The Effects of an Informational Intervention on Attention to Anti-Vaccination Content on YouTube

Abstract: The spread of misinformation related to health, especially vaccination, is a potential contributor to myriad public health problems. This misinformation is frequently spread through social media. Recently, social media companies have intervened in the dissemination of misinformation regarding vaccinations. In the current study we focus on YouTube. Recognizing the extent of the problem, YouTube implemented an informational modification that affected many videos related to vaccination beginning in February 2019. We collect original data and analyze the effects of this intervention on video viewership. We find that this informational intervention reduced traffic to the affected videos, both overall, and in comparison to a carefully-matched set of control videos that did not receive the informational modification.

07/06/2021

CoVaxxy: A Collection of English-Language Twitter Posts About COVID-19 Vaccines

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John Bollenbacher, David Axelrod, Niklas Loynes, Christopher Torres-Lugo, Kai-Cheng Yang, Filippo Menczer, John Bryden

Keywords Paper

Social network analysis, communities identification, expertise and authority discovery, Trend identification and tracking, time series forecasting, New social media applications, interfaces, interaction techniques, Organizational and group behavior mediate

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changes, health, humans, locations, questions, reddit, sentiment, traditional, twitter, twitter reddit

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cancer, detection, fake, fake news, health, networks, news, sources

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behaviors, changes, humans, impact, learning, measures, performance, predictions, terms, toxic, toxicity

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Applied computing, Life and medical sciences, Health informatics, Mathematics of computing, Probability and statistics, Probabilistic representations, Bayesian networks, Causal networks

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Psychological, personality-based and ethnographic studies of social media, Qualitative and quantitative studies of social media, Social media usage on mobile devices, location, human mobility, and behavior, Organizational and group behavior mediated by soc

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Keywords Paper

applications, simulation, covid-19, contact tracing, distributed inference, set transformer, deepset, epidemiology, domain randomization, retraining

13:24

11/08/2020

Qualitative and quantitative studies of social media, Credibility of online content, Social network analysis, communities identification, expertise and authority discovery, Trust, reputation, recommendation systems

2:31

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Kostantinos Papadamou, Antonis Papasavva, Savvas Zannettou and
Jeremy Blackburn, Nicolas Kourtellis, Ilias Leontiadis, Gianluca Stringhini, Michael Sirivianos

Applied computing, Life and medical sciences, Computing methodologies, Machine learning, Machine learning approaches, Learning latent representations, Neural networks

8:07

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Qualitative and quantitative studies of social media, Credibility of online content, Subjectivity in textual data, sentiment analysis, polarity/opinion identification and extraction, linguistic analyses of social media behavior, Organizational and group be

7:51

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cancer, claims, deep learning, detection, learning, linguistic, misinformation, spread, texts, tweets, twitter

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virtual reality, vr, user studies, in-vr questionnaires, invrqs, break in presence, surveys, biosignals, research methods

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attention, bias, deep learning, detection, groups, identities, learning, sources, toxic, toxicity

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Yanfang Ye, Yujie Fan, Shifu Hou and
Yiming Zhang, Yiyue Qian, Shiyu Sun, Qian Peng, Mingxuan Ju, Wei Song, Kenneth Loparo

Keywords Paper

heterogeneous data, data-driven system, community-level covid-19 risk assessment, community mitigation

11:06

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Keywords Paper

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