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.

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.

Increasing numbers of museums use Twitter to attract visitors, engage effectively with them and facilitate the construction and strengthening of communities, where visitors are creating their own authentic, trustworthy content. This paper creates and analyzes the network of Twitter users that talk about top European museums (based on TripAdvisor’s’ ranking) and produces some semantic analysis of the actual content of tweets. Findings show that there is not a real correlation between traffic in Twitter and the corresponding TripAdvisor’s’ ranking. There is an active discussion between users, sometimes involving different museums, while official accounts play an important role in information circulation. Word adjacencies over all connected tweets revealed general topics of discussions. A careful assessment of the discussions could boost public interest, promote the reputation of the museum and help the museum make improvements to deliver better visitor experiences that surpass their expectations.

Reliable financial reporting is critical for maintaining market confidence and guiding stakeholders’ decision-making, yet traditional audit methods often fail to detect sophisticated fraud schemes that are hidden within large volumes of transactional data. This systematic literature review synthesizes 43 empirical and theoretical studies published between 2010 and 2024 that utilize data analytics techniques for the prevention and detection of fraud in financial statements. Following the PRISMA guidelines, we conducted a four-phase review—identification, screening, eligibility assessment, and inclusion—to ensure transparency and reproducibility. Our analysis categorizes techniques into supervised machine learning classifiers (e.g., decision trees and neural networks), statistical anomaly detection methods, network-based analyses, and real-time monitoring frameworks. We evaluate each approach’s comparative effectiveness, highlight persistent challenges such as data imbalance, model interpretability, and governance constraints, and also trace evolving methodological trends over time. The review reveals that integrating predictive analytics and continuous monitoring into accounting information systems can transform audits from reactive investigations into proactive fraud prevention mechanisms. We conclude by proposing a future research agenda focusing on developing explainable AI models for audit applications, establishing robust data governance frameworks to support automated monitoring, and conducting longitudinal field studies to assess the real-world impact of analytics-driven controls.

In this paper, we use methods from social network analysis to investigate patterns in data regarding the spreading of rumours regarding serious economic situations. More specifically, we use data acquired from Twitter during a period of time regarding keyword