{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T13:44:59Z","timestamp":1781358299617,"version":"3.54.1"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T00:00:00Z","timestamp":1561075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2019,6,21]]},"abstract":"<jats:p>The design of computational methods to recognize alcohol intake is a relevant problem in ubiquitous computing. While mobile crowdsensing and social media analytics are two current approaches to characterize alcohol consumption in everyday life, the question of how they can be integrated, to examine their relative value as informative of the drinking phenomenon and to exploit their complementarity towards the classification of drinking-related attributes, remains as an open issue. In this paper, we present a comparative study based on five years of Instagram data about alcohol consumption and a 200+ person crowdsensing campaign collected in the same country (Switzerland). Our contributions are two-fold. First, we conduct data analyses that uncover temporal, spatial, and social contextual patterns of alcohol consumption on weekend nights as represented by both crowdsensing and social media. This comparative analysis provides a contextual snapshot of the alcohol drinking practices of urban youth dwellers. Second, we use a machine learning framework to classify individual drinking events according to alcohol and non-alcohol categories, using images features and contextual cues from individual and joint data sources. Our best performing models give an accuracy of 82.3% on alcohol category classification (against a baseline of 48.5%) and 90% on alcohol\/non-alcohol classification (against a baseline of 65.9%) using a fusion of image features and contextual cues in this task. Our work uncovers important patterns in drinking behaviour across these two datasets and the results of study are promising towards developing systems that use machine learning for self-monitoring of alcohol consumption.<\/jats:p>","DOI":"10.1145\/3328930","type":"journal-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T13:45:01Z","timestamp":1561383901000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Drinks &amp; Crowds"],"prefix":"10.1145","volume":"3","author":[{"given":"Thanh-Trung","family":"Phan","sequence":"first","affiliation":[{"name":"Idiap Research Institute &amp; EPFL, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Skanda","family":"Muralidhar","sequence":"additional","affiliation":[{"name":"Idiap Research Institute &amp; EPFL, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Gatica-Perez","sequence":"additional","affiliation":[{"name":"Idiap Research Institute &amp; EPFL, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Alcohol Statistic from WHO","unstructured":"2014. Alcohol Statistic from WHO . http:\/\/www.who.int\/substance_abuse\/publications\/global_alcohol_report\/profiles\/che.pdf Accessed: 2017-11-20. 2014. Alcohol Statistic from WHO. http:\/\/www.who.int\/substance_abuse\/publications\/global_alcohol_report\/profiles\/che.pdf Accessed: 2017-11-20."},{"key":"e_1_2_1_2_1","volume-title":"Alcohol In Figures","year":"2017","unstructured":"2017. Alcohol In Figures 2017 . https:\/\/www.eav.admin.ch\/eav\/fr\/home\/dokumentation\/publikationen\/zahlen-und-fakten.html Accessed : 2017-11-20. 2017. Alcohol In Figures 2017. https:\/\/www.eav.admin.ch\/eav\/fr\/home\/dokumentation\/publikationen\/zahlen-und-fakten.html Accessed: 2017-11-20."},{"key":"e_1_2_1_3_1","volume-title":"Retrieved","year":"2018","unstructured":"2018. Definition of party in English . Retrieved April 16, 2018 from https:\/\/dictionary.cambridge.org\/dictionary\/english\/party 2018. Definition of party in English. Retrieved April 16, 2018 from https:\/\/dictionary.cambridge.org\/dictionary\/english\/party"},{"key":"e_1_2_1_4_1","volume-title":"Global status report on alcohol and health from WHO","unstructured":"2018. Global status report on alcohol and health from WHO . http:\/\/apps.who.int\/iris\/bitstream\/handle\/10665\/274603\/9789241565639-eng.pdf Accessed: 2018-12-03. 2018. Global status report on alcohol and health from WHO. http:\/\/apps.who.int\/iris\/bitstream\/handle\/10665\/274603\/9789241565639-eng.pdf Accessed: 2018-12-03."