MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages

• URL: http://arxiv.org/abs/2506.22529v1
• Date: Fri, 27 Jun 2025 12:32:19 GMT
• Title: MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages
• Authors: Lu Kalkbrenner, Veronika Solopova, Steffen Zeiler, Robert Nickel, Dorothea Kolossa,
• Abstract summary: Misinfo-TeleGraph is the first German-language Telegram-based graph dataset for misinformation detection.<n>It includes over 5 million messages from public channels, enriched with metadata, channel relationships, and both weak and strong labels.<n>We evaluate both text-only models and graph neural networks (GNNs) that incorporate message forwarding as a network structure.
• Score: 5.533030792414604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Connectivity and message propagation are central, yet often underutilized, sources of information in misinformation detection -- especially on poorly moderated platforms such as Telegram, which has become a critical channel for misinformation dissemination, namely in the German electoral context. In this paper, we introduce Misinfo-TeleGraph, the first German-language Telegram-based graph dataset for misinformation detection. It includes over 5 million messages from public channels, enriched with metadata, channel relationships, and both weak and strong labels. These labels are derived via semantic similarity to fact-checks and news articles using M3-embeddings, as well as manual annotation. To establish reproducible baselines, we evaluate both text-only models and graph neural networks (GNNs) that incorporate message forwarding as a network structure. Our results show that GraphSAGE with LSTM aggregation significantly outperforms text-only baselines in terms of Matthews Correlation Coefficient (MCC) and F1-score. We further evaluate the impact of subscribers, view counts, and automatically versus human-created labels on performance, and highlight both the potential and challenges of weak supervision in this domain. This work provides a reproducible benchmark and open dataset for future research on misinformation detection in German-language Telegram networks and other low-moderation social platforms.

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