# HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemble

arXiv: https://arxiv.org/abs/2508.21482 · HTML: https://mlsanity.com/paper/2508.21482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [DESlib: A Dynamic ensemble selection library in Python](https://mlsanity.com/paper/1802.04967/) (2018)
- [Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification](https://mlsanity.com/paper/2009.01047/) (2020)
- [TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection](https://mlsanity.com/paper/2304.09421/) (2023)
- [META-DES.H: a dynamic ensemble selection technique using meta-learning and a dynamic weighting approach](https://mlsanity.com/paper/1811.01742/) (2018)
- [Selecting and combining complementary feature representations and classifiers for hate speech detection](https://mlsanity.com/paper/2201.06721/) (2022)
- [Multi-view autoencoders for Fake News Detection](https://mlsanity.com/paper/2504.08102/) (2025)

## Abstract (arXiv)

Psychological biases, such as confirmation bias, make individuals particularly vulnerable to believing and spreading fake news on social media, leading to significant consequences in domains such as public health and politics. Machine learning-based fact-checking systems have been widely studied to mitigate this problem. Among them, ensemble methods are particularly effective in combining multiple classifiers to improve robustness. However, their performance heavily depends on the diversity of the constituent classifiers-selecting genuinely diverse models remains a key challenge, especially when models tend to learn redundant patterns. In this work, we propose a novel automatic classifier selection approach that prioritizes diversity, also extended by performance. The method first computes pairwise diversity between classifiers and applies hierarchical clustering to organize them into groups at different levels of granularity. A HierarchySelect then explores these hierarchical levels to select one pool of classifiers per level, each representing a distinct intra-pool diversity. The most diverse pool is identified and selected for ensemble construction from these. The selection process incorporates an evaluation metric reflecting each classifiers's performance to ensure the ensemble also generalises well. We conduct experiments with 40 heterogeneous classifiers across six datasets from different application domains and with varying numbers of classes. Our method is compared against the Elbow heuristic and state-of-the-art baselines. Results show that our approach achieves the highest accuracy on two of six datasets. The implementation details are available on the project's repository: https://github.com/SaraBCoutinho/HSFN .