Quantification of Productivity of the Brands on Social Media With Respect to Their Responsiveness
| Autor: | Ume Ayesha, Basit Shahzad, M. Saqib Nawaz, M. Ikram Ullah Lali, Muzafar Khan, Kinza Mehr Awan, Abdullatif M. Abdullatif |
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| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
business.industry 05 social sciences General Engineering Network structure Advertising Popularity Online advertising Geolocation brands Information and Communications Technology followers 0502 economics and business retweet count General Materials Science Social media lcsh:Electrical engineering. Electronics. Nuclear engineering Business lcsh:TK1-9971 Productivity company response 050203 business & management |
| Zdroj: | IEEE Access, Vol 7, Pp 9531-9539 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2891081 |
| Popis: | Social online marketing is expanding fast with the evolution and recent development in the information and communication technology. Investigating how companies are exploiting social media for marketing, advertisement, and consumer's engagement is gaining more and more interest. In this paper, brands/companies data on Twitter is collected and analyzed to compute the overall company response on Twitter. Responsiveness of a company is inferred from three features: company popularity, average company replies, and average followers' replies. Twitter network features are used in calculating the posting frequency for companies and their followers. It is shown that the proposed approach can be used in finding the responsiveness of companies and their followers. Furthermore, useful links for a brand consumer is extracted and the posting behavior of brands and their followers is determined with the help of Twitter network features, such as retweet count and geolocation. This paper contributes to the literature on how Twitter data and its network structure features can be exploited in finding the responsiveness and posting behavior of companies and their followers. We believe that this approach can be used effectively in developing prediction and information-filtering systems, particularly the personalized-recommendation systems. |
| Databáze: | OpenAIRE |
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