The purpose of this study is to investigate influencers on Twitter to discover the characteristics of their tweets through PIAR, a unique data mining research tool developed by the University of Salamanca that combines graph theory and social influence theory. An analysis of 3853 users posting about two automotive Japanese car firms, Toyota and Nissan, reveals the characteristics influencers have on this social network. The findings suggest that influencers use more hashtags and mentions on average when they tweet, and their word count is fewer than those with less power on this virtual community. Surprisingly, they tend to include less embedded links on their posts. Additionally, influencers have on average a large number of people they follow and they clearly express their opinions and feelings (either positive or negative) when tweeting. The results broaden the understanding of how influencers write and behave on social networks when they communicate with their users' community. Further, it provides insights for practitioners and marketers on how to discover influencers talking about their brands by observing tweets' content.
Purpose
Due to the size and importance of social media, user-generated content analysis is becoming a key factor for companies and brands across the world. By using Twitter messages’ content, the purpose of this paper is to identify which elements of the messages enable tweet diffusion and facilitate eWOM.
Design/methodology/approach
In total, 30,082 tweets collected from 10,120 Twitter users were classified based on four assorted brands. By comparing with multiple regression techniques high vs low purchase involvement and hedonic vs utilitarian products and using the theory of heuristic-systematic processing of information, the authors examine the causes of tweet diffusion.
Findings
The authors illustrate how the elements of a tweet (hashtags, mentions, links, sentiment or tweet length) influence its diffusion and popularity.
Research limitations/implications
This study validated the use of information processing theories in the social media field. The study showed a picture on how different Twitter elements influence eWOM and message diffusion under several purchase involvement situations.
Practical implications
The results of this study can help social media brand community managers of all types of companies on how to write their Twitter messages to obtain greater dissemination and popularity.
Originality/value
The study offers a unique deep brand analysis which helps brands and companies to understand their social media popularity in detail. Depending on product category, companies can achieve maximum social impact on Twitter by focusing on the interactivity items that will work best for their products or brands.
Purpose
The purpose of this study is to examine the different results and the level of success obtained with advertising campaigns developed on Facebook to promote postgraduate programs to create awareness and engagement.
Design/methodology/approach
This study combined the data envelopment analysis technique to measure advertising efficiency with multidimensional scaling (MDS) representation, thus offering alternatives for practitioners and organizations on how to evaluate social advertising performance.
Findings
Investments on social paid advertising are an affordable and effective way both to promote postgraduate programs and create engagement with prospective students. Facebook advertisements maximize visibility, which improves social and online positioning and encourages student recruitment.
Practical implications
Higher education institutions can efficiently promote their programs with a minimal social investment contributing to dissemination and engagement. Compared to other forms of traditional or digital advertising, social media ads can be efficient and affordable with wider segmentation and targeting options. Moreover, results are immediate and measurable and campaigns can be instantly modified to better suit the audience’s requirements.
Originality/value
This study is unique as it offers a new, alternative way of measuring efficiency, in addition to the classic ratios of payment models in digital advertising that combine clicks and impressions, on a sector where there are few empirical studies. Moreover, it can be easily applied to many other sectors in public and private organizations.
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