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

Polyzos, Stathis; Fotiadis, Anestis; Huan, Tzung‐Cheng

doi:10.1016/j.techsoc.2023.102198

Cited by 5 publications

(1 citation statement)

References 86 publications

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“…It revealed that individuals are experiencing a crisis due to the coronavirus, leading to heightened anxiety levels fueled by COVID-19-related news. In a separate investigation, Polyzos E et al [22] explored the effects of online campaigns honoring frontline workers on COVID-19 outcomes in the United Kingdom. By employing random regression forests and cointegration analysis on Twitter data, they identified delayed negative impacts from unfavorable sentiments to vaccination rates and from new cases to negative sentiment posts.…”

Section: Related Workmentioning

confidence: 99%

Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network

Al Sailawi,

Kangavari

2024

AIMSPH

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<abstract> <p>This study explores the use of artificial intelligence (AI) to analyze information from X (previously Twitter) feeds related to COVID-19, specifically focusing on the time following the World Health Organization's (WHO) vaccination announcement. This aspect of the pandemic has not been studied by other researchers focusing on vaccination news. By utilizing advanced AI algorithms, the research aims to examine a wealth of data, sentiments, and trends to enhance crisis management strategies effectively. Our methods involved collecting a dataset of tweets from December 2020 to July 2021. By using specific keywords strategically, we gathered a substantial 15.5 million tweets, focusing on important hashtags like #vaccine and #coronavirus while filtering out irrelevant replies and retweets. The assessment of three different machine learning models–BiLSTM, FFNN, and CNN – highlights the exceptional performance of BiLSTM, achieving an impressive F1-score of 0.84 on the test set, with Precision and Recall metrics at 0.85 and 0.83, respectively. The study provides a detailed visualization of global sentiments on COVID-19 topics, with a main goal of extracting insights to manage public health crises effectively. Sentiment labels were predicted using various classification models and categorized as positive, negative, and neutral for each country after adjusting for population differences. An important finding from the analysis is the variation in sentiments across regions, for instance, with Eastern European countries showing positive views on post-vaccination economic recovery, while China and the United States express negative opinions on the same topic.</p> </abstract>

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Section: Related Workmentioning

confidence: 99%

Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network

Al Sailawi,

Kangavari

2024

AIMSPH

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Dynamic Returns Connectedness: Portfolio Hedging Implications During the COVID‐19 Pandemic and the Russia–Ukraine War

Rubbaniy,

Khalid,

Syriopoulos

et al. 2024

Journal of Futures Markets

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We apply a Time‐Varying Parameter Vector Auto Regressive (TVP‐VAR) connectedness approach on global assets to investigate time‐varying dynamic connectedness, portfolio performance, and hedge effectiveness during COVID‐19 and the Russia–Ukraine war. With increased connectedness and the changing role of energy and soft commodities during these two events, we find the minimum correlation (connectedness) portfolio performing better during COVID‐19 and the Russia–Ukraine war and that cumulative returns of portfolios are higher during COVID‐19. Additionally, we find varying (stable) hedge effectiveness of equity market indices and soft commodities (cryptocurrencies). This paper provides specific insights to investors about using optimal portfolios and hedging during pandemics and military conflicts.

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Frontline Heroes

Sumner,

Kinsella

2023

Encyclopedia of Heroism Studies

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

Cited by 5 publications

References 86 publications

Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network

Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network

Dynamic Returns Connectedness: Portfolio Hedging Implications During the COVID‐19 Pandemic and the Russia–Ukraine War

Frontline Heroes

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