arXiv subjects

Sean Maudsley-Barton

Publications and source records attributed to Sean Maudsley-Barton.

8 recordsLinked to original sources

arXiv2026

A Multi-Branch Feature Fusion Approach for Health Misinformation Detection and Propagation

This paper presents a multi-branch fusion framework for detecting and characterising the propagation of health misinformation in online social networks (OSNs). Grounded in the Elaboration Likelihood Model (ELM) and the Theory of Planned Behaviour (TPB), the model fuses transformer-based semantics with rhetorical cues, stance representations, and psychologically motivated proxies in a unified multi-task architecture. In addition to binary classification, we introduce the Cognitive Propagation Score (CPS), an interpretable post-hoc auxiliary score computed from psychologically motivated, text-derived cues capturing argument complexity, emotional intensity, and content-derived virality potential, to support diffusion-risk reasoning when engagement ground truth is incomplete or unavailable. Experiments on three benchmark datasets, Constraint, COVID--19\_FNIR, and Monkeypox, show strong classification performance, achieving ROC--AUC up to 0.9999 on COVID--19\_FNIR, while propagation-oriented ranking achieves near-perfect agreement when engagement-derived supervision is available (Monkeypox, Spearman's $ρ= 0.9952$) and similarly high ranking alignment under proxy-based supervision on COVID--19\_FNIR ($ρ= 0.9954$). Compared with representative literature baselines, the fusion model improves detection on Constraint and COVID--19\_FNIR, while Monkeypox remains more challenging, reflecting domain- and signal-specific differences. Ablation analysis further indicates that psychological and rhetorical branches provide complementary gains beyond semantic embeddings. Overall, the framework bridges cognitive theory and neural modelling to improve transparency and to support scalable misinformation monitoring, with future work required to validate CPS against human-centred diffusion judgements.

cs.LG↗

arXiv2026

Integrating Persuasion Theory into the Epidemiological Modelling of Health Misinformation Spread on Social Media

This study presents a hybrid epidemiological and behavioural framework to simulate the spread of health misinformation on social media. We extend the classical Susceptible--Infected--Recovered (SIR) model to a six-compartment structure (SIRMMM), incorporating Misinformed Susceptible (MS), Misinformed Infected (MI), and Misinformed Recovered (MR) compartments to better reflect the dynamics of the misinformation lifecycle. To account for individual-level behavioural variation, we extend the SIRMMM model by integrating psychological signals from the Elaboration Likelihood Model (ELM), including sentiment polarity, engagement metrics, and cognitive effort, which dynamically modulate the misinformation transmission rate, yielding the ELM-SIRMMM framework. Model parameters were estimated using the FibVID dataset, which captures COVID-19 misinformation on Twitter. Generalisability was tested on two additional datasets: MC-Fake (emotional misinformation) and Monant (general health misinformation). Results show that the ELM-SIRMMM model enhances both predictive accuracy and dynamic realism. On FibVID, it decreases RMSE by 5.5%, delays the misinformation peak from day 150 to day 160, and increases its peak prevalence from 6% to 7%. On MC-Fake, it accurately reproduces a flash-rumour pattern, infecting 38% of users by day 45 and achieving 97% misinformation recovery, all while maintaining model accuracy. In contrast, minimal behavioural signal variability in the Monant dataset leads to marginal benefit, with only a 3% peak and 57% of users remaining susceptible. These findings suggest that structural elaboration alone is insufficient. Functional realism in modelling misinformation spread requires dynamic psychological inputs that vary meaningfully across time and contexts.

cs.SI↗

arXiv2026

BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter

Health misinformation circulating during pandemics can gain traction rapidly, creating harmful narratives that compete with public health guidance. Most topic-modelling pipelines treat engagement as an external outcome, limiting their ability to prioritise semantically coherent topics that are also rapidly diffusing. We introduce BERTopic-VP, a virality-prioritised topic-modelling framework that combines contextual embedding-based clustering (BERTopic) with a post hoc Virality Prioritisation (VP) layer. The pipeline is complemented by a two-stage hybrid misinformation detection module that fuses a supervised content-based classifier with an external verification signal derived from public-health knowledge bases. Applied to three benchmark datasets, COVID-19_FNIR, Monkeypox, and Constraint, the framework achieves strong classification performance, with F1 up to 0.950 and ROC-AUC up to 0.989, while identifying high-impact clusters under top 1%, 5%, and 10% VP thresholds. For datasets without native engagement metadata, prioritisation is based on a logistic propensity-to-spread score, used as an ordinal proxy for diffusion potential rather than a direct measure of engagement. The results show that integrating semantic structure, virality-aware ranking, and affective-linguistic profiling enables scalable and interpretable comparative analysis of misinformation across pandemics. The proposed framework supports monitoring-oriented early warning by surfacing low-volume but high-risk narratives for analyst review.

cs.CL↗

arXiv2025

Linguistic Patterns in Pandemic-Related Content: A Comparative Analysis of COVID-19, Constraint, and Monkeypox Datasets

