Understanding participant behavior trajectories in online health support groups using automatic extraction methods
M. Wen, and C. Rose. Proceedings of the 17th ACM international conference on Supporting group work - GROUP \textquotesingle12, page 179-188. ACM Press, (2012)
DOI: 10.1145/2389176.2389205
Abstract
This 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.
Description
Understanding participant behavior trajectories in online health support groups using automatic extraction methods | Proceedings of the 17th ACM international conference on Supporting group work
%0 Conference Paper
%1 Wen_2012
%A Wen, Miaomiao
%A Rose, Carolyn Penstein
%B Proceedings of the 17th ACM international conference on Supporting group work - GROUP \textquotesingle12
%D 2012
%I ACM Press
%K behavior-analysis forum health helper
%P 179-188
%R 10.1145/2389176.2389205
%T Understanding participant behavior trajectories in online health support groups using automatic extraction methods
%U https://doi.org/10.1145%2F2389176.2389205
%X This 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.
@inproceedings{Wen_2012,
abstract = {This 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.},
added-at = {2020-10-13T17:21:58.000+0200},
author = {Wen, Miaomiao and Rose, Carolyn Penstein},
biburl = {https://www.bibsonomy.org/bibtex/2f89754fe4055b93be4b8a71c30da73cb/brusilovsky},
booktitle = {Proceedings of the 17th {ACM} international conference on Supporting group work - {GROUP} {\textquotesingle}12},
description = {Understanding participant behavior trajectories in online health support groups using automatic extraction methods | Proceedings of the 17th ACM international conference on Supporting group work},
doi = {10.1145/2389176.2389205},
interhash = {c521a0fbaadf8509bf34a0afb592cc2e},
intrahash = {f89754fe4055b93be4b8a71c30da73cb},
keywords = {behavior-analysis forum health helper},
pages = {179-188},
publisher = {{ACM} Press},
timestamp = {2020-10-13T17:21:58.000+0200},
title = {Understanding participant behavior trajectories in online health support groups using automatic extraction methods},
url = {https://doi.org/10.1145%2F2389176.2389205},
year = 2012
}