},{"key":"e_1_2_1_5_1","volume-title":"Retrieved","year":"2018","unstructured":"2018. Sklearn with GridSearchCV . Retrieved September 05, 2018 from http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.GridSearchCV.html 2018. Sklearn with GridSearchCV. Retrieved September 05, 2018 from http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.GridSearchCV.html"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702153"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185677.2185741"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2009.32"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI.2015.59"},{"key":"e_1_2_1_10_1","volume-title":"Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC public health 17, 1","author":"Attwood Sophie","year":"2017","unstructured":"Sophie Attwood , Hannah Parke , John Larsen , and Katie L Morton . 2017. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC public health 17, 1 ( 2017 ), 394. Sophie Attwood, Hannah Parke, John Larsen, and Katie L Morton. 2017. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC public health 17, 1 (2017), 394."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3090051"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5993\/AJHB.32.4.9"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130902"},{"key":"e_1_2_1_14_1","volume-title":"celebrations, and commiserations: measuring drinking during feasting and fasting to improve national and individual estimates of alcohol consumption. BMC medicine 13, 1","author":"Bellis Mark A","year":"2015","unstructured":"Mark A Bellis , Karen Hughes , Lisa Jones , Michela Morleo , James Nicholls , Ellie McCoy , Jane Webster , and Harry Sumnall . 2015. Holidays , celebrations, and commiserations: measuring drinking during feasting and fasting to improve national and individual estimates of alcohol consumption. BMC medicine 13, 1 ( 2015 ), 113. Mark A Bellis, Karen Hughes, Lisa Jones, Michela Morleo, James Nicholls, Ellie McCoy, Jane Webster, and Harry Sumnall. 2015. Holidays, celebrations, and commiserations: measuring drinking during feasting and fasting to improve national and individual estimates of alcohol consumption. BMC medicine 13, 1 (2015), 113."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3161161"},{"key":"e_1_2_1_16_1","volume-title":"Different digital paths to the keg? How exposure to peers' alcohol-related social media content influences drinking among male and female first-year college students. Addictive behaviors 57","author":"Boyle Sarah C","year":"2016","unstructured":"Sarah C Boyle , Joseph W LaBrie , Nicole M Froidevaux , and Yong D Witkovic . 2016. Different digital paths to the keg? How exposure to peers' alcohol-related social media content influences drinking among male and female first-year college students. Addictive behaviors 57 ( 2016 ), 21--29. Sarah C Boyle, Joseph W LaBrie, Nicole M Froidevaux, and Yong D Witkovic. 2016. Different digital paths to the keg? How exposure to peers' alcohol-related social media content influences drinking among male and female first-year college students. Addictive behaviors 57 (2016), 21--29."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.15288\/jsad.2015.76.635"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971672"},{"key":"e_1_2_1_20_1","volume-title":"Urban nightscapes: Youth cultures, pleasure spaces and corporate power","author":"Chatterton Paul","unstructured":"Paul Chatterton and Robert Hollands . 2003. Urban nightscapes: Youth cultures, pleasure spaces and corporate power . Vol. 18 . Psychology Press . Paul Chatterton and Robert Hollands. 2003. Urban nightscapes: Youth cultures, pleasure spaces and corporate power. Vol. 18. Psychology Press."},{"key":"e_1_2_1_21_1","volume-title":"Xception: Deep learning with depthwise separable convolutions. arXiv preprint","author":"Chollet Fran\u00e7ois","year":"2017","unstructured":"Fran\u00e7ois Chollet . 2017 . Xception: Deep learning with depthwise separable convolutions. arXiv preprint (2017), 1610--02357. Fran\u00e7ois Chollet. 2017. Xception: Deep learning with depthwise separable convolutions. arXiv preprint (2017), 1610--02357."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370288"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702154"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10579-012-9185-0"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1177\/0042098013484532"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ejcn.1601746"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1024\/suc.2003.49.2.105"},{"key":"e_1_2_1_30_1","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1609\/icwsm.v8i1.14554","article-title":"A Tale of Cities: Urban Biases in Volunteered Geographic Information","volume":"14","author":"Hecht Brent J","year":"2014","unstructured":"Brent J Hecht and Monica Stephens . 