Understanding Global Reaction to the Recent Outbreaks of COVID-19: Insights from Instagram Data Analysis

Rafi, Abdul Muntakim; Rana, Shivang; Kaur, Rajwinder; Wu, Qi; Zadeh, Pooya Moradian

doi:10.1109/smc42975.2020.9283376

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…In [101], an experiment on Instagram multimedia data was carried out with the purpose of modeling the emotional impact caused by the COVID-19 pandemic. Two neural network models (attention-based LSTM and ResNet50 CNN with transfer learning) were trained on caption data together with additional text extracted from images using an optical character recognition Python library (Pytesseract), which incorporates information from subtitles and image samples extracted from video data.…”

Section: Techniquesmentioning

confidence: 99%

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

et al. 2022

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Section: Techniquesmentioning

confidence: 99%

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

et al. 2022

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“…Rafi et al . [24] used ResNet-50 CNN and attention-based LSTM networks to analysis the sentiment of the images associated with COVID-19 from Instagram. Yin et al .…”

Section: Related Workmentioning

confidence: 99%

“…Although there have been many studies on the sentiment analysis of COVID-19 [21] , [22] , [23] , [24] , few researchers have focused on the vaccine. In addition, they emphasized the emotional changes of online social media messages without considering their comparison.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 vaccine sensing: Sentiment analysis and subject distillation from twitter data

Han¹,

Liu²,

Luo³

et al. 2022

Telematics and Informatics Reports

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“…The emotions expressed on social media may affect the emotions of audience (Ferrara and Yang, 2015). Through emotional analysis of images on Instagram during COVID-19, Rafi et al (2020) found that users tend to share more positive posts. Some researchers consider valence on social media platforms as an important factor influencing user engagement (L opez Barbosa et al, 2015;Ji et al, 2019;So et al, 2016).…”

Section: Valence and Content Characteristics Of Postsmentioning

confidence: 99%

Understanding citizen engagement and concerns about the COVID-19 pandemic in China: a thematic analysis of government social media

Luo

Duan

Shang

et al. 2021

AJIM

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PurposeIn crises such as the coronavirus disease 2019 (COVID-19) pandemic, governments need to act in time to lead citizens toward rational reactions and disclose information effectively to the public. This paper aims to understand the content published by the government and identify how citizen engagement relates to content type and emotional valence.Design/methodology/approachThe grounded theory approach was adopted and nine types of content posted by the government were observed. The data were obtained from “People's Daily”, an official Sina Weibo account representing the voice of the Chinese government, from January 3 to June 22 in 2020.FindingsThe analysis shows that information related to emotional support and social mobilization were the most reposted, while those mentioning immoral and illegal incidents were the most liked and commented. Also, it was found that positive posts tend to attract more likes, yet with fewer reposts than neutral posts.Originality/valueThe authors adopted thematic analysis and focused on the impact of post content and valence on user participation behavior. This study expands the existing literature. The government can improve crises management capability by learning about citizen engagement behaviors on social media.

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Understanding Global Reaction to the Recent Outbreaks of COVID-19: Insights from Instagram Data Analysis

Cited by 11 publications

References 14 publications

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

COVID-19 vaccine sensing: Sentiment analysis and subject distillation from twitter data

Understanding citizen engagement and concerns about the COVID-19 pandemic in China: a thematic analysis of government social media

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