Twitter is one of the most popular social networking platforms that people use to communicate and interact. Organisations and companies use Twitter, as well as other social media platforms, for the marketing of their products or services. To achieve this goal they seek to partner with influential Twitter users, as a part of their influencer marketing strategy. Influencer marketing is considered more effective than traditional marketing. Influencers are more trustworthy than a business due to the fact that they have developed close connection with their followers. This marketing trend has played an important role in the rise of fake influencers in Twitter. Fake influencers inflate their follower counts by buying fake Twitter accounts from vendors and they manage to partner with companies. However, that partnership does not benefit companies as the influencer's engagement is fake. In this paper we analyse centrality and overall network characterization measures applied on Twitter fake influencer accounts and on legitimate influencer accounts. The results showed that the measures we propose are statistically significant and can be easily applied to automatically detect fake influencers on Twitter.
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