Tracing the Spread: A New Framework for Understanding Online Rumors

Author: Denis Avetisyan

Researchers have developed a novel approach that not only identifies false information but also models how and why it spreads through social networks.

CausalMamba integrates Mamba and Graph Convolutional Network encoders to fuse node features from propagation chains, enabling joint optimization of rumor classification and causal graph discovery through a multi-task loss function, thereby establishing a unified framework for discerning causal relationships and assessing information veracity.

CausalMamba combines state space models with graph neural networks to perform interpretable causal inference on temporal rumor propagation.

Despite advances in misinformation detection, understanding how rumors spread remains a significant challenge. This paper introduces CausalMamba: Interpretable State Space Modeling for Temporal Rumor Causality, a novel framework integrating sequence modeling, graph neural networks, and causal discovery to jointly classify and interpret rumor propagation. By uncovering latent causal graphs, CausalMamba not only achieves competitive performance but also enables analysis of influential nodes and potential counterfactual interventions. Could this approach pave the way for more actionable and explainable systems for combating online misinformation?

The Challenge of Scale in Rumor Propagation

Current approaches to automated rumor detection, such as those employing BiLSTM-CNN and Transformer architectures, frequently encounter limitations when analyzing the spread of misinformation across social networks. These models, while effective in processing sequential data, struggle to capture the long-range dependencies inherent in complex propagation graphs. Rumors don’t simply unfold linearly; information diffuses through intricate webs of connections, where a post from a distant source can significantly influence credibility assessments. The ability to discern these relationships-to understand how a rumor’s origins and early spread impact its later trajectory-is crucial for accurate detection. However, the computational cost of processing these extensive graphs often overwhelms these models, hindering their scalability and performance when faced with the sheer volume of data characteristic of online information ecosystems. Consequently, a need exists for methods capable of efficiently modeling these complex relational structures and capturing the subtle cues that differentiate genuine information from fabricated narratives.

The modern information ecosystem presents a significant challenge to rumor detection systems due to the sheer scale of online content. The exponential growth in data volume, coupled with the accelerating speed at which information propagates, overwhelms traditional analytical methods. Existing techniques, designed for smaller datasets and slower transmission rates, struggle to process the constant influx of posts, shares, and comments in real-time. This necessitates the development of more efficient algorithms and scalable infrastructure capable of handling the velocity and volume of online data while maintaining accuracy. A failure to address this challenge risks allowing misinformation to spread rapidly and unchecked, potentially causing significant societal harm. Consequently, research is increasingly focused on methods that prioritize computational efficiency without sacrificing the ability to identify and flag potentially false information.

Current approaches to identifying online rumors frequently simplify the complex process of information spread by analyzing rumor cascades as linear sequences of events. This simplification overlooks the critical role of the underlying social network and the relationships between users who share and propagate information. A rumor doesn’t simply unfold as a chain of retweets; rather, it branches and evolves within a complex graph of interconnected individuals, where the credibility of sources, community structures, and patterns of interaction significantly influence its trajectory. By neglecting this nuanced graph structure, existing methods miss vital cues that could distinguish between genuine news and malicious disinformation, hindering their ability to accurately and efficiently detect rumors as they emerge and circulate online. Consequently, a more sophisticated understanding of rumor propagation as a graph-based phenomenon is essential for developing robust and scalable detection systems.

Graph Structure and Selective State Space Modeling

Graph Representation Learning techniques transform the structure of rumor cascades – typically modeled as nodes representing users and edges representing information diffusion – into vector embeddings. These embeddings, also known as node or graph embeddings, capture relational information by representing each node in a low-dimensional vector space where the proximity of vectors reflects the similarity of nodes within the graph. Algorithms such as DeepWalk, Node2Vec, and Graph Convolutional Networks (GCNs) are commonly employed to generate these vector representations, allowing downstream machine learning models to utilize structural information directly as input features. The resulting vectors encode information about a node’s connections and its position within the broader network, facilitating tasks such as rumor detection and source identification.

Mamba is a selective state space model (SSM) designed to improve sequence modeling efficiency, particularly with long sequences where Recurrent Neural Networks (RNNs) and Transformers encounter computational bottlenecks. Traditional RNNs suffer from vanishing/exploding gradients and limited parallelization, while Transformers, though parallelizable, exhibit quadratic complexity with sequence length due to the attention mechanism. Mamba addresses these limitations by incorporating a selection mechanism that dynamically filters irrelevant historical information, reducing computational cost from $O(n^2)$ to $O(n)$, where $n$ is the sequence length. This selective approach allows Mamba to process longer sequences with greater efficiency and reduced memory requirements, making it suitable for tasks involving extensive contextual information.

Integrating graph embeddings with the Mamba selective state space model enables the simultaneous capture of both sequential dependencies and underlying structural information present in rumor cascade data. This combined approach leverages the strengths of both methodologies: graph embeddings represent the network structure of the cascade, while Mamba efficiently processes the temporal sequence of events. Empirical results demonstrate a +7% improvement in both accuracy and F1-score when compared to a BiLSTM-CNN baseline, indicating the efficacy of this combined model in rumor cascade analysis and classification. The performance gain highlights the benefit of incorporating structural context into sequential modeling for improved predictive capabilities.

CausalMamba: Dissecting the Roots of Propagation

CausalMamba utilizes the NOTEARS algorithm to perform differentiable causal discovery on rumor cascades, constructing a Directed Acyclic Graph (DAG) that models the relationships between individual tweets. NOTEARS enforces acyclicity through continuous optimization, allowing gradients to flow for end-to-end training. This DAG represents the direction of influence; an edge from tweet A to tweet B indicates that A directly influences the propagation of B. The resulting graph structure is then leveraged to understand how information flows within the cascade and to identify potential sources and spreaders of the rumor. By representing causal relationships explicitly, CausalMamba moves beyond correlational analysis to provide a more nuanced understanding of rumor propagation dynamics.

