NLG-based moderator response generator to support mental health

M. Sazzad Husain, Rafael A. Calvo, Louise Ellis, Juchen Li, Laura Ospina-Pinillos, Tracey Davenport, Ian Hickie

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


The global need to effectively address mental health problems and wellbeing is well recognised. Today, online systems are increasingly being viewed as an effective solution for their ability to reach broad populations. As online support groups become popular the workload for human moderators increases. Maintaining quality feedback becomes increasingly challenging as the community grows. Tools that can automatically detect mental health problems from social media posts and then generate smart feedback can greatly reduce human overload. In this paper, we present a system for the automation of interventions using Natural Language Generation (NLG) techniques. In particular, we focus on 'depression' and 'anxiety' related interventions. Psychologists evaluated the quality of the systems' interventions and results were compared against human (i.e. moderator) interventions. Results indicate our intervention system still has a long way to go, but is a step in the right direction as a tool to assist human moderators with their service. Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationCHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450331463
Publication statusPublished - 18 Apr 2015
Externally publishedYes
Event33rd Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2015 - Seoul, Korea, Republic of
Duration: 18 Apr 201523 Apr 2015


Other33rd Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2015
CountryKorea, Republic of


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