{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:07:27Z","timestamp":1744157247789,"version":"3.37.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030046477"},{"type":"electronic","value":"9783030046484"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-04648-4_28","type":"book-chapter","created":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T00:53:52Z","timestamp":1542416032000},"page":"330-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep Self-Taught Learning for Detecting Drug Abuse Risk Behavior in Tweets"],"prefix":"10.1007","author":[{"given":"Han","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"NhatHai","family":"Phan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Geller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huy","family":"Vo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhole","family":"Manasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueqi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophie","family":"Di Lorio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thang","family":"Dinh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soon Ae","family":"Chun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,18]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Aphinyanaphongs, Y., Lulejian, A., Penfold-Brown, D., Bonneau, R., Krebs, P.: Text classification for automatic detection of e-cigarette use and use for smoking cessation from twitter: a feasibility pilot. In: Pacific Symposium on Biocomputing, vol. 21, pp. 480\u2013491 (2016)","DOI":"10.1142\/9789814749411_0044"},{"issue":"1","key":"28_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1\u2013127 (2009)","journal-title":"Found. Trends Mach. Learn."},{"key":"28_CR3","unstructured":"Bettge, A., Roscher, R., Wenzel, S.: Deep self-taught learning for remote sensing image classification. CoRR abs\/1710.07096 (2017)"},{"issue":"2","key":"28_CR4","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.resuscitation.2012.10.017","volume":"84","author":"JC Bosley","year":"2013","unstructured":"Bosley, J.C., et al.: Decoding twitter: surveillance and trends for cardiac arrest and resuscitation communication. Resuscitation 84(2), 206\u2013212 (2013)","journal-title":"Resuscitation"},{"issue":"2","key":"28_CR5","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/s13181-013-0299-6","volume":"9","author":"M Chary","year":"2013","unstructured":"Chary, M., Genes, N., McKenzie, A., Manini, A.F.: Leveraging social networks for toxicovigilance. J. Med. Toxicol. 9(2), 184\u2013191 (2013)","journal-title":"J. Med. Toxicol."},{"issue":"9","key":"28_CR6","doi-asserted-by":"publisher","first-page":"e189","DOI":"10.2196\/jmir.2741","volume":"15","author":"CL Hanson","year":"2013","unstructured":"Hanson, C.L., Cannon, B., Butron, S., Giraud-Carrier, C.: An exploration of social circles and prescription drug abuse through twitter. J. Med. Internet Res. 15(9), e189 (2013)","journal-title":"J. Med. Internet Res."},{"issue":"4","key":"28_CR7","doi-asserted-by":"publisher","first-page":"e62","DOI":"10.2196\/jmir.2503","volume":"15","author":"CL Hanson","year":"2013","unstructured":"Hanson, C.L., Burton, S.H., Giraud-Carrier, C., West, J.H., Barnes, M.D., Hansen, B.: Tweaking and tweeting exploring twitter for nonmedical use of a psychostimulant drug (adderall) among college students. J. Med. Internet Res. 15(4), e62 (2013)","journal-title":"J. Med. Internet Res."},{"issue":"10","key":"28_CR8","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1007\/s40264-015-0333-5","volume":"38","author":"PM Coloma","year":"2015","unstructured":"Coloma, P.M., Becker, B., Sturkenboom, M.C.J.M., van Mulligen, E.M., Kors, J.A.: Evaluating social media networks in medicines safety surveillance: two case studies. Drug Saf. 38(10), 921\u2013930 (2015)","journal-title":"Drug Saf."},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Dong, X., Meng, D., Ma, F., Yang, Y.: A dual-network progressive approach to weakly supervised object detection. In: Proceedings of the 2017 ACM on Multimedia Conference, MM 2017, pp. 279\u2013287 (2017)","DOI":"10.1145\/3123266.3123455"},{"key":"28_CR10","unstructured":"Northern Ireland on Drug Abuse: Overdose death rates, September 15, 2017. National Institute on Drug Abuse, 20 January 2018. https:\/\/www.drugabuse.gov\/related-topics\/trends-statistics\/overdose-death-rates"},{"key":"28_CR11","unstructured":"Northern Ireland on Drug Abuse: Twitter by the numbers: stats, demographics and fun facts, 2018. Omnicore, 7 March 2018. https:\/\/www.omnicoreagency.com\/twitter-statistics\/"},{"key":"28_CR12","unstructured":"Ex-DEA Agent: Opioid crisis fueled by drug industry and congress. CBS 60 Minutes, 17 October 2017"},{"key":"28_CR13","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.neucom.2014.05.028","volume":"144","author":"J Gan","year":"2014","unstructured":"Gan, J., Li, L., Zhai, Y., Liu, Y.: Deep self-taught learning for facial beauty prediction. Neurocomputing 144, 295\u2013303 (2014)","journal-title":"Neurocomputing"},{"issue":"8","key":"28_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"28_CR15","unstructured":"Hossain, N., Hu, T., Feizi, R., White, A.M., Luo, J., Kautz, H.A.: Precise localization of homes and activities: detecting drinking-while-tweeting patterns in communities. In: ICWSM (2016)"},{"issue":"11","key":"28_CR16","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"28_CR17","unstructured":"Marino, T.: Withdraws in latest setback for trump\u2019s opioid fight. New York Times, 17 