A feasibility study on identifying drinking-related contents in Facebook through mining heterogeneous data

ElTayeby, Omar; Eaglin, Todd; Abdullah, Malak; Burlinson, David; Dou, Wenwen; Yao, Lixia

doi:10.1177/1460458218798084

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…The second group of studies focuses on identifying substance-related posts on social media (e.g. ElTayeby et al, 2019;Roy et al, 2017). ElTayeby et al (2019) built a support vector machine model to predict whether a Facebook post is drinking-related or not, using the term frequency-inverse document frequency vector of the posts.…”

Section: Text Mining Social Media Data For the General Populationmentioning

confidence: 99%

“…The second group of studies focuses on identifying substance-related posts on social media (e.g. ElTayeby et al. , 2019; Roy et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…, 2017). ElTayeby et al. (2019) built a support vector machine model to predict whether a Facebook post is drinking-related or not, using the term frequency-inverse document frequency vector of the posts.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Rose et al, 2017) or other youth groups (e.g. ElTayeby et al, 2019;Hammond et al, 2018;Tran et al, 2018), not by YEH. As we discussed earlier, substance use is much higher among YEH and follows a unique pattern that needs a targeted approach to understanding and interventions.…”

mentioning

confidence: 99%

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Substance use and sentiment and topical tendencies: a study using social media conversations of youth experiencing homelessness

Deng

Barman‐Adhikari²,

Lee³

et al. 2022

ITP

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PurposeThis study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.Design/methodology/approachParticipants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.FindingsFacebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.Originality/valueDigital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.

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Section: Text Mining Social Media Data For the General Populationmentioning

confidence: 99%

“…The second group of studies focuses on identifying substance-related posts on social media (e.g. ElTayeby et al. , 2019; Roy et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Substance use and sentiment and topical tendencies: a study using social media conversations of youth experiencing homelessness

Deng

Barman‐Adhikari²,

Lee³

et al. 2022

ITP

View full text Add to dashboard Cite

show abstract

“…ElTayeby et al 1 developed and tested machine learning models using different types—text, image, and video—social media data (i.e. Facebook) to detect binge drinking (i.e.…”

mentioning

confidence: 99%

The rapid growth of intelligent systems in health and health care

Bian

Modave

2020

Health Informatics J

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Big data" in health is no longer a trendy buzzword but a reality. Abundant amount of data is amassed from a variety of sources including electronic health records (EHRs), administrative claims, wearables and mobile devices, genomic sequencing, medical imaging, even social media and the broad Internet, to name a few. Unfortunately, the bridge from being able to process these big data sets to knowledge discovery is still insurmountably challenging. With artificial intelligence being tested and implemented across the health and health care continuum, intelligent systems and their applications in health have the potential to transform the way how we learn from a wide range of data sources to ultimately improve health outcomes. Nevertheless, the increasing complexity of today's biomedical research requires more than traditional, single point-of-view approaches. Indeed, (big-)data-driven approaches that can reveal patterns in massive heterogeneous data sets and make clinically relevant predictions are becoming increasingly common in translational research. This Special Issue, "Intelligent Systems in Health," is an extension of a special track on the same topic in the 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE) hosted at the Université d'Artois in Arras, France. Five journal extensions have gone through additional peer-review and were accepted to this Special Issue. The five articles are showcases of the state-of-the-art research and development efforts that effectively apply intelligent algorithms and systems on a wide range of heterogeneous data sources. ElTayeby et al. 1 developed and tested machine learning models using different types-text, image, and video-social media data (i.e. Facebook) to detect binge drinking (i.e. excessive alcohol use that brings a person's blood alcohol concentration to 0.08 grams percent or above 2) among

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