Extracting key challenges in achieving sobriety through shared subspace learning Harikumar, Haripriya, Nguyen, Thin, Rana, Santu, Gupta, Sunil, Kaimal, Ramachandra and Venkatesh, Svetha 2016, Extracting key challenges in achieving sobriety through shared subspace learning, in ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International Conference, Springer International, Cham, Switzerland, pp. 420-433, doi: 10.1007/978-3-319-49586-6_28. Attached Files Name Description MIMEType Size Downloads Title Extracting key challenges in achieving sobriety through shared subspace learning Author(s) Harikumar, Haripriya Nguyen, Thin orcid.org/0000-0003-3467-8963 Rana, Santu orcid.org/0000-0003-2247-850X Gupta, Sunil orcid.org/0000-0002-3308-1930 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 420 End page 433 Total pages 14 Publisher Springer International Place of publication Cham, Switzerland Keyword(s) social media Reddit shared subspace topics alcohol addiction Summary Alcohol abuse is quite common among all people without any age restrictions. The uncontrolled use of alcohol affects both the individual and society. Alcohol addiction leads to a huge increase in crime, suicide, health related problems and financial crisis. Research has shown that certain behavioral changes can be effective towards staying abstained. The analysis of behavioral changes of quitters and those who are at the beginning phase of quitting can be useful for reducing the issues related to alcohol addiction. Most of the conventional approaches are based on surveys and, therefore, expensive in both time and cost. Social media has lend itself as a source of large, diverse and unbiased data for analyzing social behaviors. Reddit is a social media platform where a large number of people communicate with each other. It has many different sub-groups called subreddits categorized based on the subject. We collected more than 40,000 self reported user’s data from a subreddit called ‘/r/stopdrinking’. We divide the data into two groups, short-term with abstinent days less than 30 and long-term abstainers with abstinent days greater than 365 based on badge days at the time of post submission. Common and discriminative topics are extracted from the data using JS-NMF, a shared subspace non-negative matrix factorization method. The validity of the extracted topics are demonstrated through predictive performance. ISBN 9783319495859 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-49586-6_28 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 Publishing Persistent URL http://hdl.handle.net/10536/DRO/DU:30093592 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system 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, 12:19:35 EST Fri, 07 Apr 2017, 05:05:14 EST Mon, 29 May 2017, 17:29:59 EST Mon, 29 May 2017, 17:33:31 EST Tue, 30 May 2017, 06:04:53 EST Wed, 31 May 2017, 09:44:27 EST Wed, 31 May 2017, 09:51:46 EST Wed, 31 May 2017, 09:52:28 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 112 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 29 May 2017, 17:33:31 EST

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