This sub-section describes some of the tools and techniques used to identify online influencers. Brief description of features in existing social media analytic tools and their limitations are provided. Besides, the concept of social network analysis as a different approach to identify influencers are explained. Finally, some frameworks developed by other researchers and their findings are evaluated.
2.5.1 Existing analytic tools & limitations
Although Twitter do not have their own analytic tool, they have provided us with two application program interfaces(APIs) to allow us to access its data. By using the APIs, many developers have created applications to download and analyse Twitter data. Three of the most popular tools and their most attractive features are shown below:
FollowerWonk — This tool allows the user to search for keywords about various topics and users in Twitter. Besides, it is able to sort the users according to their reach. It also has a feature to compare the followers of up to three Twitter accounts (Maguire 2015).
Topsy — This tool is good for collecting data about shared contents on Twitter. Users can find out what content is shared by which user. Users can also filter out the most influential users from a list of people who shared a link. Besides, it provides a language filter for tweets so that users can download only tweets that are published in a specific language (Finn 2012).
Klout — This is one of the most popular analytic tool because it has the ability to rank users based on their social influence using their own scoring system. Users with the highest possible influence will get a score of 100 and 1 for lowest. Although the complex algorithm used to score the users ...
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...ore the fact that Twitter users are able to interact with the content on the platform. Therefore, they proposed a way that better explains how influence occurs in the Twitter network, which is by calculating the ratio of followers to followees.
In addition, Bodendorf et al. (2009) suggested a combined approach to identify influencers and determine their relationship with other online users. They started by extracting user’s opinions on a product using text mining. Then, text based relationship mining methods were used to determine the relationship between the user and other users. Finally, using SNA, a social network graph was drawn which includes the user and their opinions as the nodes of the graph and the edges represent their communication relationship. The resulting graph was analysed and degree centrality was used to determine influencers in the network.
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