Detection of Persian Rumors and Fake Information in Organizational and Media Networks Using Transformer-Based Models

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Rostami Ghazaleh; Razavi Firouzeh

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2026 | Issue:12

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Title

Detection of Persian Rumors and Fake Information in Organizational and Media Networks Using Transformer-Based Models

Author(s)

Rostami Ghazaleh | Razavi Firouzeh

Keywords

Rumor detection

fake information

natural language processing

machine learning

ParsBERT

organizational information security

Abstract

The proliferation of social networks and digital messaging platforms has significantly increased the spread of rumors and fake information within organizational and media environments. The Persian language, characterized by its orthographic diversity, rich morphological structure, and extensive use of colloquial expressions, presents unique challenges that necessitate a native framework for automated fake content detection. This study proposes a framework based on natural language processing and machine learning to classify Persian texts into three distinct categories: genuine content, fabricated information, and rumors. A dataset comprising 12, 400 Persian messages was collected and subjected to linguistic preprocessing. Three models-SVM, LSTM, and ParsBERT-were subsequently evaluated using 5-fold cross-validation. The results demonstrate that the ParsBERT model significantly outperforms classical models, achieving an F1-score of 0. 91 (p < 0. 05). These findings highlight the potential of transformer-based models for integration into early warning systems for organizational information security.

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