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Exposure to alcohol content in media increases alcohol consumption and related harm. With exponential growth of media content, it is important to use algorithms to automatically detect and quantify alcohol exposure. Foundation models such as Contrastive Language-Image Pretraining (CLIP) can detect alcohol exposure through Zero-Shot Learning (ZSL) without any additional training. In this paper, we evaluated the ZSL performance of CLIP against a supervised algorithm called Alcoholic Beverage Identification Deep Learning Algorithm Version-2 (ABIDLA2), which is specifically trained to recognise alcoholic beverages in images, across three tasks. We found ZSL achieved similar performance compared to ABIDLA2 in two out of three tasks. However, ABIDLA2 outperformed ZSL in a fine-grained classification task in which determining subtle differences among alcoholic beverages (including containers) are essential. We also found that phrase engineering is essential for improving the performance of ZSL. To conclude, like ABIDLA2, ZSL with little phrase engineering can achieve promising performance in identifying alcohol exposure in images. This makes it easier for researchers, with little or no programming background, to implement ZSL effectively to obtain insightful analytics from digital media. Such analytics can assist researchers and policy makers to propose regulations that can prevent alcohol exposure and eventually prevent alcohol consumption.

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In most countries, the alcohol industry enjoys considerable freedom to market its products. Where government regulation is proposed or enacted, the alcohol industry has often deployed legal arguments and used legal forums to challenge regulation. Governments considering marketing regulation must be cognizant of relevant legal constraints and be prepared to defend their policies against industry legal challenges.

Aims:  Alcohol is a risk factor for family violence that affects partners, parents, children and other relatives. This study aims to provide estimates of the prevalence of alcohol-related family violence reported in 2016 in Australia across numerous socio-demographic groups. Methods:  This paper presents secondary data analysis of 23,749 respondents (10,840 men, 12,909 women) from the Australian Institute of Health and Welfare’s 2016 National Drug Strategy Household Survey (NDSHS). Alcohol-related family violence was measured by self-report as being physically or verbally abused or put in fear from a family member or partner deemed by the victim as under the influence of alcohol. Logistic regression was used to analyse which factors were associated with alcohol-related family violence.      Findings:  Analysis revealed that 5.9% of respondents (7.7% of women and 4.0% of men) reported alcohol-related family violence in the past year from either a partner or another family member. Respondents who were women (vs men), within less advantaged (vs more advantaged) socio-economic groups, risky drinkers (vs non-risky drinkers), residing in outer regional areas (vs major cities), holding a diploma (vs high school education) and single with dependents, reported higher overall rates of alcohol-related family violence. In contrast, respondents aged 55+ had significantly lower odds of experiencing alcohol-related family violence than all other age groups. Conclusions: Alcohol-related family violence was significantly more prevalent amongst respondents in a range of socio-demographic categories. Identification of these groups which are adversely affected by the drinking of family and partners can aid in informing current policy to protect those more vulnerable.