What is it about?

The paper aims to explore the influence of cybersecurity on the semantic orientation of the sports consumers. Focusing on both sport and esports, this study finds the social media factors contributing in the sentiment formation and commenting behavior on Twitter and proposes a scheme for attitude modulation through identification of highly engaged nano-influencers. Experimental design was used as the research methodology. Data mining from Twitter using RStudio software was conducted using the keyword “cybersecurity” during the time of pandemic. Final corpus of 31,891 tweets were considered for the study. Initial sentiment analysis has been conducted to explore the consumer’s emotional inclination towards cybersecurity. Further through generalized equation modeling the impact of social media attributes over the consumer’s posting behavior has been analyzed.

Featured Image

Why is it important?

The research findings reveal that users are inherently positive towards cybersecurity adoption in sports and the factors such as number of tweets, number of positive words contained in these tweets and the authenticity of the information source boost the pre-established tweeting behavior. However, the influx of information from non-organizational sources such as trending topics and discussions have negative impact over the users.

Perspectives

This study is first to explore the role of nano-influencers as communication moderators over digital social platforms. This study offers a new understanding of key contributing attributes of sentiments formation over social media and offers a scheme of selection of nano-influencers to modulate the pre-established sentiments of the users. Finally, the current study offers valuable insights into social media engagements and selection of nano-influencers for practicing marketing managers.

Dr. Jitendra Yadav
ICFAI Foundation for Higher Education

Read the Original

This page is a summary of: Exploring the synergy between nano-influencers and sports community: behavior mapping through machine learning, Information Technology and People, September 2021, Emerald, DOI: 10.1108/itp-03-2021-0219.
You can read the full text:

Read

Resources

Contributors

The following have contributed to this page