# Leveraging Commonsense Knowledge on Classifying False News and Determining Checkworthiness of Claims

arXiv: https://arxiv.org/abs/2108.03731 · HTML: https://mlsanity.com/paper/2108.03731/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 65 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 30 of 30)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Generating Fact Checking Explanations](https://mlsanity.com/paper/2004.05773/) (2020)
- [Automated Fact Checking: Task formulations, methods and future directions](https://mlsanity.com/paper/1806.07687/) (2018)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018)
- [Improving Question Answering by Commonsense-Based Pre-Training](https://mlsanity.com/paper/1809.03568/) (2018)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media](https://mlsanity.com/paper/2007.07997/) (2020)
- [Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness](https://mlsanity.com/paper/1808.05542/) (2018)
- [Learning Hierarchical Discourse-level Structure for Fake News Detection](https://mlsanity.com/paper/1903.07389/) (2019)
- [A Benchmark Dataset of Check-worthy Factual Claims](https://mlsanity.com/paper/2004.14425/) (2020)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/1904.01546/) (2019)
- [Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension](https://mlsanity.com/paper/1803.00191/) (2018)
- [An Emotional Analysis of False Information in Social Media and News Articles](https://mlsanity.com/paper/1908.09951/) (2019)
- [NELA-GT-2019: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles](https://mlsanity.com/paper/2003.08444/) (2020)
- [The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and Alternative News Sources](https://mlsanity.com/paper/1705.06947/) (2017)

## Abstract (arXiv)

Widespread and rapid dissemination of false news has made fact-checking an indispensable requirement. Given its time-consuming and labor-intensive nature, the task calls for an automated support to meet the demand. In this paper, we propose to leverage commonsense knowledge for the tasks of false news classification and check-worthy claim detection. Arguing that commonsense knowledge is a factor in human believability, we fine-tune the BERT language model with a commonsense question answering task and the aforementioned tasks in a multi-task learning environment. For predicting fine-grained false news types, we compare the proposed fine-tuned model's performance with the false news classification models on a public dataset as well as a newly collected dataset. We compare the model's performance with the single-task BERT model and a state-of-the-art check-worthy claim detection tool to evaluate the check-worthy claim detection. Our experimental analysis demonstrates that commonsense knowledge can improve performance in both tasks.