Literature DB >> 33555267

Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review.

Emilie Karafillakis1, Sam Martin1, Clarissa Simas1, Kate Olsson2, Judit Takacs2,3, Sara Dada1, Heidi Jane Larson1,4.   

Abstract

BACKGROUND: Social media has changed the communication landscape, exposing individuals to an ever-growing amount of information while also allowing them to create and share content. Although vaccine skepticism is not new, social media has amplified public concerns and facilitated their spread globally. Multiple studies have been conducted to monitor vaccination discussions on social media. However, there is currently insufficient evidence on the best methods to perform social media monitoring.
OBJECTIVE: The aim of this study was to identify the methods most commonly used for monitoring vaccination-related topics on different social media platforms, along with their effectiveness and limitations.
METHODS: A systematic scoping review was conducted by applying a comprehensive search strategy to multiple databases in December 2018. The articles' titles, abstracts, and full texts were screened by two reviewers using inclusion and exclusion criteria. After data extraction, a descriptive analysis was performed to summarize the methods used to monitor and analyze social media, including data extraction tools; ethical considerations; search strategies; periods monitored; geolocalization of content; and sentiments, content, and reach analyses.
RESULTS: This review identified 86 articles on social media monitoring of vaccination, most of which were published after 2015. Although 35 out of the 86 studies used manual browser search tools to collect data from social media, this was time-consuming and only allowed for the analysis of small samples compared to social media application program interfaces or automated monitoring tools. Although simple search strategies were considered less precise, only 10 out of the 86 studies used comprehensive lists of keywords (eg, with hashtags or words related to specific events or concerns). Partly due to privacy settings, geolocalization of data was extremely difficult to obtain, limiting the possibility of performing country-specific analyses. Finally, 20 out of the 86 studies performed trend or content analyses, whereas most of the studies (70%, 60/86) analyzed sentiments toward vaccination. Automated sentiment analyses, performed using leverage, supervised machine learning, or automated software, were fast and provided strong and accurate results. Most studies focused on negative (n=33) and positive (n=31) sentiments toward vaccination, and may have failed to capture the nuances and complexity of emotions around vaccination. Finally, 49 out of the 86 studies determined the reach of social media posts by looking at numbers of followers and engagement (eg, retweets, shares, likes).
CONCLUSIONS: Social media monitoring still constitutes a new means to research and understand public sentiments around vaccination. A wide range of methods are currently used by researchers. Future research should focus on evaluating these methods to offer more evidence and support the development of social media monitoring as a valuable research design. ©Emilie Karafillakis, Sam Martin, Clarissa Simas, Kate Olsson, Judit Takacs, Sara Dada, Heidi Jane Larson. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 08.02.2021.

Entities:  

Keywords:  antivaccination movement; infodemiology; infoveillance; internet; media monitoring; research design; review; social listening; social media; vaccination; vaccination refusal

Year:  2021        PMID: 33555267      PMCID: PMC7899807          DOI: 10.2196/17149

Source DB:  PubMed          Journal:  JMIR Public Health Surveill        ISSN: 2369-2960


  86 in total

1.  Analysis of public concerns about influenza vaccinations by mining a massive online question dataset in Japan.

Authors:  Nobutoshi Nawa; Shigetoyo Kogaki; Kunihiko Takahashi; Hidekazu Ishida; Hiroki Baden; Shinichi Katsuragi; Jun Narita; Keiko Tanaka-Taya; Keiichi Ozono
Journal:  Vaccine       Date:  2016-01-14       Impact factor: 3.641

2.  Polarization of the vaccination debate on Facebook.

Authors:  Ana Lucía Schmidt; Fabiana Zollo; Antonio Scala; Cornelia Betsch; Walter Quattrociocchi
Journal:  Vaccine       Date:  2018-06-14       Impact factor: 3.641

3.  Facebook and Twitter vaccine sentiment in response to measles outbreaks.

Authors:  Michael S Deiner; Cherie Fathy; Jessica Kim; Katherine Niemeyer; David Ramirez; Sarah F Ackley; Fengchen Liu; Thomas M Lietman; Travis C Porco
Journal:  Health Informatics J       Date:  2017-11-17       Impact factor: 2.681

4.  Sentiment, Contents, and Retweets: A Study of Two Vaccine-Related Twitter Datasets.

Authors:  Elizabeth B Blankenship; Mary Elizabeth Goff; Jinging Yin; Zion Tsz Ho Tse; King-Wa Fu; Hai Liang; Nitin Saroha; Isaac Chun-Hai Fung
Journal:  Perm J       Date:  2018

5.  Human papillomavirus vaccination coverage on YouTube.

Authors:  Kevin A Ache; Lorraine S Wallace
Journal:  Am J Prev Med       Date:  2008-08-03       Impact factor: 5.043

6.  Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets.

Authors:  Jingcheng Du; Jun Xu; Hsingyi Song; Xiangyu Liu; Cui Tao
Journal:  J Biomed Semantics       Date:  2017-03-03

Review 7.  A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication.

Authors:  S Anne Moorhead; Diane E Hazlett; Laura Harrison; Jennifer K Carroll; Anthea Irwin; Ciska Hoving
Journal:  J Med Internet Res       Date:  2013-04-23       Impact factor: 5.428

8.  The Digital Distribution of Public Health News Surrounding the Human Papillomavirus Vaccination: A Longitudinal Infodemiology Study.

Authors:  L Meghan Mahoney; Tang Tang; Kai Ji; Jessica Ulrich-Schad
Journal:  JMIR Public Health Surveill       Date:  2015-03-18

9.  Comparing human papillomavirus vaccine concerns on Twitter: a cross-sectional study of users in Australia, Canada and the UK.

Authors:  Gilla K Shapiro; Didi Surian; Adam G Dunn; Ryan Perry; Margaret Kelaher
Journal:  BMJ Open       Date:  2017-10-05       Impact factor: 2.692

10.  What Drives Health Professionals to Tweet About #HPVvaccine? Identifying Strategies for Effective Communication.

Authors:  Philip M Massey; Alex Budenz; Amy Leader; Kara Fisher; Ann C Klassen; Elad Yom-Tov
Journal:  Prev Chronic Dis       Date:  2018-02-22       Impact factor: 2.830

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  10 in total

1.  Public reactions towards Covid-19 vaccination through twitter before and after second wave in India.

Authors:  Siddhi Mishra; Abhigya Verma; Kavita Meena; Rishabh Kaushal
Journal:  Soc Netw Anal Min       Date:  2022-05-31

2.  COVID-19 vaccine hesitancy: a social media analysis using deep learning.

Authors:  Serge Nyawa; Dieudonné Tchuente; Samuel Fosso-Wamba
Journal:  Ann Oper Res       Date:  2022-06-16       Impact factor: 4.820

3.  A Multi-platform Approach to Monitoring Negative Dominance for COVID-19 Vaccine-Related Information Online.

Authors:  Paola Pascual-Ferrá; Neil Alperstein; Daniel J Barnett
Journal:  Disaster Med Public Health Prep       Date:  2021-05-03       Impact factor: 1.385

4.  Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis.

Authors:  Iain Cruickshank; Tamar Ginossar; Jason Sulskis; Elena Zheleva; Tanya Berger-Wolf
Journal:  J Med Internet Res       Date:  2021-12-03       Impact factor: 5.428

5.  Young adults' preferences for influenza vaccination campaign messages: Implications for COVID-19 vaccine intervention design and development.

Authors:  Zhaohui Su; Dean McDonnell; Jun Wen; Ali Cheshmehzangi; Junaid Ahmad; Edmund Goh; Xiaoshan Li; Sabina Šegalo; Michael Mackert; Yu-Tao Xiang; Peiyu Wang
Journal:  Brain Behav Immun Health       Date:  2021-04-17

6.  Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning.

Authors:  Andrzej Jarynowski; Alexander Semenov; Mikołaj Kamiński; Vitaly Belik
Journal:  J Med Internet Res       Date:  2021-11-29       Impact factor: 5.428

7.  Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets.

Authors:  Beatrice Portelli; Simone Scaboro; Roberto Tonino; Emmanuele Chersoni; Enrico Santus; Giuseppe Serra
Journal:  J Med Internet Res       Date:  2022-05-13       Impact factor: 7.076

8.  Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model.

Authors:  Susan Cheatham; Per E Kummervold; Lorenza Parisi; Barbara Lanfranchi; Ileana Croci; Francesca Comunello; Maria Cristina Rota; Antonietta Filia; Alberto Eugenio Tozzi; Caterina Rizzo; Francesco Gesualdo
Journal:  Front Public Health       Date:  2022-07-29

9.  Effectiveness of Social Video Platforms in Promoting COVID-19 Vaccination Among Youth: A Content-Specific Analysis of COVID-19 Vaccination Topic Videos on Bilibili.

Authors:  Hao Gao; Hao Yin; Li Peng; Han Wang
Journal:  Risk Manag Healthc Policy       Date:  2022-09-01

10.  The dramatic increase in anti-vaccine discourses during the COVID-19 pandemic: a social network analysis of Twitter.

Authors:  Nihal Durmaz; Engin Hengirmen
Journal:  Hum Vaccin Immunother       Date:  2022-02-03       Impact factor: 3.452

  10 in total

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