Emmanuel Kuntsche

Background: Young people spend hours on social media every day, where following influencers is the norm. Influencers post frequently about alcohol and alcohol brands and there has been a rise in influencer marketing. However, prevalence estimates rely on manual analyses of only a small number of posts. We used a large language model (LLM) to estimate at scale how often top Australian Instagram influencers reference alcohol and alcohol brands, and whether this varies by influencer type.Methods: We identified 988 of the most-followed Australian Instagram influencers and retrieved all 667,531 of their posts that were visible to all users, including those under 18 (i.e., not age gated). An LLM classified whether each post referenced alcohol and these were screened manually to record brand references. Influencers were coded into 14 types (e.g., athletes, actors).Results: Most influencers (87.45%) referenced alcohol and 47.10% referenced an alcohol brand in at least one post. Overall, 20,448 posts (3.06%) referenced alcohol and 2,869 (0.43%) a brand (14% of alcohol-related posts). Food influencers posted the most alcohol and brand-related content, while athletes posted the least alcohol-related content but had the highest proportion referencing a brand.Conclusions: Influencer alcohol-related and alcohol-related brand content is pervasive on Instagram and reaches audiences of every age, including those under 18 who should not see alcohol advertising.So what: Given the link between exposure and use, health promotion should aim policies and strategies at reducing alcohol-related content, particularly branded content that likely reflects influencer marketing.

Introduction: Reddit is a space where people commonly discuss sensitive health‐related topics and share experiences. We explore discussions relating to alcohol use during pregnancy to understand why people turn to platforms like Reddit for public health advice, and to identify potential lessons for public health. Methods: We undertook a descriptive qualitative study, using directed content analysis, to explore 1654 Reddit comments that discussed alcohol and pregnancy, examining the purpose, positioning and use of evidence. Results: Comments primarily focussed on alcohol consumption while knowingly pregnant (57%) followed by comments about drinking before pregnancy recognition (35%). Comments were evenly split between those supporting (37%) and opposing (37%) alcohol use during pregnancy. A further 14% adopted a precautionary stance, and 11% expressed mixed views. Fifty‐four percent of comments drew on some form of evidence, with anecdotal evidence most common (43%), followed by advice from health professionals (29%). Anecdotal accounts often involved personal experience used to justify conclusions about risk. Discussion and Conclusions: The findings suggest that Reddit functions as a widely used platform for peer‐to‐peer exchange of knowledge and experiences regarding alcohol consumption, particularly while knowingly pregnant. Taken together, these findings help explain why individuals may turn to platforms like Reddit for pregnancy‐related advice—such spaces offer accessibility, immediacy, and a diversity of perspectives, alongside opportunities for emotional support and normalisation of experience. However, the relatively limited use of scientific evidence suggests an ongoing gap in effective knowledge translation.

Alcohol consumption is responsible for approximately 2.6 million deaths annually worldwide (Alpert et al., 2022; Varghese & Dakhode, 2022; World Health Organization, 2024) and is the leading risk f...

BACKGROUND Smartphone-based ecological momentary assessment (EMA) enables real-time capture of alcohol use in naturalistic settings, overcoming limitations of retrospective methods in terms of ecological validity and recall bias. However, EMA data quality depends on participant compliance, and non-compliance may be highest during drinking episodes themselves. Despite rapid growth in this field, a quantitative synthesis of compliance rates and their protocol-level predictors in smartphone-based alcohol EMA is lacking. OBJECTIVE This review aimed to (1) characterise EMA design and protocol features used in smartphone-based alcohol studies in adults; (2) evaluate reporting quality and transparency; (3) examine the extent to which participant experience outcomes were assessed; and (4) estimate pooled compliance and test whether prompting frequency and assessment duration predict it. METHODS Six databases (PubMed, Embase, Web of Science, PsycINFO, CINAHL, Scopus) were searched for studies published January 2015–March 2025 with an updated search in July 2026. Eligible studies involved adults using smartphone-based EMA to measure alcohol consumption and reported compliance data. Study quality was assessed using an adapted CREMAS checklist. Random-effects meta-analysis (REML) pooled compliance across studies with standardised data; meta-regression tested prompting frequency and assessment duration as moderators; publication bias was assessed via trim-and-fill. RESULTS Thirty-eight studies (6,594 participants) met inclusion criteria. EMA protocols predominantly used interval-contingent designs (1–9 prompts/day, 7–156 days). Reporting quality was moderate (mean CREMAS: 73.0%), with response latency and number of data collection waves the most poorly reported items. Only five studies formally assessed usability, acceptability, or burden. Pooled compliance across 21 studies was 81% (95% CI: 75%–87%; I²=94.36%). Prompting frequency significantly predicted lower compliance (b=−0.039, P=.016); assessment duration did not (P=.795). No evidence of publication bias was found. CONCLUSIONS Smartphone-based alcohol EMA achieves a pooled compliance rate of 81%, though heterogeneity was substantial. Prompt burden, more than study duration, shapes engagement. Standardized compliance reporting, routine participant experience assessment, and broader population representation are needed to advance the field.

