Esther Mead Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
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Biography and Research Information
OverviewAI-generated summary
Esther Mead's research focuses on the propagation and regulation of online discourse, particularly concerning misinformation and toxicity on social media platforms like YouTube and Twitter. Her work employs computational and epidemiological modeling approaches to analyze how information, both legitimate and harmful, spreads within digital environments. Mead has investigated the mechanisms behind video recommendation bias on YouTube and examined the influence of digital activity among far-right actors on various social media platforms.
Her publications also address the application of diffusion of innovations theory to understand adoption stages in connective action campaigns. Mead has utilized epidemiological models to study the spread of misinformation during social movements such as Black Lives Matter, as well as the propagation of toxicity and legitimate information related to COVID-19 on Twitter. She has collaborated with researchers from the University of Arkansas at Little Rock on multiple publications.
Metrics
- h-index: 9
- Publications: 25
- Citations: 305
Selected Publications
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Text Mining Domestic Extremism Topics on Multiple Social Media Platforms (2024)
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Proposing Location-based Predictive Features for Modeling Refugee Counts (2023)
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Developing Approaches to Detect and Mitigate COVID-19 Misinfodemic in Social Networks for Proactive Policymaking (2022)
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Applying diffusion of innovations theory to social networks to understand the stages of adoption in connective action campaigns (2022)
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Surface Web vs Deep Web vs Dark Web (2022)
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Assessing the influence and reach of digital activity amongst far-right actors: A comparative evaluation of mainstream and ‘free speech’ social media platforms (2022)
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Assessing Bias in YouTube’s Video Recommendation Algorithm in a Cross-lingual and Cross-topical Context (2021)
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Applying an Epidemiological Model to Evaluate the Propagation of Misinformation and Legitimate COVID-19-Related Information on Twitter (2021)
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Using Diffusion of Innovations Theory to Study Connective Action Campaigns (2021)
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Proposing a Broader Scope of Predictive Features for Modeling Refugee Counts (2021)
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Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube (2021)
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A public online resource to track COVID-19 misinfodemic (2021)
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