# Explainable Misinformation Detection Across Multiple Social Media Platforms

arXiv: https://arxiv.org/abs/2203.11724 · HTML: https://mlsanity.com/paper/2203.11724/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 97 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 24 of 24)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models](https://mlsanity.com/paper/2006.09075/) (2020)
- [Domain Adaptation with Adversarial Training and Graph Embeddings](https://mlsanity.com/paper/1805.05151/) (2018)
- [Adversarial Domain Adaptation for Duplicate Question Detection](https://mlsanity.com/paper/1809.02255/) (2018)
- [Explainable Deep Classification Models for Domain Generalization](https://mlsanity.com/paper/2003.06498/) (2020)
- [Deep Contextualized Self-training for Low Resource Dependency Parsing](https://mlsanity.com/paper/1911.04286/) (2019)
- [Neural Structural Correspondence Learning for Domain Adaptation](https://mlsanity.com/paper/1610.01588/) (2016)
- [What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training](https://mlsanity.com/paper/1805.06088/) (2018)
- [A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems](https://mlsanity.com/paper/1811.11839/) (2018)
- [Knowledge Distillation for BERT Unsupervised Domain Adaptation](https://mlsanity.com/paper/2010.11478/) (2020)
- [Model Explainability in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/2106.07410/) (2021)
- [Anatomy of an online misinformation network](https://mlsanity.com/paper/1801.06122/) (2018)
- [Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification](https://mlsanity.com/paper/2104.08480/) (2021)
- [Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media](https://mlsanity.com/paper/2105.04913/) (2021)

## Built on by (most cited first, 5 of 5)

- [Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine](https://mlsanity.com/paper/2211.09317/) (2022)
- [Evaluating Generalizability of Fine-Tuned Models for Fake News Detection](https://mlsanity.com/paper/2205.07154/) (2022)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations](https://mlsanity.com/paper/2507.05179/) (2025)
- [Experiments with truth using Machine Learning: Spectral analysis and explainable classification of synthetic, false, and genuine information](https://mlsanity.com/paper/2407.05464/) (2024)

## Abstract (arXiv)

In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability. Firstly the Domain Adversarial Neural Network (DANN) develops a generalized misinformation detector across multiple social media platforms DANN is employed to generate the classification results for test domains with relevant but unseen data. The DANN-based model, a traditional black-box model, cannot justify its outcome, i.e., the labels for the target domain. Hence a Local Interpretable Model-Agnostic Explanations (LIME) explainable AI model is applied to explain the outcome of the DANN mode. To demonstrate these two approaches and their integration for effective explainable generalized detection, COVID-19 misinformation is considered a case study. We experimented with two datasets, namely CoAID and MiSoVac, and compared results with and without DANN implementation. DANN significantly improves the accuracy measure F1 classification score and increases the accuracy and AUC performance. The results obtained show that the proposed framework performs well in the case of domain shift and can learn domain-invariant features while explaining the target labels with LIME implementation enabling trustworthy information processing and extraction to combat misinformation effectively.