Abstract
Among the most important reasons why people communicate with each other is to share and support each other through emotional problems, yet most online social systems are uncomfortable or unsafe spaces for this purpose. This has led to the development of online emotional support systems, where users needing to speak to someone can anonymously connect to a crowd of trained listeners for a one-on-one conversation. Toward understanding the qualities of this emerging type of online social system, this article examines the users, conversations, and activities performed across 7 Cups, a massive, vibrant emotional support system with a community of listeners ready to help those with any number of emotional issues. The study makes intriguing insights along the worldwide adoption of the service, the need of its users to seek support from many others, a power-law effect of listener popularity, that users have a penchant to connecting to others along common interests and that a core periphery-like structure emerges among conversation networks, and identifies qualities of the system that drive user engagement and retention. We further study the words and actions of misbehaving users who have been reported on or blocked, and build a machine learning classifier able to anticipate their undesirable actions with reasonable accuracy and very low false positive rate. The qualities recovered gave insight into the user dynamics and communication structure of an online emotional support service, the features that drive engagement, and a means of identifying misbehaving users automatically.



















Similar content being viewed by others
References
Attfield S, Kazai G, Lalmas M, Piwowarski B (2011) Towards a science of user engagement (position paper). In: Proceedings of the WSDM workshop on user modelling for Web applications, pp 9–12
Bar-Lev S (2008) “We are here to give you emotional support”: performing emotions in an online HIV/AIDS support group. Qual Health Res 18(4):509–521
Barak A (2007) Emotional support and suicide prevention through the internet: a field project report. Comput Hum Behav 23(2):971–984
Binik YM, Cantor J, Ochs E, Meana M (1997) From the couch to the keyboard: psychotherapy in cyberspace. In: Kiesler S (ed) Culture of the internet. Psychology Press, pp 71–102
Booth D, Jansen BJ (2010) A review of methodologies for analyzing websites. In: Web technologies: concepts, methodologies, tools, and applications. IGI Global, pp 145–166
Bright JI, Baker KD, Neimeyer RA (1999) Professional and paraprofessional group treatments for depression: a comparison of cognitive-behavioral and mutual support interventions. J Consult Clin Psychol 67(4):491
Calzarossa MC, Tessera D (2015) Modeling and predicting temporal patterns of Web content changes. J Netw Comput Appl 56:115–123
Calzarossa MC, Massari L, Doran D, Yelne S, Trivedi N, Moriarty G (2016) Measuring the users and conversations of a vibrant online emotional support system. In: Proceedings of the IEEE symposium on computers and communication (ISCC), pp 1193–1199
Calzarossa M, Della Vedova M, Massari L, Nebbione G, Tessera D (2019) A methodological approach for time series analysis and forecasting of Web dynamics. LNCS Trans Comput Collect Intell XXXIII 11610:128–143
Chen YR, Chen HH (2015) Opinion spammer detection in web forum. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 759–762
Chou WYS, Prestin A, Lyons C, Wen KY (2013) Web 2.0 for health promotion: reviewing the current evidence. Am J Public Health 103(1):e9–e18
Chu Z, Gianvecchio S, Koehl A, Wang H, Jajodia S (2013) Blog or block: detecting blog bots through behavioral biometrics. Comput Netw 57(3):634–646
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
Davison KP, Pennebaker JW, Dickerson SS (2000) Who talks? The social psychology of illness support groups. Am Psychol 55(2):205
Doran D, Yelne S, Massari L, Calzarossa M, Jackson L, Moriarty G (2015) Stay awhile and listen: user interactions in a crowdsourced platform offering emotional support. In: Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). ACM, pp 667–674
Fakhraei S, Foulds J, Shashanka M, Getoor L (2015) Collective spammer detection in evolving multi-relational social networks. In: Proceedings of the 21st ACM international conference on knowledge discovery and data mining, pp 1769–1778
Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174
Gao H, Hu J, Wilson C, Li Z, Chen Y, Zhao BY (2010) Detecting and characterizing social spam campaigns. In: Proceedings of the 10th ACM SIGCOMM internet measurement conference (IMC), pp 35–47
Grando F, Granville L, Lamb L (2018) Machine learning in network centrality measures: tutorial and outlook. ACM Comput Surv 51(5):102:1–102:32
Han J, Kamber M, Pei J (2006) Data mining: concepts and techniques. Morgan Kaufmann, Los Altos
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, Berlin
Hemmati A, Chung KSK (2014) Associations between personal social network properties and mental health in cancer care. In: Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 828–835