October 2017"},{"issue":"10","key":"28_CR18","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1002\/pds.3307","volume":"21","author":"EC McNaughton","year":"2012","unstructured":"McNaughton, E.C., Black, R.A., Zulueta, M.G., Budman, S.H., Butler, S.F.: Measuring online endorsement of prescription opioids abuse: an integrative methodology. Pharmacoepidemiol. Drug Saf. 21(10), 1081\u20131092 (2012)","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"28_CR19","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs\/1301.3781 (2013)"},{"key":"28_CR20","unstructured":"Monitoring the Future: A continuing study of american youth. http:\/\/www.monitoringthefuture.org"},{"issue":"8","key":"28_CR21","doi-asserted-by":"publisher","first-page":"e174","DOI":"10.2196\/jmir.2534","volume":"15","author":"M Mysl\u00edn","year":"2013","unstructured":"Mysl\u00edn, M., Zhu, S.H., Chapman, W., Conway, M.: Using twitter to examine smoking behavior and perceptions of emerging tobacco products. J. Med. Internet Res. 15(8), e174 (2013)","journal-title":"J. Med. Internet Res."},{"key":"28_CR22","unstructured":"National Institute on Drug Abuse: Gun violence archive, past summary ledgers. (n.d.). Gun Violence Archive, 20 January 2018. http:\/\/www.gunviolencearchive.org\/past-tolls"},{"key":"28_CR23","unstructured":"National Poisoning Data System: National Poisoning Data System, 16 January 2017. http:\/\/www.aapcc.org\/data-system\/"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Phan, N., Chun, S.A., Bhole, M., Geller, J.: Enabling real-time drug abuse detection in tweets. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 1510\u20131514 (2017)","DOI":"10.1109\/ICDE.2017.221"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 759\u2013766 (2007)","DOI":"10.1145\/1273496.1273592"},{"key":"28_CR26","unstructured":"SAMHSA: Key substance use and mental health indicators in the United States, 2015. SAMHSA (n.d.), 20 January 2018. https:\/\/www.samhsa.gov\/data\/sites\/default\/files\/NSDUH-FFR1-2015\/NSDUH-FFR1-2015\/NSDUH-FFR1-2015.htm"},{"key":"28_CR27","unstructured":"SAMHSA: Key substance use and mental health indicators in the United States, 2016. SAMHSA (n.d.), 20 January 2018. https:\/\/www.samhsa.gov\/data\/sites\/default\/files\/NSDUH-FFR1-2016\/NSDUH-FFR1-2016.htm"},{"issue":"3","key":"28_CR28","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s40264-015-0379-4","volume":"39","author":"A Sarker","year":"2016","unstructured":"Sarker, A., et al.: Social media mining for toxicovigilance: automatic monitoring of prescription medication abuse from twitter. Drug Saf. 39(3), 231\u2013240 (2016)","journal-title":"Drug Saf."},{"issue":"4","key":"28_CR29","doi-asserted-by":"publisher","first-page":"S122","DOI":"10.1016\/j.annemergmed.2013.07.169","volume":"62","author":"L Shutler","year":"2013","unstructured":"Shutler, L.: Prescription opioids in the twittersphere a contextual analysis of tweets about prescription drugs. Ann. Emerg. Med. 62(4), S122 (2013)","journal-title":"Ann. Emerg. Med."},{"issue":"5","key":"28_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0019467","volume":"6","author":"A Signorini","year":"2011","unstructured":"Signorini, A., Segre, A.M., Polgreen, P.M.: The use of twitter to track levels of disease activity and public concern in the U.S. during the influenza a h1n1 pandemic. PLOS ONE 6(5), 1\u201310 (2011)","journal-title":"PLOS ONE"},{"key":"28_CR31","unstructured":"Substance Abuse and Mental Health Services Administration Center for Behavioral Health Statistics and Quality (formerly the Office of Applied Studies): The dawn report: highlights of the 2009 drug abuse warning network (dawn) findings on drug-related emergency department visits, 28 December 2010"},{"key":"28_CR32","unstructured":"The National Center on Addiction and Substance Abuse: Commonly used illegal drugs, 16 January 2017. http:\/\/www.centeronaddiction.org\/addiction\/commonly-used-illegal-drugs"},{"key":"28_CR33","unstructured":"US FDA: Medwatch: the FDA safety information and adverse event reporting program, 16 January 2017. http:\/\/www.fda.gov\/Safety\/MedWatch\/"},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Weston, J., Ratle, F., Collobert, R.: Deep learning via semi-supervised embedding. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 1168\u20131175 (2008)","DOI":"10.1145\/1390156.1390303"},{"key":"28_CR35","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Liang, X., Wang, X., Yeung, D., Gupta, A.: Temporal dynamic graph LSTM for action-driven video object detection. CoRR abs\/1708.00666 (2017)","DOI":"10.1109\/ICCV.2017.200"}],"container-title":["Lecture Notes in Computer Science","Computational Data and Social Networks"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-04648-4_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,3]],"date-time":"2019-11-03T01:56:19Z","timestamp":1572746179000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-04648-4_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030046477","9783030046484"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-04648-4_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"CSoNet","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Social Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csonet2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/optnetsci.cise.ufl.edu\/CSoNet\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"106","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"44","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}