The coronavirus disease 2019 (COVID-19) pandemic was an unexpected event and resulted in catastrophic consequences with long-lasting behavioral effects. People began to seek explanations for different aspects of COVID-19 and resorted to conspiracy narratives. The objective of this article is to analyze the changes on the discussion of different COVID-19 conspiracy theories throughout the pandemic on Twitter. We have collected a data set of 1.269 million tweets associated with the discussion on conspiracy theories between January 2020 and November 2021. The data set includes tweets related to eight conspiracy theories: the 5G, Big Pharma, Bill Gates, biological weapon, exaggeration, FilmYourHospital, genetically modified organism (GMO), and the vaccines conspiracy. The analysis highlights several behaviors in the discussion of conspiracy theories and allows categorizing them into four groups. The first group are conspiracy theories that peaked at the beginning of the pandemic and sharply declined afterwards, including the 5G and FilmYourHospital conspiracies. The second group associated with the Big Pharma and vaccination-related conspiracy whose role increased as the pandemic progressed. The third are conspiracies that remained persistent throughout the pandemic such as exaggeration and Bill Gates conspiracies. The fourth are those that had multiple peaks at different times of the pandemic including the GMO and biological weapon conspiracies. In addition, the number of COVID-19 new cases was found to be a significant predictor for the next week tweet frequency for most of the conspiracies.

This paper focuses on analyzing discussions related to Genetically Modified Organisms (GMOs) on Twitter, with a specific focus on the spread of misinformation and conspiracy theories. The authors collected and analyzed 1,048,274 English tweets related to GMOs between January 2020 and December 2022 using the Twitter API. The tweets were subjected to topical and sentiment analysis to identify the prevalent themes and attitudes toward GMOs. 30.92% of the tweets in the observed period were negative, 21.65% were neutral, and 47.43% were positive. The authors identified four clusters of tweets associated with misinformation or conspiracy theories: GMOs and vaccines, GMOs and COVID-19, GMOs and Monsanto, and GMOs and Bill Gates. The findings of this analysis can inform strategies for combating the spread of false information and conspiracies on social media and improve public understanding and trust in GMO technology.

This paper investigates tax‐related determinants of indirect foreign direct investment (FDI). In particular, it studies the effects of bilateral effective average tax rates, the strength of anti‐tax avoidance rules in host countries and tax haven status of home countries on the volume of indirect FDI host countries receive. The paper uses the fourth edition of the OECD Benchmark Definition of Foreign Direct Investment (BMD4) database, which distinguishes between ultimate and immediate FDI. Methodologically, the paper relies on the standard gravity equation for FDI and applies the Poisson pseudo‐maximum likelihood estimation model. The paper shows that ultimate FDI is not influenced by tax‐related factors but only real economic determinants, whereas tax rates affect immediate FDI. This finding suggests that previous research may have overestimated the tax elasticity of FDI, and taxes do not have an impact on location decisions of FDI, but rather the route of investing—direct or indirect. The paper defines indirect FDI as the difference between ultimate and immediate FDI and finds that high bilateral effective average tax rates encourage indirect FDI. The finding is robust under different specifications.

No abstract

No abstract