Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease

Tang, Lu; Bie, Bijie; Zhi, Degui

doi:10.1016/j.ajic.2018.05.019

Cited by 54 publications

(41 citation statements)

References 17 publications

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“…These reports, or discussions surrounding the reports, were a focus of Twitter discourse. This is consistent with research that found people shared news updates, medical information, and scientific knowledge during a previous measles outbreak (Tang et al, 2018). Although the cluster is small, especially in comparison to the HPV/cancer prevention cluster, the MMR autism debate is still a topic of discussion on Twitter.…”

Section: Discussionsupporting

confidence: 84%

“…Deriving from the cognitive paradigm and the linguistic theory of frame semantics (Fillmore, 1982), SNA can highlight the most salient information in a body of text by assessing the networks that emerge. Previous studies have used this approach with other socio-scientific subjects, such as the representation of the HPV vaccine online (Ruiz and Barnett, 2015), genetically engineered foods (Jiang et al, 2018), and Twitter posts during an emerging outbreak (Tang et al, 2018). A better understanding of the eco-system for childhood vaccine themes on social media can help vaccine promotion by targeting what information social media discourse lacks.…”

Section: Themes Of Vaccine Discourse On Social Mediamentioning

confidence: 99%

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Exploring childhood vaccination themes and public opinions on Twitter: A semantic network analysis

Featherstone

Ruiz

Barnett

et al. 2020

Telematics and Informatics

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During the summer of 2018, a measles outbreak hit Europe, resulting in infections and deaths. Reports link the outbreak to increasing vaccine hesitancy fueled partly by anti-vaccine information propagated on social media. This study examines childhood vaccination themes on Twitter using semantic network analysis. Results showed the most prominent theme was HPV vaccination as a disease preventative. The MMR vaccine autism link was the second biggest theme, followed by measles outbreak rates. These themes reflect public opinions on popular vaccine topics and current issues. Results suggest social media may be an effective place to promote childhood vaccines.

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Section: Discussionsupporting

confidence: 84%

Section: Themes Of Vaccine Discourse On Social Mediamentioning

confidence: 99%

Exploring childhood vaccination themes and public opinions on Twitter: A semantic network analysis

Featherstone

Ruiz

Barnett

et al. 2020

Telematics and Informatics

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“…Past research indicates that the types of social media content can vary over the course of an outbreak and political framing of social media messages in past outbreaks appeared more frequently during the "maintenance" stage of outbreak response, which occurs during peak activity of response efforts prior to the resolution of an outbreak [39]. Ebola response activities within the US and the time period of interest in this study corresponded with the lead up to US midterm elections (which occurred Nov 4, 2014).…”

Section: Intersection Of Misinformation Political Content and Discordmentioning

confidence: 99%

Misinformation and the US Ebola communication crisis: analyzing the veracity and content of social media messages related to a fear-inducing infectious disease outbreak

2020

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Background The Ebola communication crisis of 2014 generated widespread fear and attention among Western news media, social media users, and members of the United States (US) public. Health communicators need more information on misinformation and the social media environment during a fear-inducing disease outbreak to improve communication practices. The purpose of this study was to describe the content of Ebola-related tweets with a specific focus on misinformation, political content, health related content, risk framing, and rumors. Methods We examined tweets from a random 1% sample of all tweets published September 30th - October 30th, 2014, filtered for English-language tweets mentioning “Ebola” in the content or hashtag, that had at least 1 retweet (N = 72,775 tweets). A randomly selected subset of 3639 (5%) tweets were evaluated for inclusion. We analyzed the 3113 tweets that meet inclusion criteria using public health trained human coders to assess tweet characteristics (joke, opinion, discord), veracity (true, false, partially false), political context, risk frame, health context, Ebola specific messages, and rumors. We assessed the proportion of tweets with specific content using descriptive statistics and chi-squared tests. Results Of non-joke tweets, 10% of Ebola-related tweets contained false or partially false information. Twenty-five percent were related to politics, 28% contained content that provoked reader response or promoted discord, 42% contained risk elevating messages and 72% were related to health. The most frequent rumor mentioned focused on government conspiracy. When comparing tweets with true information to tweets with misinformation, a greater percentage of tweets with misinformation were political in nature (36% vs 15%) and contained discord-inducing statements (45% vs 10%). Discord-inducing statements and political messages were both significantly more common in tweets containing misinformation compared with those without(p < 0.001). Conclusions Results highlight the importance of anticipating politicization of disease outbreaks, and the need for policy makers and social media companies to build partnerships and develop response frameworks in advance of an event. While each public health event is different, our findings provide insight into the possible social media environment during a future epidemic and could help optimize potential public health communication strategies.

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“…For example, the study of Kim et al [20] identified core keywords and sub-topic groups of studies in domestic and geriatric frailty syndrome by analysing and clustering keywords based on the co-occurrence frequency and centrality analysis. The work of Thag et al [21] performed an unsupervised analysis to explore how the word frequencies and their co-occurrence can be applied for assessing the frames used in social media discussions. The authors focused their work in the emerging infectious diseases in the United States and based their study in the creation, interpretation, and quantification of semantic networks.…”

Section: Related Workmentioning

confidence: 99%

Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter

Pérez-Rodríguez

Pérez-Pérez

Fdez-Riverola

et al. 2020

Future Generation Computer Systems

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In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients' experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients.

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Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease

Cited by 54 publications

References 17 publications

Exploring childhood vaccination themes and public opinions on Twitter: A semantic network analysis

Exploring childhood vaccination themes and public opinions on Twitter: A semantic network analysis

Misinformation and the US Ebola communication crisis: analyzing the veracity and content of social media messages related to a fear-inducing infectious disease outbreak

Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter

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