2014 . A Tale of Cities: Urban Biases in Volunteered Geographic Information . ICWSM 14 (2014), 197 -- 205 . Brent J Hecht and Monica Stephens. 2014. A Tale of Cities: Urban Biases in Volunteered Geographic Information. ICWSM 14 (2014), 197--205.","journal-title":"ICWSM"},{"key":"e_1_2_1_31_1","volume-title":"Jiebo Luo, and Henry Kautz.","author":"Hossain Nabil","year":"2016","unstructured":"Nabil Hossain , Tianran Hu , Roghayeh Feizi , Ann Marie White , Jiebo Luo, and Henry Kautz. 2016 . Inferring fine-grained details on user activities and home location from social media: Detecting drinking-while-tweeting patterns in communities. arXiv preprint arXiv:1603.03181 (2016). Nabil Hossain, Tianran Hu, Roghayeh Feizi, Ann Marie White, Jiebo Luo, and Henry Kautz. 2016. Inferring fine-grained details on user activities and home location from social media: Detecting drinking-while-tweeting patterns in communities. arXiv preprint arXiv:1603.03181 (2016)."},{"key":"e_1_2_1_32_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G","year":"2017","unstructured":"Andrew G Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . 2017 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 661--662","author":"Cindy Kao Hsin-Liu","year":"2012","unstructured":"Hsin-Liu Cindy Kao , Bo-Jhang Ho , Allan C Lin , and Hao-Hua Chu . 2012 . Phone-based gait analysis to detect alcohol usage . In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 661--662 . Hsin-Liu Cindy Kao, Bo-Jhang Ho, Allan C Lin, and Hao-Hua Chu. 2012. Phone-based gait analysis to detect alcohol usage. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 661--662."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2615569.2615678"},{"key":"e_1_2_1_35_1","first-page":"w13826","article-title":"Alcohol consumption in late adolescence and early adulthood--where is the problem","volume":"143","author":"Kuntsche Emmanuel","year":"2013","unstructured":"Emmanuel Kuntsche and Gerhard Gmel . 2013 . Alcohol consumption in late adolescence and early adulthood--where is the problem . Swiss Med Wkly 143 (2013), w13826 . Emmanuel Kuntsche and Gerhard Gmel. 2013. Alcohol consumption in late adolescence and early adulthood--where is the problem. Swiss Med Wkly 143 (2013), w13826.","journal-title":"Swiss Med Wkly"},{"key":"e_1_2_1_36_1","volume-title":"Investigating the drinking patterns of young people over the course of the evening at weekends. Drug and alcohol dependence 124, 3","author":"Kuntsche Emmanuel","year":"2012","unstructured":"Emmanuel Kuntsche and Florian Labhart . 2012. Investigating the drinking patterns of young people over the course of the evening at weekends. Drug and alcohol dependence 124, 3 ( 2012 ), 319--324. Emmanuel Kuntsche and Florian Labhart. 2012. Investigating the drinking patterns of young people over the course of the evening at weekends. Drug and alcohol dependence 124, 3 (2012), 319--324."},{"key":"e_1_2_1_37_1","volume-title":"ICAT: development of an internet-based data collection method for ecological momentary assessment using personal cell phones. European Journal of Psychological Assessment","author":"Kuntsche Emmanuel","year":"2013","unstructured":"Emmanuel Kuntsche and Florian Labhart . 2013. ICAT: development of an internet-based data collection method for ecological momentary assessment using personal cell phones. European Journal of Psychological Assessment ( 2013 ). Emmanuel Kuntsche and Florian Labhart. 2013. ICAT: development of an internet-based data collection method for ecological momentary assessment using personal cell phones. European Journal of Psychological Assessment (2013)."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1530-0277.2012.01872.x"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1978942.1979295"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.4278\/0890-1171-15.2.107"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/alcalc\/agl071"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2406367.2406400"},{"key":"e_1_2_1_43_1","volume-title":"Trinh Minh Tri Do, and Daniel Gatica-Perez","author":"Malmi Eric","year":"2013","unstructured":"Eric Malmi , Trinh Minh Tri Do, and Daniel Gatica-Perez . 