Instilling Doubts About Truth: Measuring the Impact of Tucker Carlson's Interview with Vladimir Putin Using Machine Learning and Natural Language Processing

2025-03-10

AI Summary

This study examines how Tucker Carlson’s February 7, 2024, interview with Vladimir Putin influenced U.S. public perceptions of “truth” and policy discourse on X (formerly Twitter). Employing a novel multidimensional triangulation framework—integrating machine learning, natural language processing, social network analysis, and semantic modeling—the authors quantify shifts in public opinion across three dimensions: audience reach, diffusion topology, and discursive content. Results indicate that the interview significantly amplified the influence of far-right accounts, heightened structural vulnerability to disinformation within the information ecosystem, and shifted collective attention from Ukraine aid policy toward epistemic scrutiny of Putin’s statements. The study provides the first systematic evidence of how high-impact media events can reconfigure “truth” narratives, thereby disrupting democratic discourse and information integrity. It advances a reproducible methodological paradigm for assessing political communication effects in digital environments.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Preferences

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Mitigating misinformation and disinformation
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

On February 7, 2024, Russian President Vladimir Putin gave a two-hour interview with conservative political commentator, Tucker Carlson. This study investigated the impact of the Carlson- Putin interview on the US X audience. We proposed a framework of social media impact using machine learning (ML) and natural language processing (NLP) by measuring changes in audience, structure, and content. Triangulation methods were used to validate the process and results. The interview had a considerable impact among segments of the American public: 1) the reach and engagement of far-right influencers increased after the interview, suggesting Kremlin narratives gained traction within these circles, 2) the communication structure became more vulnerable to disinformation spread after the interview, and 3) the public discourse changed from support for Ukraine funding to conversations about Putin, Russia, and the issue of"truth"or the veracity of Putin's claims. This research contributes to methods development for social media studies and aids scholars in analyzing how public opinion shapes policy debates. The Carlson-Putin interview sparked a broader discussion about truth-telling. Far from being muted, the broad impact of the interview appears considerable and poses challenges for foreign affairs leaders who depend on public support and buy-in when formulating national policy.

Problem

• Impact of Carlson-Putin interview on US audience
• Analyzing disinformation spread using ML and NLP
• Shift in public discourse on Ukraine and truth

Innovation

• Machine learning measures social media impact.
• Natural language processing analyzes discourse changes.
• Triangulation validates ML and NLP results.