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.

Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis

“…The current study aimed to examine the flow and content of tweets, capturing COVID-19 core discussions and trends following relevant key events. We assumed that some of these key events might have influenced the flow and contents of tweets on COVID-19 [7]. The potential of social media like Twitter in capturing pandemic-related sentiment may ultimately inform novel surveillance methods of individual emotional states and their spread.…”

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

“…Many previous studies have used hashtags as target labels. However, they mostly rely on a predefined set of hashtags that may not be data-driven or require extensive expert annotation instead of taking advantage of topic-specific sources and social media content on other public social media platforms [ 40 , 105 - 107 ]. Notably, using a predefined number of hashtags could be biased and too narrow for capturing relevant information, potentially missing informative hashtags for exposure or outcome.…”

“…Many social media platforms are directly searchable using hashtags, allowing the ability to gather many highly specific posts instead of gathering a large number of nonspecific posts to identify relevant hashtags, keywords, or alcohol content based on available prevalence data. Although the latter approach has shown success in studying alcohol-related behavior previously, methods to extend the analysis to alternative but potentially related outcomes, platforms, or different geographical designations remain unclear [ 39 , 40 , 108 ]. The generalization of such models necessitates the creation of individual models for each particular outcome and geographic area.…”

“…The generalization of such models necessitates the creation of individual models for each particular outcome and geographic area. Finally, these models may fail to take advantage of extensive research on themed communities to understand alcohol use and other outcomes of interest [ 30 , 39 , 40 , 76 , 106 , 109 , 110 ]. In contrast, the method outlined here may help create more efficient public health interventions to analyze the alcohol consumption behavior for a given geographic area of interest, such as a city or hospital catchment area.…”

Threats to Mental Health Facilitated by Dating Applications Use Among Men Having Sex With Men

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