caragea, C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1147 · Cited by 39
A competing risks model based on latent Dirichlet Allocation for predicting churn reasons
Slof, D., Frăsincar, F., & Matsiiako, V. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113541 · Cited by 40
Nakikj, D., & Mamykina, L. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998339 · Cited by 39
"I think we know more than our doctors"
Jacobs, M. L., Gheihman, G., Gajos, K. Z., & Gupta, A. S. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359261 · Cited by 36
CancerEmo: A Dataset for Fine-Grained Emotion Detection
Sosea, T., & Caragea, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.715 · Cited by 38
Kim, S., Cha, J., Kim, D., & Park, E. (2023) · Journal of Medical Internet Research · doi:10.2196/49074 · Cited by 39
In-group Questions and Out-group Answers
Hong, H., Gilbert, É., Abowd, G. D., & Arriaga, R. I. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702402 · Cited by 34
Hugging with a Shower Curtain: Older Adults' Social Support Realities During the COVID-19 Pandemic
Nurain, N., Chung, C.-F., Caldeira, C. M., & Connelly, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479607 · Cited by 38
Meyerhoff, J., Kornfield, R., Mohr, D. C., & Reddy, M. C. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555203 · Cited by 32
Liu, Y., Kornfield, R., Shaw, B. R., Shah, D. V., Mctavish, F. M., & Gustafson, D. H. (2017) · Digital Health · doi:10.1177/2055207617704274 · Cited by 31
Patterns of Patient and Caregiver Mutual Support Connections in an Online Health Community
Levonian, Z., Dow, M., Erikson, D., Ghosh, S., Hillberg, H. M., Narayanan, S., Terveen, L., & Yarosh, S. (2020) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3434184 · Cited by 41
Xing, W., Popov, V., Zhu, G., Horwitz, P., & McIntyre, C. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.04.022 · Cited by 29
Patterns of support in an online community for smoking cessation
Ploderer, B., Smith, W., Howard, S., Pearce, J. M., & Borland, R. (2013) · International Conference on Communities and Technologies (C&T) · doi:10.1145/2482991.2482992 · Cited by 29
A Sociotechnical Mechanism for Online Support Provision
Introne, J. E., Semaan, B., & Goggins, S. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858582 · Cited by 29
Spotswood, F., Shankar, A., & Piwek, L. (2020) · Sociology of Health & Illness · doi:10.1111/1467-9566.13057 · Cited by 28
Computer-Mediated Sharing Circles for Intersectional Peer Support with Home Care Workers
Poon, A., Luebke, M., Loughman, J., Lee, A., Guerrero, L. R., Sterling, M. R., & Dell, N. L. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579472 · Cited by 30
Wen, M., & Rosé, C. P. (2012) · International ACM SIGGROUP Conference on Supporting Group Work (GROUP) · doi:10.1145/2389176.2389205 · Cited by 28
Elhadad, N., Zhang, S., Driscoll, P., & Brody, S. (2014) · PubMed Central · Cited by 27
Peng, Z., Ma, X., Yang, D., Tsang, K. W., & Guo, Q. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445446 · Cited by 26
We Make Choices We Think are Going to Save Us
Zhang, S., Qiu, L., Chen, F. Y., Zhang, W., Yu, Y. H., & Elhadad, N. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3055134 · Cited by 28
Searching for Extremist Content Online Using the Dark Crawler and Sentiment Analysis
Scrivens, R., Gaudette, T., Davies, G. J., & Frank, R. W. (2019) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1521-613620190000024016 · Cited by 26
Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis
Zhu, J., Li, Z., Zhang, X., Zhang, Z., & Hu, B. (2023) · Journal of Medical Internet Research · doi:10.2196/45777 · Cited by 29
"People Are Either Too Fake or Too Real"
Ma, X., Andalibi, N., Barkhuus, L., & Naaman, M. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025956 · Cited by 26
Does sustained participation in an online health community affect sentiment?
Zhang, S., Bantum, E. O., Owen, J. E., & Elhadad, N. (2014) · PubMed Central · Cited by 25
Metadata
- Authors
- Wang, Y.-C., Kraut, R., & Levine, J. M.
- Affiliations
- Carnegie Mellon University (US)
- University of Pittsburgh (US)
- Venue
- Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
- Publisher
- ACM
- Published
- 2012-02-11
- Pages
- 833-842
- OpenAlex
- W2106212012
- Cited by
- 282
- References
- 44
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 9 · 2023: 18 · 2022: 24 · 2021: 25 · 2020: 28 · 2019: 23 · 2018: 24 · 2017: 31 · 2016: 16 · 2015: 24 · 2014: 22 · 2013: 6 · 2012: 5
- FWCI
- 97.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Computer Supported Cooperative Work (CSCW) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Decision Sciences › Information Systems and Management › Knowledge Management and Sharing
- Keywords
- online support groups, cancer support groups, member retention, dropout, emotional support, information support, social support, message analysis, machine learning classification, event history analysis, serious illness, online communities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review