Thanks to the popularity of smartphones with high-quality cameras and social media platforms, an exceptional amount of image data is generated and shared daily. This visual data can provide unprecedented insights into daily life and can be used to help answer research questions in psychology. However, the traditional methods used to analyze visual data are burdensome and are either time-intensive (e.g., content analysis) or require technical training (e.g., developing and training deep learning models). Zero-shot learning, where a pretrained model is used without any additional training, requires less technical expertise and may be a particularly attractive method for psychology researchers aiming to analyze image data. In this tutorial, we aim to provide an overview and step-by-step guide on how to analyze visual data with zero-shot learning. Specifically, we demonstrate how to use two popular models (Contrastive Language-Image Pretraining and Large Language and Vision Assistant) to identify a beverage in an image from a data set where we manipulated the type of beverage present, the setting, and the prominence of the beverage in the image (foreground, midground, background). To guide researchers through this process, we provide open code and data on GitHub and as a Google Colab notebook. Finally, we discuss how to interpret and report accuracy, how to create a validation data set, what steps need to be taken to implement the models with new data, and discuss future challenges and limitations of the method. To conclude, zero-shot learning requires less technical expertise and may be a particularly attractive method for psychology researchers aiming to analyze image data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Background and aims: Thanks to smart devices, social media and streaming platforms, watching videos, like movies or short social media clips, has become extremely popular. Alcohol portrayals are frequent in videos, yet their prevalence is difficult to quantify using traditional methods such as manual coding. Artificial intelligence (AI) offers a scalable solution to analyse large volumes of video images. This study aimed to compare the accuracy of three AI models in detecting alcohol presence in video images. Method: Experimental evaluation of three models: one supervised deep learning model (ABIDLA2) and two zero‐shot learning models (ZSL‐CLIP and ZSL‐LLaVA). The models were tested on datasets of video frames that had been annotated by researchers for whether they included alcohol or not. Three datasets of increasing complexity were used: (1) a Google/Bing image set of clearly visible alcohol and non‐alcohol images; (2) a set of movie frames manually annotated as containing or not containing alcohol; and (3) a contextually challenging set of movie frames from alcohol‐related settings (e.g. bars, parties) that may or may not include visible alcohol. Model performance was assessed using accuracy, unweighted average recall (UAR) and F1 score, representing the balance between precision and recall. Execution time per frame was also measured to evaluate computational efficiency. Results: Across the three datasets, ABIDLA2, ZSL‐CLIP and ZSL‐LLaVA achieved percentage accuracies of 90%, 91% and 92% on the Google/Bing images; 70%, 65% and 95% on the diverse movie‐scene dataset; and 67%, 63% and 94% on the most complex alcohol‐related dataset, respectively. In terms of execution time, ABIDLA2 processed a single frame the fastest (0.21 seconds), followed by ZSL‐LLaVA (0.45 seconds), while ZSL‐CLIP was the slowest (0.58 seconds). Conclusion: Automated artificial intelligence (AI) models appear to be able to detect alcohol imagery in videos at large scale with high accuracy and in near real time. Of the three AI models tested, ZSL‐LLaVA achieved the best balance between accuracy and speed. Offering a cost‐ and time‐efficient alternative to labour‐intensive manual coding, ZSL‐LLaVA could be used to monitor alcohol‐related visual content in videos across diverse media platforms.

Introduction: The growing accessibility of movies through streaming platforms has expanded audience reach, but also increases exposure to alcohol portrayals, which is an established risk factor for alcohol use. Hence, estimating alcohol depictions is important yet challenging due to the time and labor involved. Artificial Intelligence offers a scalable solution for analysing movie frames; however, processing every frame of a full‐length movie at 25 frames per second (fps) requires extensive computational resources. Thus, we aimed to test whether lower‐frequency sampling would affect the accuracy of alcohol exposure estimates. Methods: We analysed 20 feature‐length movies with varying known alcohol visibility and analysed each frame using zero‐shot predictions from a LLaVA v1.6 model (accuracy = 95%) as our baseline. We applied uniform downsampling from 25 fps (full‐framerate) to 1 fps and sparse interval sampling of 1 frame per N seconds ( N = 1,2,…,10), measuring both alcohol‐proportion estimates and execution time. To assess the sampling‐induced error, we computed the difference score, as the difference between sampled and full‐frame alcohol proportions. Results: A sampling frequency of 1 fps yielded an average difference score below 0.10 compared to the full‐frame analysis, while reducing execution time by 25‐fold. Error increased at sparser intervals, reaching a difference score of 0.46 at one frame per 10 s. Discussion and Conclusion: Reducing the sampling frequency from 25 to 1 fps resulted in only a minimal loss of accuracy but a substantial reduction in execution time. This finding supports 1 fps as a practical and scalable sampling frequency for large‐scale movie alcohol exposure estimation.