Invited talks, conference presentations, and posters.
An overview of my dissertation work tracing how narratives originate and propagate across international news ecosystems.
An overview of my dissertation work tracing how narratives originate and propagate across international news ecosystems.
How transformer-based models can help combat the global proliferation of misinformation: a system that scalably identifies, tracks, and analyzes the spread of misinformation across thousands of international news websites. Using multilingual Matryoshka embeddings and hierarchical level-wise clustering, it surfaces individual news stories, topics, and overarching themes; multilingual stance detection, natural language inference, and network analysis then assess bias and factual inconsistency to identify outlets that disseminate propaganda.
An overview of my dissertation work tracing how narratives originate and propagate across international news ecosystems.
An overview of my dissertation work tracing how narratives originate and propagate across international news ecosystems.
Narrative analysis across thousands of low-, mixed-, and highly-reliable news websites.
In this work, we study the co-occurrence of narratives between Weibo and foreign media ecosystems.
In this work, we study the most prominent disinformation narratives being touted by the Russian government to English-speaking audiences. To do this we perform topic analysis using the large-language model MPNet on articles published by nine different Russian disinformation websites and the new Russian “fact-checking” website waronfakes.com. We then map Reddit comments to the topics being promoted by these disinformation websites.
In this work, we study the co-occurrence of narratives between Weibo and foreign media ecosystems.
This work linking Weibo narratives to those originating in the Russian, Ukrainian, US, and Chinese news media ecosystems.
In this work, firstly utilizing daily scrapes of 3,074 news websites (both mainstream and misinformation), the large-language model MPNet, and DP-Means clustering, we build a system to automatically isolate and analyze the narratives being spread within online ecosystems. Secondly, to understand the impact of AI-Written content, we present one of the first large-scale studies of the prevalence of AI-written articles within online news media.
In this work, firstly utilizing daily scrapes of 3,074 news websites (both mainstream and misinformation), the large-language model MPNet, and DP-Means clustering, we build a system to automatically isolate and analyze the narratives being spread within online ecosystems. Secondly, to understand the impact of AI-Written content, we present one of the first large-scale studies of the prevalence of AI-written articles within online news media.
How have Russian information campaigns influenced and affected public perceptions of the Russo-Ukrainian War? In this talk, we study the coordinated information campaign to understand the most prominent disinformation narratives touted by the Russian government to English-speaking audiences.
How have Russian information campaigns influenced and affected public perceptions of the Russo-Ukrainian War? In this talk, we study the coordinated information campaign to understand the most prominent disinformation narratives touted by the Russian government to English-speaking audiences.
In this work, we study the most prominent disinformation narratives being touted by the Russian government to English-speaking audiences. To do this we perform topic analysis using the large-language model MPNet on articles published by nine different Russian disinformation websites and the new Russian “fact-checking” website waronfakes.com. We then map Reddit comments to the topics being promoted by these disinformation websites.
In this work, we study the relationships between five prominent conspiracy theories (QAnon, COVID, UFO/Aliens, 9/11, and Flat-Earth) and the role that misinformation and political polarization play in spreading these conspiracies.
In this talk, we explore using web crawls seeded from two of the largest QAnon hotbeds on the Internet, Voat and 8kun, to build a hyperlink graph in order to study the QAnon conspiracy theory.
We propose a novel machine learning architecture and technique for de novo drug discovery of anti-cancer drugs by using discrete representations of drugs’ chemical compositions and the transcriptomics of targets.
This work focuses on utilizing Transformer and CNN machine translation models to learn contextualized word representations for use in sentiment classification.
This talk concerns counteracting information leakage in Tor relay selection algorithms.