Analyzing Public Reactions during the MPox Outbreak: Findings from Topic Modeling of Tweets

Published: Sept. 1, 2023

In the last decade and a half, world has experienced outbreak of range viruses such as COVID-19, H1N1, flu, Ebola, Zika Virus, Middle East Respiratory Syndrome (MERS), Measles, West Nile just to name few. During these virus outbreaks, usage effectiveness social media platforms increased significantly served virtual communities, enabling their users share exchange information, news, perspectives, opinions, ideas, comments related outbreaks. Analysis this Big Data conversations outbreaks using concepts Natural Language Processing Topic Modeling attracted attention researchers from different disciplines Healthcare, Epidemiology, Science, Medicine, Computer Science. The recent MPox resulted in tremendous increase Twitter. Prior works field have primarily focused on sentiment analysis content Tweets, few that topic modeling multiple limitations. This paper aims address research gap makes two scientific contributions field. First, it presents results performing 601,432 Tweets about 2022 Mpox outbreak, which were posted Twitter between May 7, 2022, March 3, 2023. indicate during time may be broadly categorized into four distinct themes - Views Perspectives MPox, Updates Cases Investigations Mpox, LGBTQIA+ Community, COVID-19. Second, findings Tweets. show theme was most popular (in terms number posted) MPox. It is followed by COVID-19 respectively. Finally, comparison with prior also presented highlight novelty significance work.

Language: Английский

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Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior

Computation, Journal Year: 2023, Volume and Issue: 11(11), P. 234 - 234

Published: Nov. 17, 2023

During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, forecast worldwide perception, readiness, reactions, response linked these outbreaks. The outbreak of Marburg Virus disease (MVD), high fatality rate MVD, conspiracy theory linking FEMA alert signal United States on 4 October 2023 with MVD a zombie outbreak, resulted diverse range reactions general public which has transpired surge this context. This “Marburg Virus” featuring list top trending topics Twitter 3 2023, “Emergency Alert System” “Zombie” 2023. No prior work field mined analyzed emerging trends presented paper aims address research gap makes multiple scientific contributions field. First, it presents results performing time-series forecasting search interests related from 216 different regions global scale using ARIMA, LSTM, Autocorrelation. present optimal model for each regions. Second, correlation between zombies was investigated. findings show that there were several where statistically significant MVD-related searches zombie-related Google Finally, other helped identify those significant.

Language: Английский

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Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

Applied System Innovation, Journal Year: 2023, Volume and Issue: 6(5), P. 92 - 92

Published: Oct. 12, 2023

This paper presents multiple novel findings from a comprehensive analysis of dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023. First, the shows that average number per month wherein individuals self-reported COVID was considerably high in 2022 as compared to 2021. Second, sentiment using VADER show percentages with positive, negative, neutral sentiments were 43.1%, 42.7%, 14.2%, respectively. To add this, most positive sentiment, well negative not highly polarized. Third, result tokenization indicates tweeting patterns (in terms tokens used) similar for Tweets. Analysis these results also there no direct relationship used intensity expressed Finally, granular showed emotion sadness It followed by emotions fear, neutral, surprise, anger, joy, disgust,

Language: Английский

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A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

Data, Journal Year: 2023, Volume and Issue: 8(11), P. 163 - 163

Published: Oct. 26, 2023

The World Health Organization added Disease X to their shortlist of blueprint priority diseases represent a hypothetical, unknown pathogen that could cause future epidemic. During different virus outbreaks the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends mine multimodal components web behavior study, investigate, analyze global awareness, preparedness, response associated with these respective outbreaks. As world prepares for X, dataset on related would be crucial contribute towards timely advancement research in this field. Furthermore, none prior works field have focused development compile relevant data, which help prepare X. To address challenges, work presents emerged geographic regions world, between February 2018 August 2023. Specifically, search interests 94 regions. was developed by collecting data using Trends. all each month time range are available dataset. This paper also discusses compliance FAIR principles scientific management. Finally, an analysis is presented uphold applicability, relevance, usefulness investigation questions interrelated fields Big Data, Data Mining, Healthcare, Epidemiology, Analysis specific focus

Language: Английский

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