Narrative Extraction for Disinformation Detection
Narrative Extraction for Disinformation Detection

Abstract: 

Typically when we consider disinformation narratives, we think about the most pervasive ones: QAnon, “pizzagate”, and false information about COVID-19. These are the ones that receive widespread media attention and work to polarize individuals on a broad scale. The problem with knowing only the most “popular” disinformation narratives - for both the average Internet user and the analyst - is that by the time we become aware of their existence, they have already influenced and been shared by a lot of people. With this issue in mind, researchers should be investigating ways through which disinformation narratives can be flagged and identified without having a priori knowledge of what to look for.
In this talk, Carlos and Amber will walk through an NLP-based method they have used for combining open source deep learning models (BERT, GPT-2) and topic modeling (LDA) to identify disinformation narratives in articles. This approach involves first using a binary classifier to find texts that are potential sources of disinformation. Once these texts have been reviewed and classified by a subject matter expert, clusters of narratives can be extracted from the documents and used for further analyses. After discussing the technical approach, they will demonstrate the method on a case study using news articles that have been posted on Twitter. This allows for a real-time assessment of shared media, particularly in a high-traffic environment that encourages virality. From here, the session will conclude by demonstrating how this approach can be used outside the realm of disinformation narrative detection, specifically as a tool to analyze public responses to new products, brands, or even government policies.

Bio: 

Amber is a Machine Learning Engineer at Novetta and a student at the University of Texas at San Antonio. Her primary interests are in natural language processing and applying deep learning to analyze digital communication. Prior to starting at Novetta, Amber worked in UTSA’s Digital Politics Studio examining the effects of political incivility on social media platforms and developing methods for detecting uncivil language online. Amber will graduate from UTSA this spring with Bachelor’s degrees in Psychology and English.

Open Data Science

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