# Multilingual Models for Check-Worthy Social Media Posts Detection

arXiv: https://arxiv.org/abs/2408.06737 · HTML: https://mlsanity.com/paper/2408.06737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [Multilingual Previously Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2305.07991/) (2023)
- [LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content](https://mlsanity.com/paper/2101.11891/) (2021)
- [NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge](https://mlsanity.com/paper/2112.08544/) (2021)
- [Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning](https://mlsanity.com/paper/2210.14867/) (2022)
- [g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection](https://mlsanity.com/paper/2012.11967/) (2020)
- [Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English](https://mlsanity.com/paper/2101.02359/) (2021)
- [Some Observations on Fact-Checking Work with Implications for Computational Support](https://mlsanity.com/paper/2305.02224/) (2023)
- [MM-Claims: A Dataset for Multimodal Claim Detection in Social Media](https://mlsanity.com/paper/2205.01989/) (2022)
- [Identification of COVID-19 related Fake News via Neural Stacking](https://mlsanity.com/paper/2101.03988/) (2021)
- [Check-worthy Claim Detection across Topics for Automated Fact-checking](https://mlsanity.com/paper/2212.08514/) (2022)
- [Two-Stage Classifier for COVID-19 Misinformation Detection Using BERT: a Study on Indonesian Tweets](https://mlsanity.com/paper/2206.15359/) (2022)

## Built on by (most cited first, 2 of 2)

- [MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models](https://mlsanity.com/paper/2602.16298/) (2026)
- [Multi-Agentic System Leveraging Open-Source LLMs to Mitigate Disinformation Threats](https://mlsanity.com/paper/2606.30259/) (2026)

## Abstract (arXiv)

This work presents an extensive study of transformer-based NLP models application for detection of social media posts that contain verifiable factual claims and harmful claims. The study covers various activities, including dataset collection, dataset pre-processing, architecture selection, setup of settings, model training (fine-tuning), model testing, and implementation. The study includes a comprehensive analysis of different models, with a special focus on multilingual models where the same model is capable of processing social media posts in both English and in low-resource languages such as Arabic, Bulgarian, Dutch, Polish, Czech, Slovak. The results obtained from the study were validated against state-of-the-art models, and the comparison demonstrated the robustness of the proposed models. The novelty of this work lies in the development of multi-label multilingual classification models that can simultaneously detect harmful posts and posts that contain verifiable factual claims in an efficient way.