Abstract
A large number of online health communities exist today, helping millions of people with social support during difficult phases of their lives when they suffer from serious diseases. Interactions between members in these communities contain discussions on practical problems faced by people during their illness such as depression, side-effects of medications, etc and answers to those problems provided by other members. Analyzing these interactions can be helpful in getting crucial information about the community such as dominant health issues, identifying sentimental effects of interactions on individual members and identifying influential members. In this paper, we analyze user messages of an online cancer support community, Cancer Survivors Network (CSN), to identity the two types of social support present in them: emotional support and informational support. We model the task as a binary classification problem. We use several generic and novel domain-specific features. Experimental results show that we achieve high classification performance. We, then, use the classifier to predict the type of support in CSN messages and analyze the posting behaviors of regular members and influential members in CSN in terms of the type of support they provide in their messages. We find that influential members generally provide more emotional support as compared to regular members in CSN.
| Original language | English (US) |
|---|---|
| Title of host publication | COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014 |
| Subtitle of host publication | Technical Papers |
| Publisher | Association for Computational Linguistics, ACL Anthology |
| Pages | 827-836 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781941643266 |
| State | Published - Jan 1 2014 |
| Event | 25th International Conference on Computational Linguistics, COLING 2014 - Dublin, Ireland Duration: Aug 23 2014 → Aug 29 2014 |
Publication series
| Name | COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers |
|---|
Other
| Other | 25th International Conference on Computational Linguistics, COLING 2014 |
|---|---|
| Country | Ireland |
| City | Dublin |
| Period | 8/23/14 → 8/29/14 |
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All Science Journal Classification (ASJC) codes
- Language and Linguistics
- Linguistics and Language
Cite this
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Identifying emotional and informational support in online health communities. / Biyani, Prakhar; Caragea, Cornelia; Mitra, Prasenjit; Yen, John.
COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers. Association for Computational Linguistics, ACL Anthology, 2014. p. 827-836 (COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Identifying emotional and informational support in online health communities
AU - Biyani, Prakhar
AU - Caragea, Cornelia
AU - Mitra, Prasenjit
AU - Yen, John
PY - 2014/1/1
Y1 - 2014/1/1
N2 - A large number of online health communities exist today, helping millions of people with social support during difficult phases of their lives when they suffer from serious diseases. Interactions between members in these communities contain discussions on practical problems faced by people during their illness such as depression, side-effects of medications, etc and answers to those problems provided by other members. Analyzing these interactions can be helpful in getting crucial information about the community such as dominant health issues, identifying sentimental effects of interactions on individual members and identifying influential members. In this paper, we analyze user messages of an online cancer support community, Cancer Survivors Network (CSN), to identity the two types of social support present in them: emotional support and informational support. We model the task as a binary classification problem. We use several generic and novel domain-specific features. Experimental results show that we achieve high classification performance. We, then, use the classifier to predict the type of support in CSN messages and analyze the posting behaviors of regular members and influential members in CSN in terms of the type of support they provide in their messages. We find that influential members generally provide more emotional support as compared to regular members in CSN.
AB - A large number of online health communities exist today, helping millions of people with social support during difficult phases of their lives when they suffer from serious diseases. Interactions between members in these communities contain discussions on practical problems faced by people during their illness such as depression, side-effects of medications, etc and answers to those problems provided by other members. Analyzing these interactions can be helpful in getting crucial information about the community such as dominant health issues, identifying sentimental effects of interactions on individual members and identifying influential members. In this paper, we analyze user messages of an online cancer support community, Cancer Survivors Network (CSN), to identity the two types of social support present in them: emotional support and informational support. We model the task as a binary classification problem. We use several generic and novel domain-specific features. Experimental results show that we achieve high classification performance. We, then, use the classifier to predict the type of support in CSN messages and analyze the posting behaviors of regular members and influential members in CSN in terms of the type of support they provide in their messages. We find that influential members generally provide more emotional support as compared to regular members in CSN.
UR - http://www.scopus.com/inward/record.url?scp=84959892881&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959892881&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84959892881
T3 - COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers
SP - 827
EP - 836
BT - COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014
PB - Association for Computational Linguistics, ACL Anthology
ER -