This study conducts a computational linguistic analysis of pandemic-related online discourse to examine how language distinguishes health misinformation from factual communication. Drawing on three corpora: COVID-19 false narratives (n = 7588), general COVID-19 content (n = 10700), and Monkeypox-related posts (n = 5787), we identify significant differences in readability, rhetorical markers, and persuasive language use. COVID-19 misinformation exhibited markedly lower readability scores and contained over twice the frequency of fear-related or persuasive terms compared to the other datasets. It also showed minimal use of exclamation marks, contrasting with the more emotive style of Monkeypox content. These patterns suggest that misinformation employs a deliberately complex rhetorical style embedded with emotional cues, a combination that may enhance its perceived credibility. Our findings contribute to the growing body of work on digital health misinformation by highlighting linguistic indicators that may aid detection efforts. They also inform public health messaging strategies and theoretical models of crisis communication in networked media environments. At the same time, the study acknowledges limitations, including reliance on traditional readability indices, use of a deliberately narrow persuasive lexicon, and reliance on static aggregate analysis. Future research should therefore incorporate longitudinal designs, broader emotion lexicons, and platform-sensitive approaches to strengthen robustness.

cs.CL↗

arXiv2025

Advanced Health Misinformation Detection Through Hybrid CNN-LSTM Models Informed by the Elaboration Likelihood Model (ELM)

Health misinformation during the COVID-19 pandemic has significantly challenged public health efforts globally. This study applies the Elaboration Likelihood Model (ELM) to enhance misinformation detection on social media using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The model aims to enhance the detection accuracy and reliability of misinformation classification by integrating ELM-based features such as text readability, sentiment polarity, and heuristic cues (e.g., punctuation frequency). The enhanced model achieved an accuracy of 97.37%, precision of 96.88%, recall of 98.50%, F1-score of 97.41%, and ROC-AUC of 99.50%. A combined model incorporating feature engineering further improved performance, achieving a precision of 98.88%, recall of 99.80%, F1-score of 99.41%, and ROC-AUC of 99.80%. These findings highlight the value of ELM features in improving detection performance, offering valuable contextual information. This study demonstrates the practical application of psychological theories in developing advanced machine learning algorithms to address health misinformation effectively.

cs.SI↗

arXiv2025

Analysing Health Misinformation with Advanced Centrality Metrics in Online Social Networks

The rapid spread of health misinformation on online social networks (OSNs) during global crises such as the COVID-19 pandemic poses challenges to public health, social stability, and institutional trust. Centrality metrics have long been pivotal in understanding the dynamics of information flow, particularly in the context of health misinformation. However, the increasing complexity and dynamism of online networks, especially during crises, highlight the limitations of these traditional approaches. This study introduces and compares three novel centrality metrics: dynamic influence centrality (DIC), health misinformation vulnerability centrality (MVC), and propagation centrality (PC). These metrics incorporate temporal dynamics, susceptibility, and multilayered network interactions. Using the FibVID dataset, we compared traditional and novel metrics to identify influential nodes, propagation pathways, and misinformation influencers. Traditional metrics identified 29 influential nodes, while the new metrics uncovered 24 unique nodes, resulting in 42 combined nodes, an increase of 44.83%. Baseline interventions reduced health misinformation by 50%, while incorporating the new metrics increased this to 62.5%, an improvement of 25%. To evaluate the broader applicability of the proposed metrics, we validated our framework on a second dataset, Monant Medical Misinformation, which covers a diverse range of health misinformation discussions beyond COVID-19. The results confirmed that the advanced metrics generalised successfully, identifying distinct influential actors not captured by traditional methods. In general, the findings suggest that a combination of traditional and novel centrality measures offers a more robust and generalisable framework for understanding and mitigating the spread of health misinformation in different online network contexts.

cs.SI↗

arXiv2024

A Comparative Study of Hybrid Models in Health Misinformation Text Classification

This study evaluates the effectiveness of machine learning (ML) and deep learning (DL) models in detecting COVID-19-related misinformation on online social networks (OSNs), aiming to develop more effective tools for countering the spread of health misinformation during the pan-demic. The study trained and tested various ML classifiers (Naive Bayes, SVM, Random Forest, etc.), DL models (CNN, LSTM, hybrid CNN+LSTM), and pretrained language models (DistilBERT, RoBERTa) on the "COVID19-FNIR DATASET". These models were evaluated for accuracy, F1 score, recall, precision, and ROC, and used preprocessing techniques like stemming and lemmatization. The results showed SVM performed well, achieving a 94.41% F1-score. DL models with Word2Vec embeddings exceeded 98% in all performance metrics (accuracy, F1 score, recall, precision & ROC). The CNN+LSTM hybrid models also exceeded 98% across performance metrics, outperforming pretrained models like DistilBERT and RoBERTa. Our study concludes that DL and hybrid DL models are more effective than conventional ML algorithms for detecting COVID-19 misinformation on OSNs. The findings highlight the importance of advanced neural network approaches and large-scale pretraining in misinformation detection. Future research should optimize these models for various misinformation types and adapt to changing OSNs, aiding in combating health misinformation.

cs.IR↗

arXiv2017

A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data

The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives. However, it is currently under used. Developments in depth cameras, such as Kinect, have opened up the use of motion analysis in settings such as GP surgeries, care homes and private homes. To provide an insight into the use of Kinect in the healthcare domain, we present a review of the current state of the art. We then propose a method that can represent human motions from time-series data of arbitrary length, as a single vector. Finally, we demonstrate the utility of this method by extracting a set of clinically significant features and using them to detect the age related changes in the motions of a set of 54 individuals, with a high degree of certainty (F1- score between 0.9 - 1.0). Indicating its potential application in the detection of a range of age-related motion impairments.

cs.CV↗