Doctors Fact-Check, Journalists Get Fact-Checked: Comparing Public Trust in Journalism and Healthcare

Public trust in journalism has fallen disconcertingly low. This study sets out to understand the news industry’s credibility crisis by comparing public perceptions of journalism with public perceptions of another institution facing similar trust challenges: healthcare. Drawing on in-depth interviews with 31 US adults, we find that although both healthcare and journalism face public distrust, members of the public generally tend to feel more trusting of individual doctors than they do of individual journalists. This is because people (a) perceive doctors to be experts in their field and (b) engage more frequently with doctors than they do with journalists. Consequently, our interviewees described treating their doctors as "fact-checkers" when it comes to health information they find online, demonstrating trust in their physicians despite their lack of trust in healthcare more broadly. Meanwhile, the opposite unfolds in journalism: Instead of using legitimate news sources to fact-check potential misinformation, people feel compelled to "fact-check" legitimate news by seeking alternative sources of corroboration. We conclude that, to improve their credibility among the public, journalists must strike the right balance between persuading the public to perceive them as experts while also pursuing opportunities to engage with the public as peers

J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News

The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape. Among various types of AI-generated text, AI-generated news presents a significant threat as it can be a prominent source of misinformation online. While several recent efforts have focused on detecting AI-generated text in general, these methods require enhanced reliability, given concerns about their vulnerability to simple adversarial attacks. Furthermore, due to the eccentricities of news writing, applying these detection methods for AI-generated news can produce false positives, potentially damaging the reputation of news organizations. To address these challenges, we leverage the expertise of an interdisciplinary team to develop a framework, J-Guard, capable of steering existing supervised AI text detectors for detecting AI-generated news while boosting adversarial robustness. By incorporating stylistic cues inspired by the unique journalistic attributes, J-Guard effectively distinguishes between real-world journalism and AI-generated news articles. Our experiments on news articles generated by a vast array of AI models, including ChatGPT (GPT3.5), demonstrate the effectiveness of J-Guard in enhancing detection capabilities while maintaining an average performance decrease of as low as 7% when faced with adversarial attacks.Comment: This Paper is Accepted to The 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL 2023

VIRAL HEALTH MISINFORMATION FROM GEOCITIES TO COVID-19

Although often discussed in the public discourse as a phenomenon newly exacerbated by social media, the use of the Internet to spread health-related misinformation is as old as the Internet itself. Techniques, networks, and narratives from prior novel health outbreaks such as HIV or Ebola continue to circulate and are repurposed in the current COVID-19 pandemic. We examine and compare two case studies of health misinformation — HIV mis/disinformation in from the mid-1990s to early 2000s circulating in GeoCities and the role of official COVID-19 Dashboards in present-day COVID-19 mis/disinformation. This contributes to our understanding of current and historical health misinformation as well as the connections between them