Understanding Participant Behavior Trajectories in Online Health Support Groups Using Automatic Extraction Methods

Simple item page
dc.contributor.authorWen, Miaomiao
dc.contributor.authorRose, Carolyn Penstein
dc.date.accessioned2023-06-08T11:45:08Z
dc.date.available2023-06-08T11:45:08Z
dc.date.issued2012
dc.description.abstractThis paper presents an automatic analysis method that enables efficient examination of participant behavior trajectories in online communities, which offers the opportunity to examine behavior over time at a level of granularity that has previously only been possible in small scale case study analyses. We provide an empirical validation of its performance. We then illustrate how this method offers insights into behavior patterns that enable avoiding faulty oversimplified assumptions about participation, such as that it follows a consistent trend over time. In particular, we use this method to investigate the connection between user behavior and distressful cancer events and demonstrate how this tool could assist in cancer story summarization.en
dc.identifier.doi10.1145/2389176.2389205
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4933
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of the 2012 ACM International Conference on Supporting Group Work
dc.subjectnatural language analysis
dc.subjectcancer trajectory
dc.subjectonline support groups
dc.subjectdisease event
dc.titleUnderstanding Participant Behavior Trajectories in Online Health Support Groups Using Automatic Extraction Methodsen
gi.citation.publisherPlaceNew York, NY, USA
gi.citation.startPage179–188
gi.conference.locationSanibel Island, Florida, USA

Files

Collections

GROUP 2012