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

Mkululi Sikosana; Sean Maudsley-Barton; Oluwaseun Ajao

cs.CLAug 16, 2026

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

Authors: Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao

Organizations: Department Of Computing And Mathematics, Manchester Metropolitan University, Manchester, UK

Abstract

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.

Figures & tables

Dataset	Observed engagement?	VP signal used	Interpretation
Monkeypox	Yes	Observed $E_{i}$	Realised interaction counts drive VP ranking.
COVID–19_FNIR	No	Proxy $\tilde{E}_{i}$ from $\eta_{i}$ , equivalently $\hat{p}_{i}$	VP ranking reflects predicted diffusion propensity, not realised engagement.
Constraint	No	Proxy $\tilde{E}_{i}$ from $\eta_{i}$ , equivalently $\hat{p}_{i}$	VP ranking reflects predicted diffusion propensity, not realised engagement.

TABLE I: Engagement metadata availability and VP signal used in each dataset.

Component	Hyperparameter	Value
UMAP	n_neighbors	15
UMAP	min_dist	0.1
UMAP	n_components	5
HDBSCAN	min_cluster_size	50
HDBSCAN	min_samples	10

TABLE II: UMAP and HDBSCAN configuration used for BERTopic-VP.

Fig. 1: End-to-End Virality-Prioritised Misinformation Detection Pipeline.

Dataset	Purity	NMI	ARI	# Clusters	Coherence ( $C_{v}$ )
COVID–19_FNIR	0.742	0.210	0.181	112	0.084
Monkeypox	0.940	0.281	0.233	87	0.286
Constraint	0.522	0.036	-0.002	5	0.463

TABLE III: Comparative unsupervised topic clustering performance under BERTopic-VP. The number of clusters corresponds to the final non-outlier topic count used as the VP denominator.

Dataset	Accuracy	Precision	Recall	F1	ROC-AUC	PR-AUC
COVID–19_FNIR	0.906	0.897	0.918	0.907	0.966	0.961
Monkeypox	0.956	0.937	0.963	0.950	0.989	0.985
Constraint	0.895	0.901	0.897	0.899	0.951	0.953

TABLE IV: Comparative Supervised Classification Performance

Pandemic Source	Polarity	Joy	Fear	Anger	Sadness
COVID–19_FNIR	0.051	0.202	0.150	0.132	0.118
Constraint	0.037	0.181	0.167	0.144	0.103
Monkeypox	0.022	0.135	0.226	0.193	0.152

TABLE V: Average sentiment polarity and emotion scores by pandemic source.

Fig. 2: Distribution of emotion scores (anger, fear, joy, sadness, and disgust) across COVID–19_FNIR and mpox misinformation tweets, highlighting divergent affective triggers.

Group	TTR	Avg. Sentence Length	Flesch Reading Ease	Flesch–Kincaid Grade Level
COVID–19_FNIR	0.89	18.55	30.26	12.95
Monkeypox	0.92	15.55	55.73	8.90

TABLE VI: Average linguistic metrics for COVID–19_FNIR and mpox misinformation tweets.

Dataset	Top 1%	Top 5%	Top 10%
COVID–19_FNIR	9/112	15/112	26/112
Monkeypox	5/87	11/87	16/87

TABLE VII: VP-flagged cluster counts under different virality thresholds.

$\mathbf{R}$	Mean ARI	ARI range	$\mathbf{k}$ range	Noise range
10	0.709	[0.654, 0.770]	[42, 48]	[0.368, 0.398]

TABLE VIII: BERTopic rerun-sensitivity summary for Constraint (varying UMAP seed only,

R=10

Fig. 3: Hierarchical clustering dendrogram of representative topic clusters identified via BERTopic-VP across COVID–19_FNIR, Constraint, and Monkeypox datasets. Topic labels follow the convention COVID_k , Constraint_k , and Monkeypox_k , where $k$ denotes the topic index. The vertical axis indicates semantic linkage distance under cosine-based hierarchical clustering.

Dataset	Cluster ID	Virality Score ( $v_{j}$ )	Top Terms	Misinfo Presence
COVID–19_FNIR	1	3.457	masks, cure, prevent	Mostly factual
Monkeypox	11	13.000	scientists, losing fight	None detected
Monkeypox	25	9.765	cases, states, prevalence	None detected
Constraint	2	2.681	lagos, discharged, fct	All misinformation
Constraint	4	2.649	covid, cases, reported	Mixed

TABLE IX: Representative VP-flagged clusters with cluster-level virality score

v_{j}

Dataset	Cluster theme	Representative tweets (paraphrased)	$\mathbf{C}_{v}$
COVID–19_FNIR (Low)	Mixed preventive and political claims	“The vaccine is useless, the virus is fake, and the government just wants control.” “Masks work but lockdowns destroy jobs.” The framing conflicts across posts.	0.084
Constraint (High)	Vaccine side-effects narrative	“My friend took the shot and got sick for weeks.” “Vaccines have dangerous chemicals that doctors hide.” A single, consistent misinformation frame.	0.463
Mpox (Low)	Broad outbreak speculation	“This virus comes from labs in Europe.” “It is fake news made up by the media.” Mixed conspiracy and denial themes.	0.22
Mpox (High)	Vaccine rollout critique	“People in rural areas still cannot access vaccines.” “Authorities promise doses but none arrive.” Focused grievance on distribution failure.	0.33

TABLE X: Qualitative examples of low- vs. high-coherence clusters in COVID–19_FNIR, Constraint and Monkeypox datasets

Model / Study	Dataset / Domain	$\mathbf{C}_{v}$	Notes
BERTopic-VP (this study)	COVID–19_FNIR misinformation tweets	0.084	Lowest coherence, reflecting narrative fragmentation.
BERTopic-VP (this study)	Mpox misinformation tweets	0.286	Higher cohesion, with fewer dominant frames.
BERTopic-VP (this study)	Constraint misinformation tweets	0.463	Highest cohesion in this study’s datasets.
BERTopic (reported) [ 50 ]	COVID–19 vaccine tweets	0.52	Higher coherence due to a more thematically uniform dataset.
BERTopic (reported) [ 51 ]	Facebook COVID–19 posts	0.53	Performs comparably to and, in some settings, modestly better than LDA on multilingual health data.

TABLE XI: Within-study BERTopic-VP coherence and externally reported BERTopic coherence values used for contextual comparison.

Model / Study	Dataset / Domain	$\mathbf{C}_{v}$	Notes
LDA [ 19 ]	Twitter vaccine discourse	$\sim 0.47$	Lower robustness in microtext settings.
LDA [ 52 ]	Multi-domain comments	0.031	Lowest performance on short-text corpora.

TABLE XII: Selected reported

C_{v}

coherence values for a classical baseline (LDA) on short-text pandemic corpora, included for context only and not re-implemented in this paper.

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

Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao

Department Of Computing And Mathematics, Manchester Metropolitan University, Manchester, UK

Fake News Detection Automated Fact-Checking Health Misinformation Detection Feature Fusion Misinformation Diffusion

Open paper AskPDF arXiv

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.

Abstract

Health Misinformation Detection

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