Cross-Platform Digital Discourse Analysis of Iran: Topics, Sentiment, Polarization, and Event Validation on Telegram and Reddit

2026-03-03

AI Summary

This study investigates the thematic distribution, sentiment orientation, and polarization characteristics of Iran-related online discourse on Telegram and Reddit, and examines its temporal association with real-world geopolitical events. Employing a unified and reproducible analytical pipeline that integrates NMF topic modeling, TF-IDF feature extraction, VADER sentiment analysis, and a custom-designed keyword escalation index, this work presents the first synchronized quantification of narrative dynamics surrounding Iran across two structurally distinct platforms. The findings reveal systematic differences between the platforms in narrative structure and tonal emphasis. Moreover, escalation signals in online discourse exhibit measurable temporal offsets—either anticipating or reacting to offline events—thereby uncovering a non-zero time-lagged relationship between cross-platform digital narratives and real-world developments.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

We analyze Iran-related discourse across two structurally different platforms: Telegram (7,567 messages from international news channels) and Reddit (23,909 posts and comments from Iran-focused and global communities). Using a single reproducible pipeline, we apply NMF topic modeling over TF--IDF features, VADER sentiment scoring, and a keyword-bundle escalation index capturing military, nuclear, and diplomatic narratives. To assess whether discourse dynamics track offline developments, we compare escalation time series with external protest and geopolitical event timelines using same-day and lagged correlation analysis. Same-day correlations are weak, but the strongest relationships occur at non-zero lags, consistent with anticipatory or reactive framing rather than instantaneous mirroring. Finally, using a separate real-time collection (February 2026), we observe synchronized increases in escalation-related narratives that coincide with documented geopolitical developments. Overall, the results show systematic cross-platform differences in narrative structure and tone, and provide quantitative evidence that online escalation signals can align with real-world developments with measurable temporal offsets.

Problem

• cross-platform discourse
• Iran
• sentiment analysis
• event validation
• online-offline correlation

Innovation

• cross-platform discourse analysis
• keyword-bundle escalation index
• temporal lag correlation
• NMF topic modeling
• online-offline event validation