Representing vaccine misinformation using ontologies

Amith, Muhammad; Tao, Cui

doi:10.1186/s13326-018-0190-0

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…This approach to defining vaccine misinformation is in line with the academic literature on this topic. For example, Amith and Tao (2018) developed the Vaccine Misinformation Ontology (VAXMO) where they placed the Anti-Vaccination Information concept as a subclass of the main Misinformation class. In turn, the Anti-Vaccination Information concept included the following subclasses: Vaccine Inefficacy, Alternative Medicine, Civil Liberties, Conspiracy Theories, Falsehoods, and Ideological.…”

Section: Introductionmentioning

confidence: 99%

From Facebook to YouTube: The Potential Exposure to COVID-19 Anti-Vaccine Videos on Social Media

Gruzd

Abul-Fottouh

Song

et al. 2023

Social Media + Society

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This article examines the role of Facebook and YouTube in potentially exposing people to COVID-19 vaccine–related misinformation. Specifically, to study the potential level of exposure, the article models a uni-directional information-sharing pathway beginning when a Facebook user encounters a vaccine-related post with a YouTube video, follows this video to YouTube, and then sees a list of related videos automatically recommended by YouTube. The results demonstrate that despite the efforts by Facebook and YouTube, COVID-19 vaccine–related misinformation in the form of anti-vaccine videos propagates on both platforms. Because of these apparent gaps in platform-led initiatives to combat misinformation, public health agencies must be proactive in creating vaccine promotion campaigns that are highly visible on social media to overtake anti-vaccine videos’ prominence in the network. By examining related videos that a user potentially encounters, the article also contributes practical insights to identify influential YouTube channels for public health agencies to collaborate with on their public service announcements about the importance of vaccination programs and vaccine safety.

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Section: Introductionmentioning

confidence: 99%

From Facebook to YouTube: The Potential Exposure to COVID-19 Anti-Vaccine Videos on Social Media

Gruzd

Abul-Fottouh

Song

et al. 2023

Social Media + Society

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“…The purpose of this step was to build a gold standard corpus (ie, Reddit posts with their expert-assigned labels) that was used for the training and evaluation of the automated ML algorithms. The definition of vaccine misinformation was largely informed by the Antivaccination Information class of the Vaccine Misinformation Ontology (VAXMO) [ 31 ], a formal ontology to describe vaccine misinformation. Within the VAXMO, the Antivaccination Information class includes several subclasses, such as Vaccine inefficacy , Alternative medicine , Civil liberties , Conspiracy theories , Falsehoods , and Ideological .…”

Section: Methodsmentioning

confidence: 99%

Using Machine Learning–Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions

Preston

Sun

et al. 2021

J Med Internet Res

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Background The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)–based methods. Methods Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

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“…A longer term goal of our work is to find out and analyze the claims that people make when they engage in the debate. Potential applications of both the analysis and the annotated data would be, for example, detecting misinformation about vaccinations to populate ontologies (Amith and Tao, 2018) or training agents (Rosenfeld and Kraus, 2016) to persuade hesitant users to vaccinate.…”

Section: Introductionmentioning

confidence: 99%

Annotating Claims in the Vaccination Debate

Torsi¹,

Morante

2018

Proceedings of the 5th Workshop on Argument Mining

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In this paper we present annotation experiments with three different annotation schemes for the identification of argument components in texts related to the vaccination debate. Identifying claims about vaccinations made by participants in the debate is of great societal interest, as the decision to vaccinate or not has impact in public health and safety. Since most corpora that have been annotated with argumentation information contain texts that belong to a specific genre and have a well defined argumentation structure, we needed to adjust the annotation schemes to our corpus, which contains heterogeneous texts from the Web. We started with a complex annotation scheme that had to be simplified due to low IAA. In our final experiment, which focused on annotating claims, annotators reached 57.3% IAA.

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Representing vaccine misinformation using ontologies

Cited by 12 publications

References 31 publications

From Facebook to YouTube: The Potential Exposure to COVID-19 Anti-Vaccine Videos on Social Media

From Facebook to YouTube: The Potential Exposure to COVID-19 Anti-Vaccine Videos on Social Media

Using Machine Learning–Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions

Annotating Claims in the Vaccination Debate

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