Literature DB >> 32970973

Identifying #addiction concerns on twitter during the COVID-19 pandemic: A text mining analysis.

Elizabeth M Glowacki1, Gary B Wilcox2, Joseph B Glowacki3.   

Abstract

BACKGROUND: The 2019 Novel Coronavirus (COVID-19) is responsible for thousands of deaths and hospitalizations. To curb the spread of this highly transmissible disease, governments enacted protective guidelines for its citizens, including social distancing and stay-at-home orders. These restrictions on social interactions can be especially problematic for individuals managing or recovering from addiction given that treatment often involves access to services and resources that became limited or even unavailable at this time. Social media sites like Twitter serve as a space for users to post questions and concerns about timely topics and allow for researchers to track common themes among the public. The goal of this study was to identify how the public was discussing addiction on Twitter during the COVID pandemic.
Methods: We performed a text mining analysis to analyze tweets that contained "addiction" and "covid" to capture posts from the public that illustrated comments and concerns about addiction during the COVID-19 pandemic. We report on 3,301 tweets captured between January 31 and April 23, 2020. The study was conducted in the United States, but contained tweets from multiple countries.
Results: The most prevalent topics had to do with services offered by Acadia Healthcare and Serenity Healthcare Centers, attempts to manage time while home, difficulties of coping with alcoholism amidst rising sales of alcohol, and attention to ongoing health crises (e.g.,., opioids, vaping). Additional topics included affordable telehealth services, research from France on the relationship between nicotine and COVID-19, concerns about gambling addiction, and changing patterns in substance misuse as drug availability varies. Conclusions: Analyzing Twitter content enables health professionals to identify the public's concerns about addiction during the COVID-19 pandemic. Findings from text mining studies addressing timely health topics can serve as preliminary analyses for building more comprehensive models, which can then be used to generate recommendations for the larger public and inform policy.

Entities:  

Keywords:  Addiction; COVID-19; health communication; social media; text mining analysis

Year:  2020        PMID: 32970973     DOI: 10.1080/08897077.2020.1822489

Source DB:  PubMed          Journal:  Subst Abus        ISSN: 0889-7077            Impact factor:   3.716


  4 in total

1.  Associations between heavy drinker's alcohol-related social media exposures and personal beliefs and attitudes regarding alcohol treatment.

Authors:  Alex M Russell; Tzung-Shiang Ou; Brandon G Bergman; Philip M Massey; Adam E Barry; Hsien-Chang Lin
Journal:  Addict Behav Rep       Date:  2022-05-18

2.  Treatment seeking for alcohol-related issues during the COVID-19 pandemic: An analysis of an addiction-specialized psychiatric treatment facility.

Authors:  Mitchell J Andersson; Anders Håkansson
Journal:  Heliyon       Date:  2022-07-14

3.  Gender differences in lifetime and current use of online support for recovery from alcohol use disorder.

Authors:  Paul A Gilbert; Elizabeth Saathoff; Alex M Russell; Grant Brown
Journal:  Alcohol Clin Exp Res       Date:  2022-06-19       Impact factor: 3.928

4.  Twitter sentiment analysis from Iran about COVID 19 vaccine.

Authors:  Zahra Bokaee Nezhad; Mohammad Ali Deihimi
Journal:  Diabetes Metab Syndr       Date:  2021-12-13
  4 in total

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