Detecting discussion communities on vaccination in twitter

Bello-Orgaz, Gema; Hernández-Castro, Julio; Camacho, David

doi:10.1016/j.future.2016.06.032

Cited by 88 publications

(53 citation statements)

References 47 publications

(64 reference statements)

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“…The other community, "opinion group," is defined by user's attributes (opinions). It is similar to the concepts of member-based (characteristics of members) community and interaction-based (density of interactions) community in community detection algorithms (Bello-Orgaz et al, 2017). Investigating how two kinds of community interact with each other has been seen more often in political communication research and not in health communication.…”

Section: Contribution Discussion and Future Workmentioning

confidence: 96%

Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter

Yuan

Schuchard

Crooks

2019

Social Media + Society

107

118

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Many states in the United States allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine can have direct consequences in public health. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ efforts of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining momentum. Studies have shown that bots on social media (i.e., social bots) can influence opinion trends by posting a substantial number of automated messages. The research presented here investigates the communication patterns of anti- and pro-vaccine users and the role of bots in Twitter by studying a retweet network related to MMR vaccine after the 2015 California Disneyland measles outbreak. We first classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups using supervised machine learning. We discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group. In addition, our bot analysis discovers that 1.45% of the corpus users were identified as likely bots which produced 4.59% of all tweets within our dataset. We further found that bots display hyper-social tendencies by initiating retweets at higher frequencies with users within the same opinion group. The article concludes that highly clustered anti-vaccine Twitter users make it difficult for health organizations to penetrate and counter opinionated information while social bots may be deepening this trend. We believe that these findings can be useful in developing strategies for health communication of vaccination.

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Section: Contribution Discussion and Future Workmentioning

confidence: 96%

Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter

Yuan

Schuchard

Crooks

2019

Social Media + Society

107

118

View full text Add to dashboard Cite

show abstract

“…Other research focuses on detecting discussion communities on vaccination in Twitter [18] or analysing semantic networks [19] to identify the most relevant and influential users as well as to better understand complex drivers of vaccine hesitancy for public health communication. Tangherlini et al [20] explore what can be learned about the vaccination discussion from the realm of "mommy blogs": parents posting messages about children's health care on forum websites.…”

Section: Introductionmentioning

confidence: 99%

Monitoring stance towards vaccination in twitter messages

Kunneman

Lambooij

Wong

et al. 2020

BMC Med Inform Decis Mak

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Background: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms. Results: We found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, outperforming a rule-based sentiment analysis baseline that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. Conclusion: The outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions.

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“…The present work was also inspired by works addressing the social interplay of other health-related issues. For example, Bello et al [19] explored a new methodology to detect, track and categorise the discussion of vaccination communities. This work explored different community detection algorithms to analyse the different vaccination groups based on their retweets.…”

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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Detecting discussion communities on vaccination in twitter

Cited by 88 publications

References 47 publications

Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter

Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter

Monitoring stance towards vaccination in twitter messages

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

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