ArticleProceedings of the ACM on human-computer interaction2017
High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data.
Article in Proceedings of the ACM on human-computer interaction, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Mental health in digital microsystems across three Asian Reddit communities.Scientific reports · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Multiplatform characterization of online permanent female contraception discussion among social media users: analysis of Twitter and Reddit.Reproductive health · 2025Article
- Global prevalence and content of information about alcohol use as a cancer risk factor on Twitter.Preventive medicine · 2023Article
- Micro-temporal analyses of crime related to alcohol outlets: A comparison of outcomes over weekday, weekend, daytime and nighttime hours.Drug and alcohol review · 2023Article
- VGLM proportional odds model to infer hosts' Airbnb performance.Quality & quantity · 2022Article
- Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours.Public health nutrition · 2021Review
- A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric Hospitalizations.Proceedings of the ACM on human-computer interaction · 2021Article
- Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.JMIR public health and surveillance · 2020Review
- Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis.International journal of environmental research and public health · 2020Article
- Role of the Built and Online Social Environments on Expression of Dining on Instagram.International journal of environmental research and public health · 2020Article
- Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data.Yearbook of medical informatics · 2019Article
- Hookah Use Among College Students: Recent Use, Knowledge of Health Risks, Attitude and Reasons for Use.Journal of community health · 2018Article
- Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes.Proceedings of the ACM on human-computer interaction · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Understanding tobacco- and alcohol-related behavioral patterns is critical for uncovering risk factors and potentially designing targeted social computing intervention systems. Given that we make choices multiple times per day, hourly and daily patterns are critical for better understanding behaviors. Here, we combine natural language processing, machine learning and time series analyses to assess Twitter activity specifically related to alcohol and tobacco consumption and their sub-daily, daily and weekly cycles. Twitter self-reports of alcohol and tobacco use are compared to other data streams available at similar temporal resolution. We assess if discussion of drinking by inferred underage versus legal age people or discussion of use of different types of tobacco products can be differentiated using these temporal patterns. We find that time and frequency domain representations of behaviors on social media can provide meaningful and unique insights, and we discuss the types of behaviors for which the approach may be most useful.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.