Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies (Preprint) DOI
Xiangyu Yan, Zhuo Li, Chunxia Cao

et al.

Published: Nov. 26, 2023

BACKGROUND The mpox pandemic has caused widespread public concern around the world. spread of misinformation through internet and social media could lead to an infodemic that poses challenges control. OBJECTIVE This review aims summarize mpox-related infodemiology studies determine characteristics, influence, prevention, control measures propose prospects for future research. METHODS scoping was conducted based on a structured 5-step methodological framework. A comprehensive search performed using PubMed, Web Science, Embase, Scopus, with searches completed by April 30, 2024. After study selection data extraction, main topics were categorized summarized in 4 aspects, including trend analysis online information volume, content posts comments, emotional sentiment characteristics content, prevention infodemic. RESULTS total 1607 articles retrieved from databases according keywords, 61 included final analysis. World Health Organization’s declaration health emergency international July 2022, number related began growing rapidly. Google most widely used engine platform (9/61, 15%), Twitter app (32/61, 52%) researchers. Researchers 33 countries concerned about infodemic–related topics. Among them, top 3 article publication United States (27 studies), India (9 Kingdom (7 studies). Studies trends showed volume skyrocketed at beginning outbreak, especially when Organization provided important declarations. There large amount negative discriminatory hostile against gay, bisexual, other men who have sex men. Given infodemic, several positive measures, timely active publishing professional, high-quality, easy-to-understand online; strengthening surveillance early warning data; taking protect key populations harm CONCLUSIONS summary evidence previous is valuable understanding formulating measures. It essential researchers policy makers establish prediction approaches targeted intervention methods dealing future.

Language: Английский

Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning DOI Creative Commons
Andy Edinger, Danny Valdez, Eric R. Buhi

et al.

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e43841 - e43841

Published: May 9, 2023

Background Shortly after the worst of COVID-19 pandemic, an outbreak mpox introduced another critical public health emergency. Like was characterized by a rising prevalence misinformation on social media, through which many US adults receive and engage with news. Digital continues to challenge efforts officials in providing accurate timely information public. We examine evolving topic distributions media narratives during map tension between rapidly diffusing communication. Objective This study aims observe topical themes occurring large-scale collection tweets about using deep learning. Methods leveraged data set comprised all mpox-related that were posted May 7, 2022, July 23, 2022. then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) content each tweet generate representation its high-dimensional vector space, where semantically similar will be located closely together. projected embeddings 2D applying principal component analysis Uniform Manifold Approximation Projection (UMAP). Finally, we group these points into 7 clusters k-means clustering analyze cluster determine dominant topics. over time evaluate longitudinal thematic changes. Results Our deep-learning pipeline revealed distinct content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex men, (5) case reports, (6) vaccination, (7) World Health Organization (WHO). Clusters largely communicated erroneous or irrelevant began earlier grew faster, reaching wider audience than later communications official instances officials. Conclusions Within few weeks first reported cases, avalanche mostly false, misleading, irrelevant, damaging started circulate media. Official institutions, including WHO, acted promptly, reports within weeks, but overshadowed spreading chatter. results point need for real-time monitoring optimize responses emergencies.

Language: Английский

Citations

34
Discourse Analysis of International Scientific Organizations in the 2022 Russia–Ukraine Conflict: A Natural Language Processing Approach DOI Creative Commons
Jiayue Lu, Xiaoli Chen, Xuezhao Wang

et al.

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 89 - 89

Published: Jan. 24, 2025

The scientific community has not stayed outside the Russia–Ukraine conflict. This study analyzes attitudes and roles of international organizations in conflict, based on 923 official statements, through a combination discourse analysis Natural Language Processing (NLP) techniques, including sentiment topic modeling. findings reveal that 527 issued with 47% explicitly “supporting Ukraine condemning Russia”, 13% maintaining neutral stance. These statements reflect diverse concerns, conflict’s immediate humanitarian impact, disruption to collaboration, broader political social implications. research contributes understanding how navigate conflict contexts by systematically uncovering their attitudes, focus areas, actions. Through thematic analysis, demonstrates these articulate positions, advocate for specific measures, leverage influence address issues such as economic support, healthcare assistance. By identifying behaviors, clarifies strategic play shaping mediating relations, offering key insights into impact during geopolitical crises.

