A Content and Sentiment Analysis of Greek Tweets during the Pandemic

Kydros, Dimitrios; Argyropoulou, Maria; Vrana, Vasiliki

doi:10.3390/su13116150

Cited by 37 publications

(31 citation statements)

References 81 publications

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“…Much of the crisis-related public-centered research on social media has focused on consumer emotions [35]. For instance, Lachlan et al [5] examined Twitter content after a crisis, revealing that most tweets contained affective orientation rather than actionable information.…”

Section: Literature Review 21 Public Reactions To Organizational Crisismentioning

confidence: 99%

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

2021

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Focusing on public-centered, social-mediated crisis communication, the current exploratory study drew on situational crisis communication theory to formulate a comprehensive view of consumer reactions to crisis. Data mining and automated content analysis techniques were utilized to analyze social media posts by the public during a crisis in the cereals industry. Two path analyses showed that: (a) crisis-related social media posts tended to skip over competitor brand products, followed by two major reaction paths—(1) a rational path based on guilt attribution that justifies implications for the company and (2) an emotional path associated with public distrust; and (b) public self-blame spilled over to other stakeholders such as the government and economic system. The results give voice to issues that concern the public during crises, both as individuals and as a community. They highlight the fact that sustainable crisis management should involve additional stakeholders. Conclusions and implications for society and practice are suggested.

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Section: Literature Review 21 Public Reactions To Organizational Crisismentioning

confidence: 99%

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

2021

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“…Focusing on the similar tasks, in this research we describe the implementation of an NLP based framework for the analysis of tweets written in the Croatian language, integrating the sentiment analysis, the topis analysis and the analysis of message spreading (retweeting). There are fewer studies focused on other languages, such as sentiment analysis of Polish [13] and Greek [14] tweets or topic modelling for tweets in the Italian language [15]. Studies limited to one language are important as a potential source of material for further comparative research of COVID-19-related communication in different languages.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

et al. 2021

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This study aims to provide insights into the COVID-19-related communication on Twitter in the Republic of Croatia. For that purpose, we developed an NL-based framework that enables automatic analysis of a large dataset of tweets in the Croatian language. We collected and analysed 206,196 tweets related to COVID-19 and constructed a dataset of 10,000 tweets which we manually annotated with a sentiment label. We trained the Cro-CoV-cseBERT language model for the representation and clustering of tweets. Additionally, we compared the performance of four machine learning algorithms on the task of sentiment classification. After identifying the best performing setup of NLP methods, we applied the proposed framework in the task of characterisation of COVID-19 tweets in Croatia. More precisely, we performed sentiment analysis and tracked the sentiment over time. Furthermore, we detected how tweets are grouped into clusters with similar themes across three pandemic waves. Additionally, we characterised the tweets by analysing the distribution of sentiment polarity (in each thematic cluster and over time) and the number of retweets (in each thematic cluster and sentiment class). These results could be useful for additional research and interpretation in the domains of sociology, psychology or other sciences, as well as for the authorities, who could use them to address crisis communication problems.

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“…The foodservice industry is among the areas hardest hit by the pandemic, and COVID-19 poses both threats and opportunities to the sustainability of the foodservice industry [9]. As activities were limited by the COVID-19 pandemic, people increasingly turn to social media to keep in touch with their family and friends, which added a new dimension to the effects of the COVID-19 pandemic in terms of sharing new information and communicating by an alternative method [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Study on Dining-Out Trends Using Big Data: Focusing on Changes since COVID-19

2021

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This study examined consumers’ emotions and needs related to dining-out experiences before and during the COVID-19 crisis. This study identifies words closely associated with the keyword “dining-out” based on big data gleaned from social media and investigates consumers’ perceptions of dining-out and related issues before and after COVID-19. The research findings can be summarized as follows: In 2019, frequently appearing dining-related words were dining-out, family, famous restaurant, recommend, and dinner. In 2020, they were dining-out, family, famous restaurant, and Corona. The analysis results for the dining-out sentimental network based on 2019 data revealed discourses revolving around delicious, nice, and easily. For the 2020 data, discourses revolved around struggling, and, cautious. The analysis of consumers’ dining-out demand network for 2019 data showed discourses centered around reservation, famous restaurant, meal, order, and coffee. However, for 2020 data, discourses were formed around delivery, price, order, take-out, and social distance. In short, with the outbreak of the pandemic, delivery, takeout, and social distance emerged as new search words. In addition, compared with before the COVID-19 pandemic, a weakening trend in positive emotions and an increasing trend in negative emotions were detected after the outbreak of the COVID-19 pandemic; specifically, fear was found to be the fear emotion.

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A Content and Sentiment Analysis of Greek Tweets during the Pandemic

Cited by 37 publications

References 81 publications

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining

Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model

A Study on Dining-Out Trends Using Big Data: Focusing on Changes since COVID-19

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