Hu Y (2005) Efficient, high-quality force-directed graph drawing. Math J 10(1):37–71
Hu X, Liu H, Tang J (2014) Online social spammer detection. In: Proceedings of the 28th AAAI conference on artificial intelligence, pp 59–65
Huang MP, Alessi NE (1996) The internet and the future of psychiatry. Am J Psychiatry 153(7):861–869
Ikehara CS, Crosby ME (2005) Assessing cognitive load with physiological sensors. In: Proceedings of the 38th annual Hawaii international conference on system sciences. IEEE
Jacques RD (1996) The nature of engagement and its role in hypermedia evaluation and design. Ph.D. thesis, South Bank University
Kayes I, Iamnitchi A (2017) Privacy and security in online social networks: a survey. Online Soc Netw Media 3–4:1–21
Konradt U, Sulz K (2001) The experience of flow in interacting with a hypermedia learning environment. J Educ Multimed Hypermedia 10(1):69–84
Lalmas M, O’Brien H, Yom-Tov E (2014) Measuring user engagement. Synth Lect Inf Concepts Retr Serv 6(4):1–132
Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data 1(1):2
Li Y, Kim DW, Zhang J, Doran D (2018) Teafilter: detecting suspicious members in an online emotional support service. In: Proceedings EAI international conference on security and privacy in communication networks
Lupien SJ, McEwen BS, Gunnar MR, Heim C (2009) Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nat Rev Neurosci 10(6):434
Malliaros F, Vazirgiannis M (2013) Clustering and community detection in directed networks: a survey. Phys Rep 533(4):95–142
Maloney-Krichmar D, Preece J (2005) A multilevel analysis of sociability, usability, and community dynamics in an online health community. ACM Trans Comput–Hum Interact 12(2):201–232
Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement. ACM, pp 29–42
Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256
Newman MEJ (2010) Networks: an introduction. Oxford University Press, Oxford
Newman MW, Lauterbach D, Munson SA, Resnick P, Morris ME (2011) It’s not that I don’t have problems, I’m just not putting them on Facebook: challenges and opportunities in using online social networks for health. In: Proceedings of the ACM 2011 conference on computer supported cooperative work. ACM, pp 341–350
Ploderer B, Smith W, Howard S, Pearce J, Borland R (2013) Patterns of support in an online community for smoking cessation. In: Proceedings of the 6th international conference on communities and technologies. ACM, pp 26–35
Procidano ME, Heller K (1983) Measures of perceived social support from friends and from family: three validation studies. Am J Community Psychol 11(1):1–24
Rochlen AB, Zack JS, Speyer C (2004) Online therapy: review of relevant definitions, debates, and current empirical support. J Clin Psychol 60(3):269–283
Seah Ml, Cairns P (2008) From immersion to addiction in videogames. In: Proceedings of the British HCI group annual conference on people and computers: culture, creativity, interaction. British Computer Society, pp 55–63
Stanton-Salazar RD, Spina SU (2005) Adolescent peer networks as a context for social and emotional support. Youth Soc 36(4):379–417
Tsai YC, Liu CH (2012) Factors and symptoms associated with work stress and health-promoting lifestyles among hospital staff: a pilot study in Taiwan. BMC Health Serv Res 12(1):199
Wang YC, Kraut R, Levine JM (2012) To stay or leave? The relationship of emotional and informational support to commitment in online health support groups. In: Proceedings of the ACM 2012 conference on computer supported cooperative work. ACM, pp 833–842
Webster J, Ho H (1997) Audience engagement in multimedia presentations. ACM SIGMIS Database 28(2):63–77
White M, Dorman SM (2001) Receiving social support online: implications for health education. Health Educ Res 16(6):693–707
Wu F, Shu J, Huang Y, Yuan Z (2015) Social spammer and spam message co-detection in microblogging with social context regularization. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, pp 1601–1610
Yang J, Leskovec J (2015) Defining and evaluating network communities based on ground-truth. Knowl Inf Syst 42(1):181–213
Yang C, Harkreader RC, Gu G (2011) Die free or live hard? Empirical evaluation and new design for fighting evolving Twitter spammers. In: Proceedings of the international workshop on recent advances in intrusion detection. Springer, Berlin, pp 318–337
Yuen EK, Goetter EM, Herbert JD, Forman EM (2012) Challenges and opportunities in internet-mediated telemental health. Prof Psychol Res Pract 43(1):1–8
Zuckerman E (2003) Finding, evaluating, and incorporating internet self-help resources into psychotherapy practice. J Clin Psychol 59(2):217–225
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Doran, D., Massari, L., Calzarossa, M.C. et al. User interactions and behaviors in a large-scale online emotional support service. Soc. Netw. Anal. Min. 9, 36 (2019). https://doi.org/10.1007/s13278-019-0581-y
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-019-0581-y