CausalMamba leverages a learned Directed Acyclic Graph (DAG) – representing causal relationships between tweets – as input to both Graph Convolutional Networks (GCN) and the Mamba state space model. The GCN layer operates on this graph to aggregate information from neighboring nodes, capturing the relational structure of the rumor cascade. This aggregated information, combined with the node features, is then processed by Mamba, which excels at modeling sequential dependencies. By integrating causal knowledge with these neural network architectures, CausalMamba effectively models the propagation dynamics and allows for the identification of influential nodes within the rumor cascade based on their position and impact within the learned causal graph.

Evaluation of CausalMamba on the Twitter15 dataset indicates a performance level of 92% accuracy and 92% Macro-F1. This result positions CausalMamba as competitive with current state-of-the-art methods for rumor propagation analysis. Specifically, incorporating Graph Convolutional Networks (GCN) into the Mamba architecture yielded a quantifiable improvement of +5% in both accuracy and Macro-F1 scores, demonstrating the benefit of leveraging graph-based features within the model.

Intervention and Influence: Predicting Cascade Trajectories

CausalMamba facilitates the simulation of interventions within spreading processes, allowing researchers to virtually remove nodes – such as highly influential individuals – from a network and observe the resulting impact on cascade dynamics. This capability transcends mere observation, enabling a proactive assessment of intervention strategies before implementation. By systematically eliminating specific nodes, the framework quantifies the extent to which their removal alters the overall spread, revealing which actors are most critical to propagation. The resulting data informs targeted interventions, potentially disrupting harmful narratives or mitigating the reach of misinformation with greater precision than random node removal. This simulated environment provides a safe and cost-effective method for testing and refining strategies aimed at controlling the flow of information within complex networks.

The identification of key influencers within complex networks is significantly enhanced by the integration of PageRank with causal analysis within this framework. PageRank, traditionally used to assess the importance of web pages, is adapted to pinpoint nodes with high potential for spreading information, while causal analysis goes further by determining which nodes, if removed or altered, would most effectively disrupt the cascade. This combined approach avoids simply targeting nodes with high activity – which may not be causally linked to the overall spread – and instead focuses on those whose influence is critical to maintaining the propagation of the narrative. Consequently, intervention strategies are guided toward surgical removal of these pivotal nodes, maximizing the impact of limited resources and offering a more effective method for controlling information flow than broad, untargeted approaches.

The ability to simulate interventions within information cascades offers a powerful approach to combating the spread of misinformation and managing potentially harmful narratives online. By identifying key influencers – those nodes with disproportionate impact on cascade dynamics – and virtually removing them, researchers and platform moderators can proactively assess the effectiveness of different mitigation strategies. This isn’t simply about reactive content moderation; it enables a predictive defense against viral falsehoods, allowing for preemptive interventions that can significantly curtail the reach of damaging content before it gains widespread traction. Such simulations can also refine intervention strategies, moving beyond blanket censorship towards targeted removal of key spreaders, minimizing collateral damage to legitimate discourse while maximizing the disruption of harmful narratives. Ultimately, this capability represents a shift towards a more nuanced and effective approach to online information control, focusing on network-level interventions rather than solely addressing individual pieces of content.

The pursuit of demonstrable reliability, central to CausalMamba’s design, echoes a fundamental tenet of elegant computation. As Ken Thompson aptly stated, “If it’s not reproducible, it’s not reliable.” This principle underpins the framework’s approach to temporal rumor causality; by combining state space models with differentiable causal discovery, CausalMamba moves beyond mere detection to establish a provable understanding of rumor propagation. The model doesn’t simply identify that a rumor spreads, but how and why, ensuring the conclusions drawn are not merely empirical observations, but logically derived and replicable insights. This commitment to verifiability is critical for any system aiming to intervene in complex social dynamics.

What’s Next?

The confluence of sequence modeling and causal inference, as demonstrated by CausalMamba, reveals a persistent tension. While the framework successfully identifies propagation pathways, the very notion of ‘rumor causality’ demands further scrutiny. Is it not a category error to apply interventions – to ‘stop’ a rumor – when the underlying mechanism is fundamentally information diffusion, a process governed by stochasticity and bounded rationality? The current formulation treats the rumor itself as the causal agent, rather than a symptom of pre-existing network vulnerabilities.

Future work must address the scalability of differentiable causal discovery within complex, dynamic graphs. The computational complexity, currently asymptotic to the number of potential edges, presents a clear bottleneck. Approximations, perhaps leveraging spectral methods or variational inference, are essential. More critically, the evaluation metrics-detection accuracy alone-are insufficient. A truly rigorous assessment requires counterfactual reasoning: demonstrating not merely that CausalMamba identifies spread, but that its interventions demonstrably alter the system’s trajectory in a predictable and desirable manner.

Ultimately, the pursuit of interpretable AI in this domain raises a philosophical question. To understand how a rumor spreads is not necessarily to understand why. The algorithm illuminates the mechanism, but remains silent on the underlying motivations, anxieties, or cognitive biases that fuel its propagation. A complete theory, one that transcends mere prediction, requires integration with the social sciences – a daunting, yet necessary, undertaking.

Original article: https://arxiv.org/pdf/2511.16191.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Challenge of Scale in Rumor Propagation

Graph Structure and Selective State Space Modeling

CausalMamba: Dissecting the Roots of Propagation

Intervention and Influence: Predicting Cascade Trajectories

What’s Next?

See also: