Usage of Scientific References in MMR Vaccination Debates on Twitter

Addawood, Aseel

doi:10.1109/asonam.2018.8508385

Cited by 11 publications

(18 citation statements)

References 48 publications

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“…The first study identified on social media monitoring around vaccination was published in 2006 [ 26 ], with an increasing number of studies published yearly since then. Most studies analyzed online discourse on Twitter (n=42) [ 8 , 27 - 67 ], YouTube (n=12) [ 68 - 79 ], Facebook (n=11) [ 80 - 90 ], and online forums (eg, babytree, mothering.com, mumsnet, KaksPlus; n=9) [ 26 , 91 - 98 ]. A diversification of social media platforms can be observed in recent years, with studies of platforms such as Pinterest (n=1) [ 99 ], Weibo (n=1) [ 100 ], Reddit (n=1) [ 101 ], or Yahoo!…”

Section: Resultsmentioning

confidence: 99%

“…The main objective of the majority of studies in this review (55/86, 64%) was to better understand how vaccination is portrayed on social media, whether through the analysis of online discourse or sentiments, or by looking at how information is produced, shared, and engaged with [ 8 , 26 , 27 , 30 , 33 , 39 , 42 - 44 , 46 - 51 , 53 - 56 , 58 - 62 , 68 - 80 , 82 - 88 , 90 - 94 , 98 , 99 , 101 , 102 , 105 , 106 ].…”

Section: Resultsmentioning

confidence: 99%

“…Forty-nine studies used either social media application program interfaces (APIs) (n=24) [ 29 , 31 - 33 , 35 , 37 - 41 , 43 , 45 , 48 , 54 , 56 - 58 , 65 - 67 , 82 , 84 , 87 , 88 , 101 , 103 ], automated monitoring tools (n=20) [ 27 , 36 , 42 , 44 , 46 , 47 , 49 - 53 , 55 , 59 , 61 , 62 , 64 , 105 , 107 , 108 , 110 ], or a combination of both (n=5) [ 8 , 28 , 32 , 34 , 63 ] to extract data from social media platforms. The term API refers to a software intermediary that allows two apps to talk to each other [ 112 ].…”

Section: Resultsmentioning

confidence: 99%

“…The difficulty in finding the correct self-assigned demographic of users, and whether they are real users or automated bots, makes the findings less generalizable. Finally, a large number (n=45) of studies using either APIs or automated software focused on Twitter due to the ease of access given by the platform to its data stream compared to other platforms, which may give a skewed perspective of social media attitudes toward vaccines [ 8 , 27 - 59 , 61 - 67 , 105 , 107 , 108 , 110 ].…”

Section: Resultsmentioning

confidence: 99%

“…Simple search strategies with one to three keywords were most common. Only 10 studies used more extensive search strategies with Boolean operators to link keywords (eg, AND, OR) or truncations to identify words with different endings (eg, vaccin*) [ 27 , 29 , 42 , 43 , 50 , 51 , 66 , 101 , 105 , 108 ]. Although simpler search strategies were perceived as a limitation by some [ 8 , 71 , 73 ], no data were available on the accuracy of short strategies as opposed to longer and more complex search strategies.…”

Section: Resultsmentioning

confidence: 99%

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

Karafillakis¹,

Martin²,

Simas³

et al. 2021

JMIR Public Health Surveill

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

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

Karafillakis¹,

Martin²,

Simas³

et al. 2021

JMIR Public Health Surveill

View full text Add to dashboard Cite

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Tracking the structure and sentiment of vaccination discussions on Mumsnet

Silva,

Skeva,

House

et al. 2023

Soc. Netw. Anal. Min.

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Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a UK-based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets—small connected network subgraphs—we show how the topological structure of vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment toward vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis.

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