An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Thakur, Nirmalya; Han, Chia Y.

doi:10.3390/covid2080076

Cited by 28 publications

(14 citation statements)

References 113 publications

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“…An exploratory study and sentiment analysis on a big dataset about the COVID-19 pandemic focusing on the Omicron variant was proposed by Thakur N. et al [32]. The role of Twitter during the COVID-19 pandemic in spreading information and misinformation was presented through sentiment analysis [33].…”

Section: Related Workmentioning

confidence: 99%

Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis

et al. 2023

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This article address approaches to the development of methods for assessing the psychological state of social network members during the coronavirus pandemic through sentiment analysis of messages. The purpose of the work is to determine the psychological tension index by using a previously developed thematically ranked dictionary. Researchers have investigated methods to evaluate psychological tension among social network users and to forecast the psychological distress. The approach is novel in the sense that it ranks emojis by mood, considering both the emotional tone of tweets and the emoji’s dictionary meanings. A novel method is proposed to assess the dynamics of the psychological state of social network users as an indicator of their subjective well-being, and develop targeted interventions for help. Based on the ranking of the Emotional Vocabulary Index (EVI) and Subjective Well-being Index (SWI), a scheme is developed to predict the development of psychological tension. The significance lies in the efficient assessment of the fluctuations in the mental wellness of network users as an indication of their emotions and a prerequisite for further predictive analysis. The findings gave a computed value of EVI of 306.15 for April 2022. The prediction accuracy of 88.75% was achieved.

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Section: Related Workmentioning

confidence: 99%

Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis

et al. 2023

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“…This is a significant increase as compared to recent historical data as far as a high number of Google Searches (indicated by the search interest value being 100) related to Disease X are The internet serves as a platform for public health organizations to efficiently and affordably distribute healthcare information. However, it is crucial that reliable news is disseminated to the general public [115][116][117]. In the last few weeks, many public health organizations have disseminated information pertaining to Disease X, with the aim of reaching a wide audience [118][119][120][121].…”

Section: Data Analysis and Potential Applicationsmentioning

confidence: 99%

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

Thakur,

Cui,

Patel

et al. 2023

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

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“…Some of the recent works in this field include Twitter datasets on hate speech and abusive language [ 48 ], the European migration crisis [ 49 ], natural hazards [ 50 ], misogynistic language [ 51 ], offensive language [ 52 ], civil unrest [ 53 ], exoskeletons [ 54 ], the efficacy of hydroxychloroquine as a treatment for COVID-19 [ 55 ], pregnancy outcomes [ 56 ], drug-related knowledge [ 57 ], the public opinion of people in Indonesia on different matters [ 58 ], a severe storm and F1 tornado that struck Central Pennsylvania [ 59 ], online learning during the COVID-19 Omicron wave [ 60 ], multi-ideology or white supremacy [ 61 ], Sundanese (the second-largest tribe in Indonesia) [ 62 ], vaccines [ 63 ], BlackLivesMatter movement [ 64 ], the Omicron variant of COVID-19 [ 65 ], hazardous events at the Baths of Diocletian site in Rome [ 66 ], memes from Black Twitter [ 67 ], and the Arabic language [ 68 ]. In addition to this, the outbreak of COVID-19 was associated with the development of multiple Twitter datasets, such as Twitter datasets on conversations about COVID-19 in Spanish [ 69 ], Bengali [ 70 ], and English [ 71 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Performing a comprehensive analysis of Tweets related to virus outbreaks, pandemics, and epidemics has been of significant interest to researchers in this field in the recent past. In [ 65 ], the authors presented a study on Tweets posted about the Omicron variant of COVID-19. The specific characteristics of Tweets that were studied included sentiment, language usage, Tweet source, Tweet types (retweets, original Tweets, and replies), and embedded URLs.…”

Section: Literature Reviewmentioning

confidence: 99%

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

Thakur

2022

Infectious Disease Reports

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The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., “neutral” sentiment was present in most of the Tweets. It was followed by “negative” and “positive” sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.

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An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Cited by 28 publications

References 113 publications

Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis

Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis

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

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

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