Mass media plays a central role in delivering science-based crisis, risk, and healthcare information to the general public. However, we have shown a gap between what scholars and expert practitioners advise based on scientific guidelines and what mass media communicates about risk and healthcare. This project test AI, NLP, and data visualization methods to see how well they can close the gap between best practices in communicating science-based knowledge and current mass media practices focusing primarily on news stories. Our approach can assist mass media content creators in preparing effective and high-quality health information that conforms to science-based criteria, and assist media consumers in finding the highest quality information.
Yaros, Ron; Haque, Md Mahfuzul; Rony, Md Main Uddin; Hassan, Naeemul;. Varying amounts of information in health news headlines can affect user selection and interactivity. Association for Education in Journalism and Mass Communication (AEJMC). 2020
Fariha Afsana, Muhammad Ashad Kabir, Naeemul Hassan, Manoranjan Paul. Automatically assessing quality of online health articles. IEEE Journal of Biomedical and Health Informatics. 2020
Dhoju, Sameer; Rony, Md Main Uddin; Kabir, Muhammad Ashad; Hassan, Naeemul; . A large-scale analysis of health journalism by reliable and unreliable media.. 17th World Congress on Medical and Health Informatics, MEDINFO 2019. 2019
Dhoju, Sameer; Main Uddin Rony, Md; Ashad Kabir, Muhammad; Hassan, Naeemul; . Differences in health news from reliable and unreliable media. Companion Proceedings of The 2019 World Wide Web Conference. 2019
Sima Bhowmik, Md Main Uddin Rony, Md Mahfuzul Haque, Kristen Alley Swain, Naeemul Hassan. Examining the role of clickbait headlines to engage readers with reliable health-related information. AAAI Symposium on AI for Social Good. 2019