Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/12972
Title: Twitter influencers or cheated buyers?
Authors: Zenonos, Savvas 
Tsirtsis, Andreas 
Tsapatsoulis, Nicolas 
Keywords: Centrality measures;Centralization;Influencer marketing;Network characterization measures;Reciprocity;Twitter fake influencers
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: Aug-2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, 2018, Athens, Greece, 12-15 August
metadata.dc.doi: https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00049
Abstract: 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.
URI: http://ktisis.cut.ac.cy/handle/10488/12972
Rights: © 2018 IEEE.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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