LAUSR

Twitter is one of the best online platforms for social interaction, introducing unique means of story-telling through tweets and enabling multiple approaches to the analysis of their content. This study was motivated by the increasing practice of incorporating Twitter into cultural studies and the research gap in Twitter-based cultural studies between emerging nations. This research aims to examine the emotional and sentimental cultural traits of Arabic and Hispanic viewers of a specific football match, as shown through their tweets, regardless of their distinct languages, to determine whether cultural diversity can be noticed in online interaction. Hundreds of tweets from both communities were translated into English as an intermediate language and then evaluated and contrasted using machine learning (ML) models. According to the research, Arabs are more collectivistic (as opposed to individualistic) and, as a result, exhibit less emotional arousal than Hispanics, which was partially supported by the collected Twitter data. This demonstrates how Twitter could play a key part in cultural research, and, therefore, this study contributes to cross-national comparative cultural research. We demonstrate that our method can also be used to evaluate the quality of machine translation based on how effectively it captures the emotions and sentiments of original languages.