The web impact scattering problem
暂无分享,去创建一个
In this chapter we address how to identify the online presence of companies as the basic strategy for conducting a cybermetric analysis. Presence is determined by a web domain node, which is the core around which content is generated. From this web presence the web size is calculated: The number of hosted files or indexed URLs in the domain node. Then we look at the scattering of presence and size, both internally (or vertically) within the web domain nodes (through subdirectories or web subdomains) and externally (or horizontally) from alternative or satellite web domains. Finally, we explore two case studies focusing on internal and external scattering, respectively. The first is an analysis of 10 global companies that are leaders in their respective sectors. For each company, internal scattering is calculated from the first-level subdomains and subdirectories. The results show a wide variability in both the chosen approach (subdirectory or subdomain) and in the distribution of content, no specific patterns having been found. The second case study is a comprehensive analysis of the external scattering of Microsoft, both through its alternative web domains and its web satellites (in this case limited to Twitter). With regard to alternative web domains, the results show not only high external scattering but also considerable interaction between the various alternative domains, an aspect that may adversely affect the measurement of impact. As for the web satellites, there is clearly a need to rethink the concept of web presence in social networks, to go from simply counting files to considering blocks of information.
[1] Enrique Orduña-Malea,et al. Disclosing the network structure of private companies on the web: The case of Spanish IBEX 35 share index , 2015, Online Inf. Rev..
[2] Mike Thelwall,et al. Web crawling ethics revisited: Cost, privacy, and denial of service , 2006 .
[3] José-Antonio Ontalba-Ruipérez,et al. Redes de conectividad entre empresas tecnológicas a través de un análisis métrico longitudinal de menciones de usuario en Twitter , 2016 .