How do sentiments affect virality on Twitter?

Jiménez-Zafra, Salud María; Sáez-Castillo, A. J.; Sánchez, Antonio Conde; Martín–Valdivia, María Teresa

doi:10.1098/rsos.201756

Cited by 15 publications

(10 citation statements)

References 49 publications

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“…It is clear that much of the information users wished to disseminate was negative in nature. As for the difference between groups in this study, there is a research report that focuses on virality, one of the characteristics of sentiment analysis using social media [ 29 ] Virality is an explosive spread of attention and information through social media and word-of-mouth on the internet. Virality is derived from “viral”—as in a virus.…”

Section: Discussionmentioning

confidence: 99%

Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users

Ueda,

Han,

Zhang

et al. 2024

JMIR Infodemiology

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Background The COVID-19 pandemic prompted global behavioral restrictions, impacting public mental health. Sentiment analysis, a tool for assessing individual and public emotions from text data, gained importance amid the pandemic. This study focuses on Japan’s early public health interventions during COVID-19, utilizing sentiment analysis in infodemiology to gauge public sentiment on social media regarding these interventions. Objective This study aims to investigate shifts in public emotions and sentiments before and after the first state of emergency was declared in Japan. By analyzing both user-generated tweets and retweets, we aim to discern patterns in emotional responses during this critical period. Methods We conducted a day-by-day analysis of Twitter (now known as X) data using 4,894,009 tweets containing the keywords “corona,” “COVID-19,” and “new pneumonia” from March 23 to April 21, 2020, approximately 2 weeks before and after the first declaration of a state of emergency in Japan. We also processed tweet data into vectors for each word, employing the Fuzzy-C-Means (FCM) method, a type of cluster analysis, for the words in the sentiment dictionary. We set up 7 sentiment clusters (negative: anger, sadness, surprise, disgust; neutral: anxiety; positive: trust and joy) and conducted sentiment analysis of the tweet groups and retweet groups. Results The analysis revealed a mix of positive and negative sentiments, with “joy” significantly increasing in the retweet group after the state of emergency declaration. Negative emotions, such as “worry” and “disgust,” were prevalent in both tweet and retweet groups. Furthermore, the retweet group had a tendency to share more negative content compared to the tweet group. Conclusions This study conducted sentiment analysis of Japanese tweets and retweets to explore public sentiments during the early stages of COVID-19 in Japan, spanning 2 weeks before and after the first state of emergency declaration. The analysis revealed a mix of positive (joy) and negative (anxiety, disgust) emotions. Notably, joy increased in the retweet group after the emergency declaration, but this group also tended to share more negative content than the tweet group. This study suggests that the state of emergency heightened positive sentiments due to expectations for infection prevention measures, yet negative information also gained traction. The findings propose the potential for further exploration through network analysis.

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Section: Discussionmentioning

confidence: 99%

Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users

Ueda,

Han,

Zhang

et al. 2024

JMIR Infodemiology

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“…Hashtags reflect on trends. Therefore, depending on the virality of a trend, sentiments for the high and low number of hashtags can be the same (Jiménez-Zafra et al, 2021).…”

Section: Tweet Sentiment and Number Of Retweets And Hashtagsmentioning

confidence: 99%

The Drivers of Polarity in Sentiments on Social Media: an Exploratory Study on the 2021 Canadian Federal Election

Noor

2024

Preprint

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<p>The drivers of polarity in sentiments on social media - an exploratory study on the 2021 Canadian Federal Election Hiba Mohammad Noor Master of Science in Management, 2022 Master of Science in Management, Ryerson University Social media is used by the public, voters, and politicians to share their political opinions leading to online political discourse. The opinions shared by voters on social media have different sentiments associated with them depending on voter needs and priorities. Understanding the factors that drive these sentiments can help policymakers and other political stakeholders to understand voter needs and expectations and develop policies that align with those needs. This research focuses on identifying the factors (keywords) that drive these sentiments. This research also investigates the relationship between these keywords and the number of retweets and hashtags. Sentiment analysis was performed on 779,169 tweets related to the 2021 Canadian Federal election followed by text clustering and keywords analysis. The topics and keywords that drive the sentiments were identified. Chi-Square test was used to investigate the relationship between these keywords, hashtags, and the number of retweets. The results suggest that some keywords are common in opposite sentiment types (positive and negative) which shows polarization in Twitter and some keywords are unique to a sentiment type which shows that these keywords drive that specific sentiment. The results also suggest that there is no significant relationship between the keywords and the number of hashtags but has a significant relationship between the keywords and the number of retweets for extremely negative tweets only. </p>

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“…Sentiment analysis, alternatively termed opinion mining, is a computational method utilized to ascertain the sentiment—be it positive, negative, or neutral—expressed within a given text (Jimenez-Zafra et al, 2021; Khan et al, 2022; Kim et al, 2021). This powerful tool allows understanding individuals’ responses to events and emotional perspectives, proving invaluable in assessing public sentiment and societal tendencies (Saranya & Usha, 2023; Zunic et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming

Wang

2023

Global Media and China

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This article aims to gain insights into the prevailing public sentiment during the policy relaxation period by examining whether the post-COVID-19 landscape reflects signs of withering or booming conditions. Employing methods from natural language processing (NLP) and machine learning (ML), the analysis reveals a predominance of positive sentiment from December 7, 2022 to May 17, 2023, indicative of an optimistic perspective and a potentially flourishing environment. A predictive model based on logistic regression emerges as a notably effective tool for sentiment prediction, suggesting potential utility in predicting future public health crises. A comparison of sentiments in translations by the government aligns with previous research, revealing a less favorable depiction of translated texts compared to the source texts. Furthermore, the commonality index, a measure of group consensus value, surpasses the typical range, while the certainty index, a measure of confidence, slightly falls below the norm. These findings offer valuable insights for policy considerations while highlighting areas for international communication and understanding improvement.

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How do sentiments affect virality on Twitter?

Cited by 15 publications

References 49 publications

Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users

Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users

The Drivers of Polarity in Sentiments on Social Media: an Exploratory Study on the 2021 Canadian Federal Election

Sentiment analysis of tweets and government translations: Assessing China’s post-COVID-19 landscape for signs of withering or booming

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