Understanding behavioral differences between short and long-term drinking abstainers from social media Harikumar, Haripriya, Nguyen, Thin, Gupta, Sunil, Rana, Santu, Kaimal, Ramachandra and Venkatesh, Svetha 2016, Understanding behavioral differences between short and long-term drinking abstainers from social media, in ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International Conference, Springer International, Cham, Switzerland, pp. 520-533, doi: 10.1007/978-3-319-49586-6_36. Attached Files Name Description MIMEType Size Downloads Title Understanding behavioral differences between short and long-term drinking abstainers from social media Author(s) Harikumar, Haripriya Nguyen, Thin orcid.org/0000-0003-3467-8963 Gupta, Sunil orcid.org/0000-0002-3308-1930 Rana, Santu orcid.org/0000-0003-2247-850X Kaimal, Ramachandra Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Advanced Data Mining and Applications. International Conference (12th : 2016 : Gold Coast, Queensland) Conference location Gold Coast, Queensland Conference dates 12-15 Dec. 2016 Title of proceedings ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International Conference Editor(s) Li, Jinyan Li, Xue Wang, Shuliang Li, Jianxin Sheng, Quan Z. Publication date 2016 Series Lecture notes in artificial intelligence Conference series Advanced Data Mining and Applications International Conference Start page 520 End page 533 Total pages 14 Publisher Springer International Place of publication Cham, Switzerland Keyword(s) feature selection health promotion Reddit stop drinking abstinence Summary Drinking alcohol has high cost on society. The journey from being a regular drinker to a successful quitter may be a long and hard journey, fraught with the risk to relapse. Research has shown that certain behavioral changes can be effective towards staying abstained. Traditional way to conduct research on drinking abstainers uses questionnaire based approach to collect data from a curated group of people. However, it is an expensive approach in both cost and time and often results in small data with less diversity. Recently, social media has emerged as a rich data source. Reddit is one such social media platform that has a community (‘subreddit’) with an interest to quit drinking. The discussions among the group dates back to year 2011 and contain more than 40,000 posts. This large scale data is generated by users themselves and without being limited by any survey questionnaires. The most predictive factors from the features (unigrams, topics and LIWC) associated with short-term and long-term abstinence are identified using Lasso. It is seen that many common patterns manifest in unigrams, topics and LIWC. Whilst topics provided much richer associations between a group of words and the outcome, unigrams and LIWC are found to be good at finding highly predictive solo and psycho linguistically important words. Combining them we have found that many interesting patterns that are associated with the successful attempt made by the long-term abstainer, at the same time finding many of the common issues faced during the initial period of abstinence. ISBN 9783319495859 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-49586-6_36 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 0 Not Applicable HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2016, Springer International Persistent URL http://hdl.handle.net/10536/DRO/DU:30093593 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 06 Apr 2017, 12:19:44 EST Fri, 07 Apr 2017, 05:05:16 EST Mon, 29 May 2017, 17:58:23 EST Mon, 29 May 2017, 17:59:16 EST Tue, 30 May 2017, 06:04:56 EST Wed, 31 May 2017, 10:03:55 EST Wed, 31 May 2017, 10:09:55 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 334 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 29 May 2017, 17:59:16 EST

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