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Extracting key challenges in achieving sobriety through shared subspace learning
conference contribution
posted on 2016-01-01, 00:00 authored by Haripriya Harikumar, Thin NguyenThin Nguyen, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, R Kaimal, Svetha VenkateshSvetha VenkateshAlcohol 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.
History
Event
Advanced Data Mining and Applications. International Conference (12th : 2016 : Gold Coast, Queensland)Volume
10086Series
Lecture notes in artificial intelligencePagination
420 - 433Publisher
Springer InternationalLocation
Gold Coast, QueenslandPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2016-12-12End date
2016-12-15ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319495859Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, Springer International PublishingEditor/Contributor(s)
J Li, J Li, X Li, Q Sheng, S WangTitle of proceedings
ADMA 2016 : Proceedings of the 12th Advanced Data Mining and Applications International ConferenceUsage metrics
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