Skip to content

Using Artificial Intelligence to estimate how common alcohol references and alcohol-related brand references are among influencers on Instagram

Oct 2026

Abstract

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.

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#small language model Dataset Open access Oct 2026

Socratic guiding questions in synthetic arithmetic data: matched LoRA runs (revision v2)

Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...

O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al. · 465 citations
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.