# MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages

arXiv: https://arxiv.org/abs/2506.22529 · HTML: https://mlsanity.com/paper/2506.22529/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [MagNet: A Neural Network for Directed Graphs](https://mlsanity.com/paper/2102.11391/) (2021)
- [User Preference-aware Fake News Detection](https://mlsanity.com/paper/2104.12259/) (2021)
- [Neural Ranking Models with Weak Supervision](https://mlsanity.com/paper/1704.08803/) (2017)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [Graph Neural Networks with Continual Learning for Fake News Detection from Social Media](https://mlsanity.com/paper/2007.03316/) (2020)
- [The Role of User Profile for Fake News Detection](https://mlsanity.com/paper/1904.13355/) (2019)
- [The Pushshift Telegram Dataset](https://mlsanity.com/paper/2001.08438/) (2020)
- [Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets](https://mlsanity.com/paper/2309.06503/) (2023)
- [Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection](https://mlsanity.com/paper/2401.15717/) (2024)

## Built on by (most cited first, 1 of 1)

- [Graph-Based Detection of Disinformation Narrative Diffusion between Russian and Ukrainian Telegram Channels](https://mlsanity.com/paper/2607.11894/) (2026)

## Abstract (arXiv)

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.