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Investors are constantly aware of the behaviour of stock markets. This affects their emotions and motivates them to buy or sell shares. Financial sentiment analysis allows us to understand the effect of social media reactions and emotions on the stock market and vice versa. In this research, we analyse Twitter data and important worldwide financial indices to answer the following question: How does the polarity generated by Twitter posts influence the behaviour of financial indices during pandemics? This study is based on the financial sentiment analysis of influential Twitter accounts and its relationship with the behaviour of important financial indices. To carry out this analysis, we used fundamental and technical financial analysis combined with a lexicon-based approach on financial Twitter accounts. We calculated the correlations between the polarities of financial market indicators and posts on Twitter by applying a date shift on tweets. In addition, correlations were identified days before and after the existing posts on financial Twitter accounts. Our findings show that the markets reacted 0 to 10 days after the information was shared and disseminated on Twitter during the COVID-19 pandemic and 0 to 15 days after the information was shared and disseminated on Twitter during the H1N1 pandemic. We identified an inverse relationship: Twitter accounts presented reactions to financial market behaviour within a period of 0 to 11 days during the H1N1 pandemic and 0 to 6 days during the COVID-19 pandemic. We also found that our method is better at detecting highly shifted correlations by using SenticNet compared with other lexicons. With SenticNet, it is possible to detect correlations even on the same day as the Twitter posts. The most influential Twitter accounts during the period of the pandemic were The New York Times, Bloomberg, CNN News and Investing.com, presenting a very high correlation between sentiments on Twitter and stock market behaviour. The combination of a lexicon-based approach is enhanced by a shifted correlation analysis, as latent or hidden correlations can be found in data.

Purpose The purpose of this paper is to identify the factors that generate public value in e-government services through emerging technologies and to answer the following question: Which are the factors that generate public value, in the e-government services, through emerging technologies? Design/methodology/approach Based on a multivariate linear regression model, the author tests the public value of e-government services through emerging technologies in the metropolitan area of the Toluca Valley. Five factors are evaluated to understand public value: anti-corruption strategies, access to public information, transparency platforms, social media and service kiosks. Findings Smart strategies and technologies must be guided by the generation of public value through anti-corruption strategies, open data, access to information and data privacy. The efforts of governments should focus on avoiding corruption, making government transparent, opening data and correct handling of information privacy. Technology is an important mechanism to boost public value generation. Research limitations/implications Mexico is a developing country, and there are very few emerging technologies implemented in e-Government. Practical implications The results are important to identify good practices for the generation of public value in the e-Government area. Originality/value The study of emerging technologies is a new area in government, and this paper studies the generation of public value through emerging technologies in a developing country.

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The implementation of information technologies in government could have significant effects on efficiency, transparency, and corruption. However, it is not clear whether and how citizens perceive these effects. Based on a survey conducted in 2015, this study examines the role of technology use and its effects on transparency, efficiency, and corruption in Mexican local governments from the perspective of citizens. Specifically, the paper seeks to respond to the following question: What technology-related factors affect citizens' perceptions of transparency, efficiency, and corruption? The results of multivariate regression analyses indicate that interactions between citizens and municipal governments, supported by technologies, do affect citizens' perception of transparency, efficiency, and corruption. The most impactful technologies identified were websites, social media, and mobile technologies. The only demographic factor that had a significant effect on citizens' perception was employment status.