Multidimensional Analysis of Trust in News Articles (Student Abstract)


  • Avneet Kaur Indraprastha Institute of Information Technology, New Delhi
  • Maitree Leekha Delhi Technological University
  • Utkarsh Chawla Delhi Technological University
  • Ayush Agarwal Delhi Technological University
  • Mudit Saxena Shiv Nadar University
  • Nishtha Madaan IBM Research AI - India
  • Kalapriya Kannan IBM Research AI - India
  • Sameep Mehta IBM Research AI - India



The advancements in the field of Information Communication Technology have engendered revolutionary changes in the journalism industry, not only on the part of the journalists and the media personnel, but also on the people consuming these news stories, who today, are only a click away from all the updates they need. However, these advances have also exposed the prevailing venality, wearying off the trust of the public in news media. How then, does an individual discern that which, out of the countless news stories for an incident, should be trusted? This work introduces a system that presents the user a multidimensional analysis for trust in news from various media sources based on the textual content of the articles, assessment of the journalists' perspectives and the temporal diversity of the issues being covered by the media houses publishing the news articles. Our experiments on a self-collected dataset confirm that the system aids in a comprehensive analysis of trust.




How to Cite

Kaur, A., Leekha, M., Chawla, U., Agarwal, A., Saxena, M., Madaan, N., Kannan, K., & Mehta, S. (2020). Multidimensional Analysis of Trust in News Articles (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13837-13838.



Student Abstract Track