Impact of Explainable Artificial Intelligence on User Trust, Privacy Perception, and Content Engagement in Social Media Platforms

Artificial intelligence (AI) is widely used by social media platforms to personalize feeds, recommend videos, suggest accounts, and select advertisements.However, users may not understand why a particular item appears or how their data influences these recommendations.Explainable Artificial Intelligence (XAI) aims to make AI outputs more understandable to people.This paper presents a survey-based research framework for examining the relationship between perceived explanation clarity and usefulness, user trust, privacy perception, and willingness to engage with recommended content.A questionnaire with demographic questions and five-point response scales is proposed.To demonstrate the planned analysis, a simulated dataset of 150 records is used for descriptive illustration.In this simulated dataset, mean ratings were 3.43 for explanation clarity, 3.54 for explanation usefulness, 3.19 for trust, 3.50 for privacy confidence, and 3.30 for engagement willingness on a five-point scale.These figures are illustrative only and do not represent real participants or establish real-world relationships.The paper outlines a methodology for collecting genuine responses and analysing them using descriptive statistics, reliability checks, correlation, and regression where appropriate.The work highlights the need to consider both transparency and privacy when designing user-centred AI recommendations.