During the time of the coronavirus, strict prevention policies, social distancing, and limited contact with others were enforced in Greece. As a result, Twitter and other social media became an important place of interaction, and conversation became online. The aim of this study is to examine Twitter discussions around COVID-19 in Greece. Twitter was chosen because of the critical role it played during the global health crisis. Tweets were recorded over four time periods. NodeXL Pro was used to identify word pairs, create semantic networks, and analyze them. A lexicon-based sentiment analysis was also performed. The main topics of conversation were extracted. “New cases” are heavily discussed throughout, showing fear of transmission of the virus in the community. Mood analysis showed fluctuations in mood over time. Positive emotions weakened and negative emotions increased. Fear is the dominant sentiment. Timely knowledge of people’s sentiment can be valuable for government agencies to develop efficient strategies to better manage the situation and use efficient communication guidelines in Twitter to disseminate accurate, reliable information and control panic.
In this paper we provide some insights in Homer's Iliad from the perspective of social network analysis. We use the original text and other public available data to create a social network (i.e. a graph) that comprises of all actors in the Iliad together with their interactions. We present some visualizations of these data and discuss concepts like connectivity, connected components and groupings. Furthermore, we calculate some well-established metrics, coming from social network analysis in this social network and discuss the numerical results. These results indicate that the Iliadic network is a smallworld network, rather dissasortative and relatively easy to disconnect.
The 2022–2023 winter period is alleged to be one of the toughest since World War II with respect to energy, especially electricity, natural gas and oil. The paper investigates the public discussions on Twitter in five widely spoken European languages and English. Networks of users are formed in order to locate possible important nodes that control the distribution of information. The networks are rather sparse and do not belong to the general class of ‘small worlds’. The communities of users seem to gather around one user; however, users also interact with others within the groups. Regarding the users’ sentiments, the negatives are definitely higher than the positive ones. Sentiments appear to be stable in their scores during the examined period and for each language; fear and sadness are dominant among them. Energy prices are frequently discussed in all languages, along with major political events. Findings may help governments to better understand public views and develop an effective strategy to communicate with and protect EU citizens.
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