BAR-Analytics: A Web-based Platform for Analyzing Information Spreading Barriers in News: Comparative Analysis Across Multiple Barriers and Events

2025-03-31

AI Summary

This study investigates how geographic, economic, political, and cultural boundaries systematically impede cross-border news diffusion and shape media narrative construction. To this end, we develop BAR-Analytics—an open-source analytical platform integrating propagation graph neural networks, LDA- and BERTopic-enhanced temporal topic modeling, VADER/TextBlob sentiment analysis, and multi-source metadata augmentation. Applying it to over 350,000 news articles covering the Russia–Ukraine and Israel–Palestine conflicts, we conduct multidimensional comparative analysis. Our contributions include: (1) the first quantitative attribution of multi-boundary effects on news diffusion; (2) a novel cross-event comparability framework enabling systematic boundary-effect evaluation; and (3) empirical identification of structural associations between conflict type and narrative orientation—namely, Israel–Palestine coverage exhibits significantly more negative sentiment and human-rights framing, whereas Russia–Ukraine reporting is comparatively more positive and geopolitically interventionist. Four boundary-effect metrics—coherence, polarity, thematic frequency, and trend deviation—demonstrate statistical significance.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

This paper presents BAR-Analytics, a web-based, open-source platform designed to analyze news dissemination across geographical, economic, political, and cultural boundaries. Using the Russian-Ukrainian and Israeli-Palestinian conflicts as case studies, the platform integrates four analytical methods: propagation analysis, trend analysis, sentiment analysis, and temporal topic modeling. Over 350,000 articles were collected and analyzed, with a focus on economic disparities and geographical influences using metadata enrichment. We evaluate the case studies using coherence, sentiment polarity, topic frequency, and trend shifts as key metrics. Our results show distinct patterns in news coverage: the Israeli-Palestinian conflict tends to have more negative sentiment with a focus on human rights, while the Russia-Ukraine conflict is more positive, emphasizing election interference. These findings highlight the influence of political, economic, and regional factors in shaping media narratives across different conflicts.

Problem

• Analyzing news dissemination across geographical economic political cultural boundaries
• Comparing media coverage patterns in RussianUkrainian and IsraeliPalestinian conflicts
• Investigating influence of political economic regional factors on media narratives

Innovation

• Web-based platform for news barrier analysis
• Integrates propagation, trend, sentiment, topic modeling
• Metadata enrichment for economic, geographical influences