# Profiling the news spreading barriers using news headlines

arXiv: https://arxiv.org/abs/2304.11088 · HTML: https://mlsanity.com/paper/2304.11088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 81 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches](https://mlsanity.com/paper/1904.01172/) (2019)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledge](https://mlsanity.com/paper/2101.00154/) (2021)
- [Combining pre-trained language models and structured knowledge](https://mlsanity.com/paper/2101.12294/) (2021)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)
- [Recommending Themes for Ad Creative Design via Visual-Linguistic Representations](https://mlsanity.com/paper/2001.07194/) (2020)
- [Predicting the Effects of News Sentiments on the Stock Market](https://mlsanity.com/paper/1812.04199/) (2018)
- [Forecasting with Economic News](https://mlsanity.com/paper/2203.15686/) (2022)
- [kogito: A Commonsense Knowledge Inference Toolkit](https://mlsanity.com/paper/2211.08451/) (2022)
- [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)

## Abstract (arXiv)

News headlines can be a good data source for detecting the news spreading barriers in news media, which may be useful in many real-world applications. In this paper, we utilize semantic knowledge through the inference-based model COMET and sentiments of news headlines for barrier classification. We consider five barriers including cultural, economic, political, linguistic, and geographical, and different types of news headlines including health, sports, science, recreation, games, homes, society, shopping, computers, and business. To that end, we collect and label the news headlines automatically for the barriers using the metadata of news publishers. Then, we utilize the extracted commonsense inferences and sentiments as features to detect 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 inferences-based semantic knowledge and sentiment offers better performance than the usual (the average F1-score of the ten categories improves from 0.41, 0.39, 0.59, and 0.59 to 0.47, 0.55, 0.70, and 0.76 for the cultural, economic, political, and geographical respectively) for classifying the news-spreading barriers.