Literature DB >> 23790998

A practical approach for content mining of Tweets.

Sunmoo Yoon1, Noémie Elhadad2, Suzanne Bakken3.   

Abstract

Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.
Copyright © 2013 American Journal of Preventive Medicine.

Entities:  

Mesh:

Year:  2013        PMID: 23790998      PMCID: PMC3694275          DOI: 10.1016/j.amepre.2013.02.025

Source DB:  PubMed          Journal:  Am J Prev Med        ISSN: 0749-3797            Impact factor:   5.043


  13 in total

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4.  Efficacy and use of an internet-delivered computer-tailored lifestyle intervention, targeting saturated fat intake, physical activity and smoking cessation: a randomized controlled trial.

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Journal:  Ann Behav Med       Date:  2008-03-25

5.  Infodemiology and infoveillance tracking online health information and cyberbehavior for public health.

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Journal:  Am J Prev Med       Date:  2011-05       Impact factor: 5.043

Review 6.  Strategies for analyzing ecological momentary assessment data.

Authors:  J E Schwartz; A A Stone
Journal:  Health Psychol       Date:  1998-01       Impact factor: 4.267

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Journal:  Worldviews Evid Based Nurs       Date:  2012-03-20       Impact factor: 2.931

9.  Making sense of the data explosion: the promise of systems science.

Authors:  Patricia L Mabry
Journal:  Am J Prev Med       Date:  2011-05       Impact factor: 5.043

10.  Social desirability bias in self-reported dietary, physical activity and weight concerns measures in 8- to 10-year-old African-American girls: results from the Girls Health Enrichment Multisite Studies (GEMS).

Authors:  Lisa M Klesges; Tom Baranowski; Bettina Beech; Karen Cullen; David M Murray; Jim Rochon; Charlotte Pratt
Journal:  Prev Med       Date:  2004-05       Impact factor: 4.018

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  18 in total

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3.  Prevalence of Marijuana-Related Traffic on Twitter, 2012-2013: A Content Analysis.

Authors:  Leah Thompson; Frederick P Rivara; Jennifer M Whitehill
Journal:  Cyberpsychol Behav Soc Netw       Date:  2015-06

4.  Twitter-Based Detection of Illegal Online Sale of Prescription Opioid.

Authors:  Tim K Mackey; Janani Kalyanam; Takeo Katsuki; Gert Lanckriet
Journal:  Am J Public Health       Date:  2017-10-19       Impact factor: 9.308

5.  What Can We Learn About Mental Health Needs From Tweets Mentioning Dementia on World Alzheimer's Day?

Authors:  Sunmoo Yoon
Journal:  J Am Psychiatr Nurses Assoc       Date:  2016-11       Impact factor: 2.385

6.  A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.

Authors:  Caitlin Dreisbach; Theresa A Koleck; Philip E Bourne; Suzanne Bakken
Journal:  Int J Med Inform       Date:  2019-02-20       Impact factor: 4.046

7.  Tweets About Acute Nicotine Toxicity Due to e-Liquid Exposure.

Authors:  Sarah Trigger; Moronke Akinso Johnson; Anh Nguyen Zarndt; Danielle K Hill
Journal:  Tob Regul Sci       Date:  2021-01

8.  A new source of data for public health surveillance: Facebook likes.

Authors:  Steven Gittelman; Victor Lange; Carol A Gotway Crawford; Catherine A Okoro; Eugene Lieb; Satvinder S Dhingra; Elaine Trimarchi
Journal:  J Med Internet Res       Date:  2015-04-20       Impact factor: 5.428

9.  Discovering health topics in social media using topic models.

Authors:  Michael J Paul; Mark Dredze
Journal:  PLoS One       Date:  2014-08-01       Impact factor: 3.240

10.  Web Conversations About Complementary and Alternative Medicines and Cancer: Content and Sentiment Analysis.

Authors:  Mauro Mazzocut; Ivana Truccolo; Marialuisa Antonini; Fabio Rinaldi; Paolo Omero; Emanuela Ferrarin; Paolo De Paoli; Carlo Tasso
Journal:  J Med Internet Res       Date:  2016-06-16       Impact factor: 5.428

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