M isinfo T ele G raph: Network-driven Misinformation Detection for G erman Telegram Messages (2025)

Abstract: Connectivity and message propagation are central, yet often underutilised, 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.

## introduction disinformation and misinformation, with their proven impact on democratic elections, have become one of the most harmful online phenomena of our age @xcite . ever since mainstream social media platforms implemented more thorough content moderation policies against harmful speech and misinformation, many users migrated to telegram @xcite . for instance, it was shown that around 30% of adults use the telegram messenger as a news source in germany @xcite . telegram has become a key platform for spreading misinformation, conspiracy theories and far-right ideologies in germany, while largely remaining unmoderated @xcite , and solidifying false beliefs with the echo chamber effect @xcite . already in 2017, the council of europe reported that conventional fact-checking was becoming unable to respond to such data volumes to identify check-worthy content and verify it in a timely manner @xcite . therefore, in recent years, extensive research has been conducted on identifying misinformation using machine learning methods. however, most studies focused on data from x (formerly twitter) and on the english language, while for other languages, including german, mostly simple textbased methods were investigated. in this study, we present our misinfo-telegraph dataset foot_0 , which is a german telegram misinformation graph dataset including 13.845 german telegram channels and their messages from october 2022 to may 2024, including the forwarding information and metadata regarding views and likes. 742 messages are weakly labeled by corresponding fact-checks and newspaper articles using similarity scores from m3embeddings. we trained a graph neural network (gnn) to detect misinformation and analyzed how the incorporation of network information improves the model's performance in comparison to a textonly approach. we make our code available in github foot_1 . ## related work while multiple successful methods were developed to detect the factual correctness of news purely rely-ing on textual content @xcite @xcite , such models were shown to be languagedependent @xcite , prone to adversarial attacks @xcite , and generalize badly to new data due to over-reliance on linguistic patterns and keywords @xcite . recent works have been focusing on including social context and propagation patterns @xcite . approaches based on social context often focus on user demographics, account authenticity and political bias of the thread participants @xcite , location and profile pictures @xcite . other approaches look at social network structure, and user reactions such as likes and shares @xcite @xcite . @xcite used message view counts and information about the telegram channels in which messages have been shared, including the number of subscribers for each channel. @xcite used multivariate time series with recurrent and convolutional networks. @xcite inferred user embeddings with social network structures and classified them using an lstm-rnn. mishra (2020) analyzed user-to-user interaction propagation paths over multiple hops using a transformer architecture, while @xcite used node2vec to create graph embeddings from the follower-followee relationship. motivated by the graph structure of social networks, graph neural networks (gnns) were identified as a promising technique within propagationbased approaches. @xcite applied a gnn for misinformation detection based on data from x, including content, social context, and propagation features. @xcite extended this approach by leveraging continual learning techniques to improve the performance on unseen data. @xcite extracted node features from news articles and user preferences from x using bert and node2vec embeddings, and compared graph convolutional network (gcn) and graph-sage architectures, while also explicitly separating endogenous and exogenous user preferences. comparing multiple types of gnns for this task, @xcite showed that graphsage @xcite performed best, with a test accuracy of 96.99%. nielsen and mcconville (2022), which serves as the main inspiration for our work, implemented a heterogeneous version of the graphsage model as a baseline for their mumin dataset of multi-lingual tweets, achieving an f1 score of 61.45% compared to the labse (language-agnostic bert sentence embedding) text-only baseline of 57.90%. most existing graph-based misinformation detection datasets, like the mumin and fakenewsnet @xcite , are primarily derived from x, with limited options from other social networks. while there are non-specific datasets from platforms like telegram, such as tgdataset @xcite and the pushshift dataset (baumgartner et al., 2020), research on graph neural networks for misinformation detection in telegram data is notably absent. while zhang et al. (2021) utilized telegram threads to train a gnn for a node classification task, to the best of our knowledge, ours is the first work implementing gnns with telegram data for misinformation detection, and also the first on employing these for the german language. ## methods to create the telegram graph dataset, we used weak annotation on data that we received from data4transparency (2024). from this dataset, we constructed a graph using network information, including messages, channels, views, likes and cross-channel message forwarding. statistics of the dataset are depicted in figure 1 . the annotated training graph is used to train a graph neural network, where node embeddings are computed based on their neighborhood representation using the graphsage architecture. our methods are inspired by @xcite , who trained a gnn on a graph dataset from x. since telegram and x are very different platforms, the creation of our graph dataset differs considerably from the work of @xcite . however, we were able to reuse some of their code, and we employed the same approach to train a baseline gnn model on the data. the source text is linked with a claim, if the similarity score exceeds a threshold of 0.7, as is the case for the last claim in the knowledge base with a score of 0.737. the source text inherits the label (factual or misinformation) of the claim that it is matched with. ## additional network analysis to illustrate the potential of the misinfotelegraph dataset for network analysis, we explore structural properties of the message forwarding graph. the dataset includes forwarding relations between telegram messages and channels, allowing for classic social network analysis such as centrality computa-tions. inspired by @xcite and @xcite , we computed several centrality measures using the neo4j graph data science (gds) foot_3 library, including variations of degree centrality and betweenness centrality. these measures highlight the most influential telegram channels in terms of content dissemination and information flow. since degree centrality takes all edges into account, we introduce a variant of degree centrality, which we named forward-degree centrality. this metric specifically counts the number of edges that represent content forwarding actions. unlike traditional degree centrality, which includes all edge classes, forward-degree centrality captures only the edges from the forwarded class. this measure allows to capture information propagation across the platform, reflecting how actively a telegram channel participates in origination and redistribution patterns of misinformation-related messages. notably, channels like auf1 and freie sachsen act as broadcast hubs with high outgoing edge counts, while others like tagesereignisse der offenbarung mostly redistribute external content. this asymmetry illustrates distinct roles in the misinformation ecosystem -original content creators versus amplifiers -and offers interpretable context for gnn-based classification. additional centrality metrics, extended tables, and cypher queries are available in appendix b. ## discussion the evaluation of our gnn-based misinformation detection model on the misinfotelegraph dataset has yielded several key insights. quantitative evaluation showed that our gnnbased model outperformed a text-only baseline. the graph-based approach achieved an mcc of 0.95 compared to 0.69 for the text-only model, confirming that incorporating network structure improves misinformation classification. additional experiments with different numbers of graphsage layers indicated that four layers provided the best performance, likely due to the specific telegram message forwarding network structure. moreover, the use of an lstm aggregator consistently outperformed mean aggregation, underscoring the importance of long-term dependency capture in graphbased misinformation detection. additionally, we identified cross-lingual capacities of the chosen embeddings, successfully matching german claims with messages in english and russian on multiple samples. however, qualitative evaluation revealed limitations in handling logical entailment, particularly in differentiating specific statistical claims related to covid-19 and distinguishing actors in conflicts like the gaza war. finally, we noticed that statistical claims that are often found in health-related misinformation, this topic remains hard to classify. ## conclusion this study shows that integrating network information into misinformation detection models improves performance over text-only approaches. we present the misinfotelegraph dataset and a reproducible baseline to support future research. our findings highlight ai's potential in fact-checking, while acknowledging its limits in logical entailment and bias. ai should assist, not replace, human verification, especially as its generative power still outpaces its detection, reinforcing the need for media literacy and broader misinformation countermeasures. future work should focus on multi-modal detection, better weak annotations, and ethical deployment in sensitive contexts to build more robust misinformation detection systems.