# Classification of news spreading barriers

arXiv: https://arxiv.org/abs/2304.08167 · HTML: https://mlsanity.com/paper/2304.08167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles](https://mlsanity.com/paper/2110.10457/) (2021)
- [Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues](https://mlsanity.com/paper/1910.01160/) (2019)
- [Analysis of information cascading and propagation barriers across distinctive news events](https://mlsanity.com/paper/2212.07742/) (2022)
- [Political and Economic Patterns in COVID-19 News: From Lockdown to Vaccination](https://mlsanity.com/paper/2212.13875/) (2022)
- [Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage](https://mlsanity.com/paper/1606.05110/) (2016)

## Abstract (arXiv)

News media is one of the most effective mechanisms for spreading information internationally, and many events from different areas are internationally relevant. However, news coverage for some news events is limited to a specific geographical region because of information spreading barriers, which can be political, geographical, economic, cultural, or linguistic. In this paper, we propose an approach to barrier classification where we infer the semantics of news articles through Wikipedia concepts. To that end, we collected news articles and annotated them for different kinds of barriers using the metadata of news publishers. Then, we utilize the Wikipedia concepts along with the body text of news articles as features to infer the news-spreading barriers. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods. The results show that the proposed approach using Wikipedia concepts based semantic knowledge offers better performance than the usual for classifying the news-spreading barriers.