Dataset Information

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis.

ABSTRACT: Binge Drinking (BD) is a common risky behaviour that people hardly report to healthcare professionals, although it is not uncommon to find, instead, personal communications related to alcohol-related behaviors on social media. By following a data-driven approach focusing on User-Generated Content, we aimed to detect potential binge drinkers through the investigation of their language and shared topics. First, we gathered Twitter threads quoting BD and alcohol-related behaviours, by considering unequivocal keywords, identified by experts, from previous evidence on BD. Subsequently, a random sample of the gathered tweets was manually labelled, and two supervised learning classifiers were trained on both linguistic and metadata features, to classify tweets of genuine unique users with respect to media, bot, and commercial accounts. Based on this classification, we observed that approximately 55% of the 1 million alcohol-related collected tweets was automatically identified as belonging to non-genuine users. A third classifier was then trained on a subset of manually labelled tweets among those previously identified as belonging to genuine accounts, to automatically identify potential binge drinkers based only on linguistic features. On average, users classified as binge drinkers were quite similar to the standard genuine Twitter users in our sample. Nonetheless, the analysis of social media contents of genuine users reporting risky behaviours remains a promising source for informed preventive programs.

SUBMITTER: Crocamo C

PROVIDER: S-EPMC7084454 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis.

Crocamo Cristina C Viviani Marco M Bartoli Francesco F Carrà Giuseppe G Pasi Gabriella G

International journal of environmental research and public health 20200226 5

Binge Drinking (BD) is a common risky behaviour that people hardly report to healthcare professionals, although it is not uncommon to find, instead, personal communications related to alcohol-related behaviors on social media. By following a data-driven approach focusing on User-Generated Content, we aimed to detect potential binge drinkers through the investigation of their language and shared topics. First, we gathered Twitter threads quoting BD and alcohol-related behaviours, by considering u ...[more]

PMID: 32111047

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Project description:BackgroundBinge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting).ObjectiveThe aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions.MethodsThis study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented.ResultsThe findings showed unique differences in the types of sensor data that were triangulated with the individuals' episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants' awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet.ConclusionsThis study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population.

Project description:Excessive consumption of alcohol is a major problem in the United States and abroad. Despite many years of study, it is unclear why some individuals drink alcohol excessively while others do not. It has been postulated that either lower or greater acute responses to alcohol, or both, depending on the limb of the breath alcohol concentration curve, contribute to propensity for alcohol misuse.To prospectively assess the relationship of acute alcohol responses to future binge drinking.Within-subject, double-blind, placebo-controlled, multidose laboratory alcohol challenge study with intensive follow-up. Each participant completed 3 randomized sessions examining responses to a high (0.8 g/kg) and low (0.4 g/kg) alcohol dose and placebo, followed by quarterly assessments for 2 years examining drinking behaviors and alcohol diagnoses.Participants recruited from the community.High-risk heavy social drinkers aged 21 to 35 years who habitually engage in weekly binge drinking (n = 104) and light drinker controls (n = 86).We conducted 570 laboratory sessions with a subsequent 99.1% follow-up (1506 of 1520).Biphasic Alcohol Effects Scale, Drug Effects Questionnaire, cortisol response, Timeline Follow-Back, Drinker Inventory of Consequences-Recent, and DSM-IV alcohol abuse and dependence.Alcohol produced greater stimulant and rewarding (liking and wanting) responses and lower sedative and cortisol responses in heavy vs light drinkers. Among the heavy drinkers, greater positive effects and lower sedative effects after alcohol consumption predicted increased binge drinking frequency during follow-up. In turn, greater frequency of binge drinking during follow-up was associated with greater likelihood of meeting diagnostic criteria for alcohol abuse and dependence.The widely held low level response theory and differentiator model should be revised: in high-risk drinkers, stimulant and rewarding alcohol responses even at peak breath alcohol concentrations are important predictors of future alcohol problems.clinicaltrials.gov Identifier: NCT00961792.

Dataset Information

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis.

Publications

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis.

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