In today's rapidly evolving digital landscape, understanding how artificial intelligence shapes information consumption has become crucial. Manon Revel, a pioneering researcher at the MIT Laboratory for Information and Decision Systems (LIDS) and the MIT Institute for Data, Systems, and Society (IDSS), has been at the forefront of examining how machine learning algorithms can analyze the impact of online advertising on journalism credibility.
Revel's journey into research began with aspirations of following her father's path into journalism. During her teenage years on France's Atlantic coast, she demonstrated meticulous preparation when covering a holiday fireworks display for local radio—researching dozens of fireworks types and conducting extensive interviews before her live broadcast. This dedication to thorough investigation would later become invaluable in her AI-driven research methodologies.
"I've always been driven to uncover new insights by asking questions others haven't considered," Revel explains. "This approach—whether investigating fireworks or developing AI algorithms—requires comprehensive background research and innovative thinking."
While France's education system required early specialization, Revel's passion for discovery led her to pursue engineering and applied mathematics at École Centrale Paris, completing both bachelor's and master's degrees while maintaining her interest in journalism. Seeking interdisciplinary opportunities, she found her perfect match at MIT's LIDS and the Technology and Policy Program (TPP) at IDSS.
"MIT was the first place where my diverse interests could truly converge," Revel notes. "Here, I could simultaneously explore artificial intelligence applications, information systems, and their societal impacts."
Initially planning only for a master's degree, Revel was so inspired by MIT's collaborative environment that she remained for her PhD, joining the innovative Social and Engineering Systems (SES) doctoral program. This interdisciplinary initiative combines statistical methods, engineering principles, and social sciences to address complex societal challenges—perfect for Revel's research ambitions.
Working with four distinguished advisors across multiple disciplines, Revel has developed sophisticated AI approaches to understanding digital information consumption. Her research gained particular relevance following the 2016 U.S. presidential election, which highlighted the critical need to analyze how information spreads in the digital era and how artificial intelligence can help identify and mitigate information disorder.
Revel's groundbreaking project, conducted with fellow researcher Amir Tohidi, employs advanced machine learning techniques to investigate how clickbait advertising erodes reader trust in digital journalism. As publications struggle with revenue models, many increasingly rely on native ads designed to blend with legitimate content. Revel's research team developed a sophisticated Bayesian text classification AI system trained using Amazon's Mechanical Turk data to identify clickbait content.
After analyzing over 1.4 million advertisements from 2016-2019, their AI-driven analysis revealed that more than 80% qualified as clickbait. Furthermore, their large-scale randomized experiments demonstrated that even brief exposure to clickbait near legitimate content could significantly damage reader trust—particularly for medium-familiarity publications recognized by 25-50% of the audience. Interestingly, the most established outlets like CNN and Fox News showed no significant impact from these ads.
Through her artificial intelligence research, Revel aims to raise awareness about the long-term risks of prioritizing short-term financial gains through native advertising. She acknowledges the difficult balance publishers must maintain between revenue generation and preserving trust—especially crucial in today's era of widespread "information disorder," encompassing intentionally false content, unintentionally shared misinformation, and harmful but factual information.
Revel continues to develop AI-powered approaches to understanding human information processing, including projects examining how audience perception aligns with media coverage and modeling voting systems where information can be delegated to more knowledgeable contacts. Despite the potentially draining nature of studying politics, journalism, and misinformation, Revel maintains an objective perspective focused on understanding rather than judging human behavior.
In an era where artificial intelligence increasingly mediates our information consumption, Revel values authenticity and intellectual honesty among her colleagues—qualities that have made MIT's research environment particularly conducive to her innovative work at the intersection of AI, information science, and human behavior.
