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AI-Personalized News Feeds and Their Ethical Implications: A Statistical Analysis of Privacy, Trust, Engagement, and Algorithmic Manipulation

Students & Supervisors

Student Authors
Susmita Mitra
Bachelor of Science in Computer Science & Engineering, FST
Sandiya Akter Boby
Bachelor of Science in Computer Science & Engineering, FST
Sudipto Kumar Chakrabarty
Bachelor of Science in Computer Science & Engineering, FST
Arnob Debnath
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Md. Mortuza Ahmmed
Associate Professor, Faculty, FST

Abstract

AI-driven personalized news feeds have rapidly gained popularity in recent years, and have changed the nature of information access, but it has brought ethical debates related to privacy, data accuracy, and the impact of algorithms in a new way. The purpose of this study is to evaluate the impact of algorithmic personalization on user engagement, privacy concerns, and trust in news media. For analyzing this study, secondary data were collected covering the period from 2015 to 2024 from reliable sources, including BRAC Institute of Governance and Development (BIGD), Reuters Institute for the Study of Journalism, United Nations Development Programme (UNDP), Bangladesh Telecommunication Regulatory Commission (BTRC), World Bank – Digital Economy Reports. The key variables are AI adoption rates in personalized news feeds, average daily time spent, privacy concerns, trust levels, misinformation incidents, and the timing of regulatory interventions. This study applies quantitative trend analysis to assess outcomes. The findings indicate that AI adoption in news feeds grew from less than 1% in 2015 to 35% in 2024, while average daily time spent on news apps increased from 15 to 45 minutes. However, privacy concerns rose from 30% to 48%, public trust in online news declined from 60% to 46%, and misinformation incidents increased about sevenfold (2 to 14 per year). The results suggest that AI personalization-driven engagement will only remain sustainable if users’ privacy is protected and they are made aware of how algorithms curate their feeds, since declining trust poses a long-term risk despite short-term growth.

Keywords

AI-personalized news privacy concerns Trust in media User engagement algorithmic manipulation

Publication Details

  • Type of Publication:
  • Conference Name: International Conference on Challenges and Trends in Arts and Social Sciences (ICCTASS 2025)
  • Date of Conference: 11/12/2025 - 11/12/2025
  • Venue: American International University-Bangladesh (AIUB), Dhaka-1229, Bangladesh.
  • Organizer: FASS, AIUB
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