2013 . From Foursquare to My Square : Learning Check-in Behavior from Multiple Sources.. In ICWSM. Eric Malmi, Trinh Minh Tri Do, and Daniel Gatica-Perez. 2013. From Foursquare to My Square: Learning Check-in Behavior from Multiple Sources.. In ICWSM."},{"key":"e_1_2_1_44_1","volume-title":"Alcohol-related Facebook activity predicts alcohol use patterns in college students. Addiction research & theory 24, 5","author":"Marczinski Cecile A","year":"2016","unstructured":"Cecile A Marczinski , Heather Hertzenberg , Perilou Goddard , Sarah F Maloney , Amy L Stamates , and Kathleen O Connor . 2016. Alcohol-related Facebook activity predicts alcohol use patterns in college students. Addiction research & theory 24, 5 ( 2016 ), 398--405. Cecile A Marczinski, Heather Hertzenberg, Perilou Goddard, Sarah F Maloney, Amy L Stamates, and Kathleen O Connor. 2016. Alcohol-related Facebook activity predicts alcohol use patterns in college students. Addiction research & theory 24, 5 (2016), 398--405."},{"key":"e_1_2_1_45_1","volume-title":"British alcohol policy and the new culture of intoxication. Crime, media, culture 1, 3","author":"Measham Fiona","year":"2005","unstructured":"Fiona Measham and Kevin Brain . 2005. Binge drinking , British alcohol policy and the new culture of intoxication. Crime, media, culture 1, 3 ( 2005 ), 262--283. Fiona Measham and Kevin Brain. 2005. Binge drinking, British alcohol policy and the new culture of intoxication. Crime, media, culture 1, 3 (2005), 262--283."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750511.2750524"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971677"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2015.7363914"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3152832.3152857"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594368.2594386"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1360-0443.2003.00467.x"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971713"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2797901"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2494091.2497284"},{"key":"e_1_2_1_55_1","volume-title":"Jussara M Almeida, Mirco Musolesi, and Antonio AF Loureiro.","author":"Silva Thiago H","year":"2014","unstructured":"Thiago H Silva , Pedro OS Vaz de Melo , Jussara M Almeida, Mirco Musolesi, and Antonio AF Loureiro. 2014 . You Are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare.. In ICWSM. Thiago H Silva, Pedro OS Vaz de Melo, Jussara M Almeida, Mirco Musolesi, and Antonio AF Loureiro. 2014. You Are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare.. In ICWSM."},{"key":"e_1_2_1_56_1","first-page":"12","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","volume":"4","author":"Szegedy Christian","year":"2017","unstructured":"Christian Szegedy , Sergey Ioffe , Vincent Vanhoucke , and Alexander A Alemi . 2017 . Inception-v4, inception-resnet and the impact of residual connections on learning .. In AAAI , Vol. 4. 12 . Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning.. In AAAI, Vol. 4. 12.","journal-title":"AAAI"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drugalcdep.2010.02.011"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2675133.2675278"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2807545"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1111\/add.12862"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrurstud.2007.04.003"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/1644038.1644094"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702289"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1470-6431.2008.00725.x"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3328930","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3328930","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:54:41Z","timestamp":1750204481000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3328930"}},"subtitle":["Characterizing Alcohol Consumption through Crowdsensing and Social Media"],"short-title":[],"issued":{"date-parts":[[2019,6,21]]},"references-count":63,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,6,21]]}},"alternative-id":["10.1145\/3328930"],"URL":"https:\/\/doi.org\/10.1145\/3328930","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,21]]},"assertion":[{"value":"2018-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-04-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-06-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}