Twitter conversations predict the daily confirmed COVID-19 cases

Lamsal, Rabindra; Harwood, Aaron; Read, Maria Rodriguez

doi:10.1016/j.asoc.2022.109603

Cited by 19 publications

(12 citation statements)

References 43 publications

(46 reference statements)

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“…The network architecture applied to predict sentiment labels is a fine-tuned BERT model [29] trained with SemEval-2017 corpus and based on a pre-trained language model for English tweets (BERTweet) trained on 850M English tweets.…”

Section: Dataset Labelling and Statisticsmentioning

confidence: 99%

Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers

et al. 2023

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Energy prices have gone up gradually since last year, but a drastic hike has been observed recently in the past couple of months, affecting people's thrift. This, coupled with the load shedding and energy shortages in some parts of the world, led many to show anger and bitterness on the streets and on social media. Despite subsidies offered by many Governments to their citizens to compensate for high energy bills, the energy price hike is a trending topic on Twitter. However, not much attention is paid to opinion mining on social media posts on this topic. Therefore, in this study, we propose a solution that takes advantage of both a transformer-based sentiment analysis method and topic modeling to explore public engagement on Twitter regarding energy prices rising. The former method is employed to annotate the valence of the collected tweets as positive, neutral and negative, whereas the latter is used to discover hidden topics/themes related to energy prices for which people have expressed positive or negative sentiments. The proposed solution is tested on a dataset composed of 366,031 tweets collected from 01 January 2021 to 18 June 2022. The findings show that people have discussed a variety of topics which directly or indirectly affect energy prices. Moreover, the findings reveal that the public sentiment towards these topics has changed over time, in particular, in 2022 when negative sentiment was dominant.

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Section: Dataset Labelling and Statisticsmentioning

confidence: 99%

Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers

et al. 2023

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“…Various aspects of public health can be explored using social media and most notably Twitter [ 42 , 43 ]. In a systematic review published some years before the pandemic, six categories of Twitter usage were identified from users trying to obtain health information [ 31 ].…”

Section: Literature Reviewmentioning

confidence: 99%

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

Polyzos

Fotiadis

Huan

2023

Technology in Society

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“…A similar study that focused on performing sentiment analysis of Tweets originating from multiple countries was performed by Dubey [56]. In addition to the above, several studies focused on performing sentiment analysis of Tweets about COVID-19 originating from different countries such as the United Kingdom [57][58][59][60][61][62], United States [59,60,[63][64][65][66][67], Canada [68][69][70][71][72], India [73][74][75][76][77], Australia [78][79][80], and Brazil [81][82][83].…”

Section: Recent Work That Focused On Sentiment Analysis Of Tweets Abo...mentioning

confidence: 99%

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

Thakur¹

2023

Preprint

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Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

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Twitter conversations predict the daily confirmed COVID-19 cases

Cited by 19 publications

References 43 publications

Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers

Soaring Energy Prices: Understanding Public Engagement on Twitter Using Sentiment Analysis and Topic Modeling With Transformers

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

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