Discovering habits of effective online support group chatrooms

Abstract

For users of online support groups, prior research has suggested that a positive social environment is a key enabler of coping. Typically, demonstrating such claims about social interaction would be approached through the lens of sentiment analysis. In this work, we argue instead for a multifaceted view of emotional state, which incorporates both a static view of emotion (sentiment) with a dynamic view based on the behaviors present in a text. We codify this dynamic view through data annotations marking information sharing, sentiment, and coping efficacy. Through machine learning analysis of these annotations, we demonstrate that while sentiment predicts a user's stress at the beginning of a chat, dynamic views of efficacy are stronger indicators of stress reduction.

Towards Careful Practices for Automated Linguistic Analysis of Group Learning

“…For example, analysis of task-relevant differences in information sharing practices between military and civilian pairs performing the same task in a lab study (Mayfield, Garbus, Adamson, & Rosé, 2001; as well as conversational strategies associated with stress reduction in online cancer support chats Mayfield, Wen, Rosé, & Golant, 2012). There we have also found that positioning with respect to knowledge transfer is predictive of stress reduction in these chats; however, it does not appear to be directly related to selfefficacy.…”

Towards an Online Empathy Assisted Counselling Web Application

“…A major challenge faced when undertaking chat conversations containing unstructured data, is measuring the tone and therefore empathy inclusivity in general in the chat exchanges bearing in mind that in such a situation, there is total lack of body language, which is an important part in emotional interpretation. Moreover, the transition time, that is the interchange time between the chat parties may be too small to make meaningful interpretation and in this way emotional information may not be fully passed on [6].…”

Affective Behaviour Analysis of On-line User Interactions: Are On-line Support Groups More Therapeutic than Twitter?

“…(Ghosh et al, 2017;). Mayfield et al (2012) applied sentimentanalysis combined with extensive turn-level annotation to investigate stress reduction in on-line support group chat-rooms, showing that sentimentanalysis is a good predictor of entrance stress level. Furthermore, similar to our setting, they applied automatic thread-extraction to determine conversation threads.…”

“…According to Mayfield et al (2012), users with high incoming stress tend to request less information from others, as a percentage of their time, and share much more information, in absolute terms. In addition, high information sharing has been shown to be a good predictor of stress reduction at the end of the chat (Mayfield et al, 2012). Regarding information sharing, we rely on Dialogue Acts (Austin, 1975) to model the speaker's intention in producing an utterance.…”

“…According to Barak et al (2008) OSG effectiveness is hard to assess, while some studies showed OSG's potential to change participants' attitudes, no such effect was observed in other studies (see Related Work Section for more details). Furthermore the scope of previous work on analysis of users' behaviour in OSG has been limited by the fact that they relied on expert annotation of posts and comments (Mayfield et al, 2012).…”