Profiling the barriers to the spreading of news using news headlines

News headlines can be a good data source for detecting the barriers to the spreading of news in news media, which can be useful in many real-world applications. In this study, we utilize semantic knowledge through the inference-based model COMET and the 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 common-sense inferences and sentiments as features to detect the barriers to the spreading of news. We compare our approach to the classical text classification methods, deep learning, and transformer-based methods …