High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data

Huang, Tom; Elghafari, Anas; Relia, Kunal; Chunara, Rumi

doi:10.1145/3134689

Cited by 24 publications

(21 citation statements)

References 68 publications

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“…Based on surveillance-like data from Twitter, strategies may be implemented encouraging awareness of the negative consequences of hazardous drinking, delivering a preventive message about BD. Likelihood of targeted behaviour patterns and the identification of target groups or places at high risk for unhealthy behaviours may represent key, high-resolution information to inform relevant stakeholders responsible for preventive policies [63]. Specifically, detecting real users reporting BD and alcohol-related risky behaviours on social media appears as a complex but promising approach deserving a deeper investigation in future studies.…”

Section: Preventive Implications and Conclusionmentioning

confidence: 99%

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis

Crocamo

Viviani

Bartoli

et al. 2020

IJERPH

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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.

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Section: Preventive Implications and Conclusionmentioning

confidence: 99%

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis

Crocamo

Viviani

Bartoli

et al. 2020

IJERPH

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“…Of the twenty-two papers discussed in this section, three are focussed on opioid abuse [35,41,42], eight on tobacco and marijuana use [6,12,13,40,43,45,46,49], one on alcohol abuse [36], and one on the street drug, mephedrone [44]. Twitter is the most popular source of data (18 papers) [6,11,12,[35][36][37][38][39][40][41][42][43][44][45][46][47][48][49], with Reddit [11][12][13], and online health communities [12,13], both represented. Supervised machine learning (8 papers -all utilising Twitter data) and unsupervised machine learning (11 papers) were both evident in the reviewed papers, with classical machine learning approaches more common than modern neural-network-based approaches (17 and 2 papers, respectively).…”

Section: Substance Abusementioning

confidence: 99%

“…In addition to these shifts in tobacco use, there have also been substantial changes in the regulation of marijuana products, particularly in the US context, and these changes have led -it has been suggested 6 https://www.cdc.gov/drugoverdose/data/ statedeaths.html [89] -to an increase in marijuana use [90]. Given these public health concerns, using NLP to investigate tobacco, e-cigarette, and marijuana use, has become an active research area, especially to classify discussions [6,12,43,45,46] or to determine whether a particular user is above or below 21 years of age [40]. Reported findings included evidence that Twitter users frequently discussed ways in which e-cigarettes can be used in the workplace in a bid to circumvent smoking bans [43], and evidence that hookah was discussed more frequently at the weekend, indicating its use is associated with leisure activities, while reported tobacco use tends to be more consistent across the week [40].…”

Section: Tobacco E-cigarette and Marijuana Use And Abusementioning

confidence: 99%

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Conway

Chapman

2019

Yearb Med Inform

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Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.

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“…Online social communities constitute a significant presence in our lives. Researchers and practitioners are using online data to illuminate many aspects of life including health, politics and culture, based on what people post [1][2][3][4][5]. For example, some studies have focused on recipe websites and shown how food names in online recipes can be a proxy for consumption and dietary patterns of individuals [6].…”

Section: Introductionmentioning

confidence: 99%

Role of the Built and Online Social Environments on Expression of Dining on Instagram

Mhasawade

Elghafari

Duncan

et al. 2020

IJERPH

Self Cite

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Online social communities are becoming windows for learning more about the health of populations, through information about our health-related behaviors and outcomes from daily life. At the same time, just as public health data and theory has shown that aspects of the built environment can affect our health-related behaviors and outcomes, it is also possible that online social environments (e.g., posts and other attributes of our online social networks) can also shape facets of our life. Given the important role of the online environment in public health research and implications, factors which contribute to the generation of such data must be well understood. Here we study the role of the built and online social environments in the expression of dining on Instagram in Abu Dhabi; a ubiquitous social media platform, city with a vibrant dining culture, and a topic (food posts) which has been studied in relation to public health outcomes. Our study uses available data on user Instagram profiles and their Instagram networks, as well as the local food environment measured through the dining types (e.g., casual dining restaurants, food court restaurants, lounges etc.) by neighborhood. We find evidence that factors of the online social environment (profiles that post about dining versus profiles that do not post about dining) have different influences on the relationship between a user’s built environment and the social dining expression, with effects also varying by dining types in the environment and time of day. We examine the mechanism of the relationships via moderation and mediation analyses. Overall, this study provides evidence that the interplay of online and built environments depend on attributes of said environments and can also vary by time of day. We discuss implications of this synergy for precisely-targeting public health interventions, as well as on using online data for public health research.

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High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data

Cited by 24 publications

References 68 publications

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

Role of the Built and Online Social Environments on Expression of Dining on Instagram

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