Literature DB >> 29154103

Lung Cancer Messages on Twitter: Content Analysis and Evaluation.

Jeannette Sutton1, Sarah C Vos2, Michele K Olson2, Chelsea Woods3, Elisia Cohen4, C Ben Gibson5, Nolan Edward Phillips5, Jamie L Studts6, Jan M Eberth7, Carter T Butts8.   

Abstract

PURPOSE: The aim of this project was to describe and evaluate the levels of lung cancer communication across the cancer prevention and control continuum for content posted to Twitter during a 10-day period (September 30 to October 9) in 2016.
METHODS: Descriptive and inferential statistics were used to identify relationships between tweet characteristics in lung cancer communication on Twitter and user-level data. Overall, 3,000 tweets published between September 30 and October 9 were assessed by a team of three coders. Lung cancer-specific tweets by user type (individuals, media, and organizations) were examined to identify content and structural message features. The study also assessed differences by user type in the use of hashtags, directed messages, health topic focus, and lung cancer-specific focus across the cancer control continuum.
RESULTS: Across the universe of lung cancer tweets, the majority of tweets focused on treatment and the use of pharmaceutical and research interventions, followed by awareness and prevention and risk topics. Among all lung cancer tweets, messages were most consistently tweeted by individual users, and personal behavioral mobilizing cues to action were rare.
CONCLUSIONS: Lung cancer advocates, as well as patient and medical advocacy organizations, with an interest in expanding the reach and effectiveness of social media efforts should monitor the topical nature of public tweets across the cancer continuum and consider integrating cues to action as a strategy to increase engagement and behavioral activation pertaining to lung cancer reduction efforts.
Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Social media; Twitter messaging; content analysis; lung cancer

Mesh:

Year:  2017        PMID: 29154103     DOI: 10.1016/j.jacr.2017.09.043

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  15 in total

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