Monitoring adolescent alcohol use via multimodal analysis in social multimedia

Underage drinking or adolescent alcohol use is a major public health problem that causes more than 4,300 annual deaths. Traditional methods for monitoring adolescent alcohol consumption are based on surveys, which have many limitations and are difficult to scale. The main limitations include 1) respondents may not provide accurate, honest answers, 2) surveys with closed-ended questions may have a lower validity rate than other question types, 3) respondents who choose to respond may be different from those who chose not to respond, thus creating bias, 4) cost, 5) small sample size, and 6) lack of temporal sensitivity. We propose a novel approach to monitoring underage alcohol use by analyzing Instagram users' contents in order to overcome many of the limitations of surveys. First, Instagram users' demographics (such as age, gender and race) are determined by analyzing their selfie photos with automatic face detection and face analysis techniques supplied by a state-of-the-art face processing toolkit called Face++. Next, the tags associated with the pictures uploaded by users are used to identify the posts related to alcohol consumption and discover the existence of drinking patterns in terms of time, frequency and location. To that end, we have built an extensive dictionary of drinking activities based on internet slang and major alcohol brands. Finally, we measure the penetration of alcohol brands among underage users within Instagram by analyzing the followers of such brands in order to evaluate to what extent they might influence their followers' drinking behaviors. Experimental results using a large number of Instagram users have revealed several findings that are consistent with those of the conventional surveys, thus partially validating the proposed approach. Moreover, new insights are obtained that may help develop effective intervention. We believe that this approach can be effectively applied to other domains of public health.

[1]  V. Srivastava,et al.  Alcohol and female puberty: the role of intraovarian systems. , 2001 .

[2]  K. Lynch,et al.  Health problems in adolescents with alcohol use disorders: self-report, liver injury, and physical examination findings and correlates. , 2001, Alcoholism, clinical and experimental research.

[3]  Henry Wechsler,et al.  Magnitude of alcohol-related mortality and morbidity among U.S. college students ages 18-24. , 2002, Journal of studies on alcohol.

[4]  David W Jamieson-Drake,et al.  Prevalence and Correlates of Alcohol-Induced Blackouts Among College Students: Results of an E-Mail Survey , 2002, Journal of American college health : J of ACH.

[5]  Mary Ellen O'Connell,et al.  The Epidemiology of Underage Drinking in the United States: An Overview , 2004 .

[6]  Jiebo Luo,et al.  The wisdom of social multimedia: using flickr for prediction and forecast , 2010, ACM Multimedia.

[7]  Jeff Chester,et al.  Alcohol Marketing in the Digital Age , 2010 .

[8]  James Nicholls,et al.  Everyday, everywhere: alcohol marketing and social media--current trends. , 2012, Alcohol and alcoholism.

[9]  Tao Mei,et al.  Robust and accurate mobile visual localization and its applications , 2013, TOMCCAP.

[10]  Chong-Wah Ngo,et al.  Unified entity search in social media community , 2013, WWW.

[11]  Ching-Yung Lin,et al.  Mobile App Connecting People Based on Personality Detection and Image Perception Analysis , 2014, 2014 IEEE International Symposium on Multimedia.

[12]  Asha Menon,et al.  Automatic identification of alcohol-related promotions on Twitter and prediction of promotion spread , 2014, 2014 Systems and Information Engineering Design Symposium (SIEDS).

[13]  Yun Yang,et al.  Emotionally Representative Image Discovery for Social Events , 2014, ICMR.

[14]  Munmun De Choudhury,et al.  Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity , 2014, ICWSM.

[15]  Jiebo Luo,et al.  Towards Lifestyle Understanding: Predicting Home and Vacation Locations from User's Online Photo Collections , 2015, ICWSM.

[16]  Jiebo Luo,et al.  Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing , 2015, AAAI.