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

• URL: http://arxiv.org/abs/2510.07579v1
• Date: Wed, 08 Oct 2025 21:51:34 GMT
• Title: Linguistic Patterns in Pandemic-Related Content: A Comparative Analysis of COVID-19, Constraint, and Monkeypox Datasets
• Authors: Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao,
• Abstract summary: This study conducts a computational linguistic analysis of pandemic-related online discourse.<n>We identify significant differences in readability, rhetorical markers, and persuasive language use.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Linguistic Indicators of Early Cognitive Decline in the DementiaBank Pitt Corpus: A Statistical and Machine Learning Study [4.417564179511245]
  This study analyzes spontaneous speech transcripts from the DementiaBank Pitt Corpus using three linguistic representations.<n> syntactic and grammatical features retain strong discriminative power even in the absence of lexical content.<n>This study supports the use of linguistically grounded features for transparent and reliable language-based cognitive screening.
  arXiv  Detail & Related papers  (2026-02-11T16:53:57Z)
• Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation [52.51005875755718]
  We focus on EEG-to-text decoding and address its hallucination issue through the lens of posterior collapse.<n>Acknowledging the underlying mismatch in information capacity between EEG and text, we reframe the decoding task as semantic summarization of core meanings.<n>Experiments on the public ZuCo dataset demonstrate that GLIM consistently generates fluent, EEG-grounded sentences.
  arXiv  Detail & Related papers  (2025-05-21T05:29:55Z)
• Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights [0.0]
  This study conducts a comparative sentiment analysis of public perceptions surrounding COVID-19 and mpox by leveraging extensive datasets of 147,475 and 106,638 tweets, respectively.<n>The analysis highlights significant differences in public sentiment driven by disease characteristics, media representation, and pandemic fatigue.<n>Through the lens of sentiment polarity and thematic trends, this study offers valuable insights into tailoring public health messaging, mitigating misinformation, and fostering trust during concurrent health crises.
  arXiv  Detail & Related papers  (2025-05-12T10:37:33Z)
• Contrastive Learning with Counterfactual Explanations for Radiology Report Generation [83.30609465252441]
  We propose a textbfCountertextbfFactual textbfExplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for understanding how decisions made by algorithms can be changed by asking what if'' scenarios. Experiments on two benchmarks demonstrate that leveraging the counterfactual explanations enables CoFE to generate semantically coherent and factually complete reports.
  arXiv  Detail & Related papers  (2024-07-19T17:24:25Z)
• Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding [72.18719355481052]
  We introduce a novel task called Medical Report Grounding (MRG)<n>MRG aims to directly identify diagnostic phrases and their corresponding grounding boxes from medical reports in an end-to-end manner.<n>We propose uMedGround, a robust and reliable framework that leverages a multimodal large language model to predict diagnostic phrases.
  arXiv  Detail & Related papers  (2024-04-10T07:41:35Z)
• Quantifying the redundancy between prosody and text [67.07817268372743]
  We use large language models to estimate how much information is redundant between prosody and the words themselves. We find a high degree of redundancy between the information carried by the words and prosodic information across several prosodic features. Still, we observe that prosodic features can not be fully predicted from text, suggesting that prosody carries information above and beyond the words.
  arXiv  Detail & Related papers  (2023-11-28T21:15:24Z)
• A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data [2.641576480886427]
  The COVID-19 infodemic, characterized by the rapid spread of misinformation and unverified claims related to the pandemic, presents a significant challenge. This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms.
  arXiv  Detail & Related papers  (2023-11-14T08:55:11Z)
• Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model [7.825530847570242]
  We identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression. The correlation between topic shifts and changes in depression severity over time was also investigated.
  arXiv  Detail & Related papers  (2023-08-22T20:30:59Z)
• Leveraging text data for causal inference using electronic health records [1.4182510510164876]
  This paper presents a unified framework for leveraging text data to support causal inference with electronic health data. We show how incorporating text data in a traditional matching analysis can help strengthen the validity of an estimated treatment effect. We believe these methods have the potential to expand the scope of secondary analysis of clinical data to domains where structured EHR data is limited.
  arXiv  Detail & Related papers  (2023-06-09T16:06:02Z)
• Natural Language Decompositions of Implicit Content Enable Better Text Representations [52.992875653864076]
  We introduce a method for the analysis of text that takes implicitly communicated content explicitly into account.<n>We use a large language model to produce sets of propositions that are inferentially related to the text that has been observed.<n>Our results suggest that modeling the meanings behind observed language, rather than the literal text alone, is a valuable direction for NLP.
  arXiv  Detail & Related papers  (2023-05-23T23:45:20Z)
• Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition [142.42920413017163]
  Current methods often generate the most common sentences due to dataset bias for individual case. We propose a novel framework that unifies template retrieval and sentence generation to handle both common and rare abnormality.
  arXiv  Detail & Related papers  (2021-01-09T04:33:27Z)
• Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952) [0.8114550931351494]
  Interdisciplinary research investigates more than 100 reports from the third plague pandemic (1894- 1952) Our goal is to develop structured accounts of some of the most significant concepts that were used to understand the epidemiology of the third plague pandemic around the globe.
  arXiv  Detail & Related papers  (2020-02-04T17:16:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.