An Extension of Social Network Group Decision-Making Based on TrustRank and Personas

Abstract

With the development of social networking big data, social network group decision-making (SN-GDM) has been widely applied in many fields. This paper focuses on three main components: (1) the determination of the decision makers' (DMs) weights based on different social influence; (2) the anti-deception mechanism; and (3) the persona method. We introduce the TrustRank algorithm and the persona method into SN-GDM. Based on the TrustRank algorithm, both trusted and deceptive DMs in a seed set are artificially identified and given initial static scores to derive the influence of each DM. Additionally, the persona method is introduced to cluster DMs and achieve personalized decision-making. Further, we present a numerical example and comparison to demonstrate the efficiency of the framework in coping with non-socially shared preferences in SN-GDM. As expected, our findings indicate that our framework reduces the influence of deceptive DMs on the decision results.

From flat file to interface: Synthesis of personas and analytics for enhanced user understanding

“…Ease of communication is one of the hallmarks of personas. Operational managers (Cai, Wang, & Gong, 2020) can, using the analytics aspect of the system, isolate the specific user segment a given persona represents. Finally, operational designers, information managers, or marketers can identify the individual users represented by the given persona and contained within the market segment.…”

Exploring Trust Dynamics in Online Social Networks: A Social Network Analysis Perspective

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