User interactions and behaviors in a large-scale online emotional support service

Derek Doran*, Luisa Massari, Maria Carla Calzarossa, Yu Li, Yuan Li, Daniele Tessera, Junjie Zhang

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review


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.
Lingua originaleEnglish
pagine (da-a)1-36
Numero di pagine36
RivistaSocial Network Analysis and Mining
Stato di pubblicazionePubblicato - 2019


  • Behavior prediction
  • Emotional support systems
  • Harassment detection
  • Network analysis
  • Time series analysis and forecasting
  • Workload characterization


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