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This study aims to investigate public sentiment toward economic stimulus using textual analysis. Specifically, it analyzes Twitter’s public opinion, emotion-based sentiment and topics related to COVID-19 economic stimulus packages.

This study applies natural language processing techniques, such as sentiment analysis, t-distributed stochastic neighbor embedding and semantic network analysis, to a global data set of 88,441 tweets from January 2020 to December 2021 extracted from the Twitter platform, discussing COVID-19 economic stimulus packages.

Results show that in the fourth quarter of 2021, there is a declining trend of positive sentiment (−5%) and an increasing trend of negative sentiment (14%), which may indicate the perceived inadequacy of COVID-19 stimulus measures. Topic modeling identifies seven topics, highlighting the necessity of stimulus in the education sector.

The big-data findings of this study provide a better understanding of public sentiment about economic stimulus for regulators and policymakers, which can help in formulating more effective fiscal and monetary policies.

Public sentiment is a significant concern for regulators because of its associated ambiguity, such as how to design stimulus packages and evaluate the effectiveness of previous measures. This study applies natural language processing, contributing to the growing literature on designing effective economic stimulus.

This paper aims to the intricate relationship between digital influencers (DIs) and their followers, aiming to develop a comprehensive framework that explains how influence works in the digital world. It focuses on understanding the cultural aspects that shape these relationships in today’s global and digital marketplace.

This study employs a holistic methodology, intertwining historical, cultural and theoretical insights to decode the DI phenomenon. Applying a Grounded Theory approach, this paper coded articles into categories, developed abstract concepts and refined them through cycles of literature collection and analysis that allowed identifing gaps in the Influencer Marketing field. This comprehensive review and inductive analysis of globalization, mediated communication and digital interactions aim to unravel the intricacies of digital and virtual influence. This paper’s theoretical development advances propositions that dissect the facets influencing digital adoption, usage, interest and value perception, leading to a detailed model of digital influence grounded in both theory and real-world examples.

This research uncovers the significant impact DIs have, driven by global connections and the way we communicate in the digital age. Historical context situates DIs within the broader narrative of mediated persuasive communication. A preliminary typology of DIs and influence contexts forms the foundation for further exploration.

This study enhances the discussion around DIs by considering the influence of technology and culture together. It draws from the thoughts of leading thinkers on how technology connects us, providing a strong foundation for future studies.

As digital influence and the surrounding technology continue to change, it’s important to think critically about these trends. This research offers valuable insights for businesses looking to navigate the digital landscape effectively, helping them make better strategic decisions about their online presence.

This study breaks new ground by offering a detailed categorization of DIs and proposing a fresh way to understand their role. It links important ideas from the past about persuasion through media to the current state of digital influence, offering insights into how digital trends might affect communication strategies.