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.
Highlights
Excessive alcohol use is a frequent risky behaviour, which accounts for between 1.3% and 3.3% of health costs globally [1]
Knowledge and perception of Binge drinking (BD) risks are often limited [8,9] among young people, with impaired decision making playing a major role [10] in actions leading to immediate
Twitter can be considered as a key source of social media contents, since it provides feasible access to data both retrospectively on sets of historical tweets connected to specific users, and prospectively to capture several matching tweets and related metadata [43], allowing to study individuals’ health behaviours, such as drug and alcohol use [27]
Summary
Excessive alcohol use is a frequent risky behaviour, which accounts for between 1.3% and 3.3% of health costs globally [1]. High rates of alcohol consumption and heavy drinking are common among young people, raising concerns in terms of public health issues [2]. The use of the term is popular and clearly recognizable to researchers in the field and to the general public and young people in particular [6]. Young adults who engage in BD are more likely to report other health risks such as riding with drunk drivers, smoking cigarettes, being a victim of violence, attempting suicide, or using illicit drugs [7]. Knowledge and perception of BD risks are often limited [8,9] among young people, with impaired decision making playing a major role [10] in actions leading to immediate
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