Language: Английский

Citations

1
Graph embedding approaches for social media sentiment analysis with model explanation DOI Creative Commons

V. S. Anoop,

C. Subin Krishna,

Usharani Hareesh Govindarajan

et al.

International Journal of Information Management Data Insights, Journal Year: 2024, Volume and Issue: 4(1), P. 100221 - 100221

Published: Feb. 28, 2024

ChatGPT, the revolutionary chat agent launched in November 2022, is still an active topic of discussion among technology enthusiasts. This open-ended chatbot allows human-like conversations with it on almost all topics since was trained millions documents and developed as a large language model. Since its inception, there have been several discussions deliberations, especially twitter other social media handles, potential ChatGPT how power artificial intelligence growing leaps bounds general. These platforms also witnessed debates negative side such adversely affecting integrity ethics biased training data. work uses graph neural network embeddings machine learning algorithms for classifying user sentiments ChatGPT. We collected total 8202 tweets manually labeled them into multiple classes positive, negative, neutral. make models explainable using SHAP (SHapley Additive exPlanations), which game theoretical technique explaining output any models. paper publishes our dataset researchers to use train advanced classification When proposed approach compared some chosen baselines, embedding-based classifiers were found be outperforming terms precision, recall, accuracy.

Language: Английский

Citations

5
Exploring the Application of Natural Language Processing for Social Media Sentiment Analysis DOI

Vishakha Joseph,

Chandra Prakash Lora,

T Narmadha

et al.

Published: March 1, 2024

Natural language processing (NLP) is a developing vicinity of studies that has the potential to provide state-of-the-art sentiment analysis abilities inside realm social media. Its software involves collection facts from networks, which include Twitter or FB, after reworking these records into an established format amenable NLP techniques. The last aim make automated predictions media posts, can be used aid choice-making methods customers, marketers, researchers, and many others. As quantity data created by users continues grow, so does complexity appropriately extracting facts. allows for efficient automatic approaches insights conversations. However, venture lies in finding extract sizeable amount textual content efficaciously. It evaluates discusses modern techniques equipment apply analysis.

Language: Английский

Citations

4
Social media and the response to mpox DOI
David C. Coker, Tareq Mohammed Ali AL-Ahdal

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 101 - 113

Published: Jan. 1, 2025

Language: Английский

Citations

0
Underneath Social Media Texts: Sentiment Responses to Public Health Emergency During 2022 COVID-19 Pandemic in China DOI
Bingyao Jia, Mao Xie, Jing Wu

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105239 - 105239

Published: Jan. 1, 2025

Language: Английский

Citations

0
We Chased COVID-19; Did We Forget Measles? - Public Discourse and Sentiment Analysis on Spiking Measles Cases Using Natural Language Processing DOI

V. S. Anoop,

Jose Thekkiniath, Usharani Hareesh Govindarajan

et al.

Lecture notes in computer science, Journal Year: 2023, Volume and Issue: unknown, P. 147 - 158

Published: Jan. 1, 2023

Language: Английский

Citations

8
Climate Change Sentiment Analysis Using Domain Specific Bidirectional Encoder Representations From Transformers DOI Creative Commons

V. S. Anoop,

Tandava Krishnan,

Ali Daud

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 114912 - 114922

Published: Jan. 1, 2024

Language: Английский

Citations

2
Characteristics, Influence, Prevention, and Control Measures of the Mpox Infodemic: Scoping Review of Infodemiology Studies DOI Creative Commons
Xiangyu Yan, Zhuo Li, Chunxia Cao

et al.

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e54874 - e54874

Published: Aug. 30, 2024

The mpox pandemic has caused widespread public concern around the world. spread of misinformation through internet and social media could lead to an infodemic that poses challenges control.

Language: Английский

Citations

2
Sentiment Classification of Diabetes-Related Tweets Using Transformer-Based Deep Learning Approach DOI

V. S. Anoop

Communications in computer and information science, Journal Year: 2023, Volume and Issue: unknown, P. 203 - 214

Published: Jan. 1, 2023

Language: Английский

Citations

5