# Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations

arXiv: https://arxiv.org/abs/2508.00674 · HTML: https://mlsanity.com/paper/2508.00674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review](https://mlsanity.com/paper/2010.10596/) (2020)
- [Why model why? Assessing the strengths and limitations of LIME](https://mlsanity.com/paper/2012.00093/) (2020)
- [Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research](https://mlsanity.com/paper/2211.15343/) (2022)
- [Counterfactual Explanations for Machine Learning: Challenges Revisited](https://mlsanity.com/paper/2106.07756/) (2021)
- [Explanation of Machine Learning Models Using Shapley Additive Explanation and Application for Real Data in Hospital](https://mlsanity.com/paper/2112.11071/) (2021)

## Built on by (most cited first, 1 of 1)

- [EHSAN: Leveraging ChatGPT in a Hybrid Framework for Arabic Aspect-Based Sentiment Analysis in Healthcare](https://mlsanity.com/paper/2508.02574/) (2025)

## Abstract (arXiv)

Social media platforms today strive to improve user experience through AI recommendations, yet the value of such recommendations vanishes as users do not understand the reasons behind them. This issue arises because explainability in social media is general and lacks alignment with user-specific needs. In this vision paper, we outline a user-segmented and context-aware explanation layer by proposing a visual explanation system with diverse explanation methods. The proposed system is framed by the variety of user needs and contexts, showing explanations in different visualized forms, including a technically detailed version for AI experts and a simplified one for lay users. Our framework is the first to jointly adapt explanation style (visual vs. numeric) and granularity (expert vs. lay) inside a single pipeline. A public pilot with 30 X users will validate its impact on decision-making and trust.