Using the profile of publishers to predict barriers across news articles
원문 초록
Detection of news propagation barriers, being economical, cultural, political, time zonal, or geographical, is still an open research issue. We present an approach to barrier detection in news spreading by utilizing Wikipedia-concepts and metadata associated with each barrier. Solving this problem can not only convey the information about the coverage of an event but it can also show whether an event has been able to cross a specific barrier or not. Experimental results on IPoNews dataset (dataset for information spreading over the news) reveals that simple classification models are able to detect barriers with high accuracy. We believe that our approach can serve to provide useful insights which pave the way for the future development of a system for predicting information spreading barriers over the news.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
How Grounded is Wikipedia? A Study on Structured Evidential Support
Intent Factored Generation: Unleashing the Diversity in Your Language Model
토론
이 논문과 연결된 포럼 글이 아직 없습니다.