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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 intro...
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... that there are seven DMs with the following relationship in a movie recommendation, we simulate the inlinks and outlinks with following-based relations among DMs, as shown in Figure 4. This study selects communicative tendency, independence, enthusiasm, and consumption as label variables affecting the DMs' movie consumption behavior (see Table 1). ...
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Recently, data-based services have played a significant role in satisfying various of requirements of people in social IoT. Since data is the basis of data-based service, therefore, it is significant to obtain trust data to enhance trust services. Few researches optimized trust issue of services from this point of view before. Therefore, based on t...
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... One notable approach, TrustRank, adapts the PageRank algorithm to specifically assess the trustworthiness of nodes in a social network. By distributing trust scores across connections, TrustRank provides a systematic way to evaluate the trust landscape within digital communities [28]. Building on this foundation, local trust propagation methods refine TrustRank's approach by focusing on the immediate trust dynamics between users. ...
This study explores trust dynamics within online social networks, blending social science theories with advanced machine-learning (ML) techniques. We examine trust’s multifaceted nature—definitions, types, and mechanisms for its establishment and maintenance—and analyze social network structures through graph theory. Employing a diverse array of ML models (e.g., KNN, SVM, Naive Bayes, Gradient Boosting, and Neural Networks), we predict connection strengths on Facebook, focusing on model performance metrics such as accuracy, precision, recall, and F1-score. Our methodology, executed in Python using the Anaconda distribution, unveils insights into trust formation and sustainability on social media, highlighting the potent application of ML in understanding these dynamics. Challenges, including the complexity of modeling social behaviors and ethical data use concerns, are discussed, emphasizing the need for continued innovation. Our findings contribute to the discourse on trust in social networks and suggest future research directions, including the application of our methodologies to other platforms and the study of online trust over time. This work not only advances the academic understanding of digital social interactions but also offers practical implications for developers, policymakers, and online communities.
... studied the trust relationships of social network and employed it to obtain the weights of DMs.Cai et al. (2020) utilized TrustRank algorithm to evaluate the influence of DMs in the social network framework, and the weights of groups can be derived by normalized the TrustRank values.Yuan et al. (2022a) measured the conflict degree of DMs and construct a conflict network, and then the weights of DMs are generated by analyzing the conflict network. Noticing the conflict opinions and behaviors of DMs,Ding et al. (2019) defined the weights of DMs by combining the conflict performance of DMs in social network.Liu et al. (2019b) established a conflict detection and elimination process to identify the conflict relationships of DMs, and then the importance of each DM can be determined based on the conflict degree. ...
Complex and uncertain decision-making environment increasingly requires the collaborations of participants, the exchange and interaction of knowledge of different actors can effectively promote and improve the decision-making process. Collaborative decision-making (CDM), a prevailing decision-making method, is extensively studied by scholars, in which different actors can communicate mutually and then reach a common goal. Hence, in view of the advantages and usefulness of CDM, an overview is conducted for showing the state of the art of CDM, pointing out the current issues and indicating the future research directions. To achieve those goals, firstly, the bibliometric analysis is implemented to briefly review the previous research topics. Then, the definitions, comparisons, weights and consensus concerning CDM are recalled, which are followed by the summarization of methodologies used for solving CDM problems. Subsequently, the current research directions and popular application areas are highlighted. Finally, the challenges and potential research directions are concluded from five perspectives.
... Naz et al. [26] explained decision analysis under the hesitant dual fuzzy environment q-rung orthopair. An extension of social network group decision-making based on trust rank and personas was proposed by Cai et al. [10]. Pal et al. [25] investigated new concepts in neutrosophic graphs. ...
Picture fuzzy graph, belonging to fuzzy graphs family, has good capabilities at times when we are faced with problems that cannot be expressed by fuzzy graphs and intuitionistic fuzzy graphs. When an element membership is not clear, neutrality is a good option that can be well-supported by a picture fuzzy graph. The previous definitions limitations in fuzzy graph energy have led us to offer new definitions in picture fuzzy graphs. In this article, we expanded the energy concept on the picture fuzzy graph and sought to use this concept in modeling issues related to this graph and solving some problems including the neutrality state. We were able to show that neutrality, as part of total energy, is effective in energy-based decisions. This is noticeable in some types of energy and is more pronounced. We were looking for a way to rank the available options using the picture fuzzy graph and its Laplacian energy/energy in decision making. We studied some types of energy including Laplacian and skew Laplacian in both picture fuzzy graphs and picture fuzzy digraphs, and discussed some of its properties. We discussed some energy boundaries in this graph, and finally, the applications of energy were presented.
... In the GDM problem, each decision-maker has his own opinions because of different knowledge backgrounds or preferences. Therefore, the consensus reaching process (CRP) is significant for GDM problems [37,38]. In many studies on CRP in GDM, the group interaction consensus model has been proved to be an effective approach to reduce or eliminate inconsistency [39,40]. ...
This paper proposes a framework for hotel selection based on online reviews by sentiment analysis from the perspective of consensus group decision making. To identify multi-granularity sentiment strength in text reviews, a sentiment analysis method based on the Word2Vec algorithm and one-vs-one strategy based Support Vector Machine (OVO-SVM) algorithm is provided. Then, richer information content can be derived from online text reviews, which are used as the data source of this study. To help members make an aggregation on the preference of hotel attributes, a consensus model with an improved feedback mechanism is proposed, which can reasonably control the adjustment cost in the consensus reaching process. Combining the hotel performance obtained from online reviews and the group preference consensus, the optimal hotel for members can be selected. At the end of this paper, a case study is presented to illustrate the use of the proposed method.
... Although large amounts of effort have been spent on SNA [54][55][56], there is no previous research regarding GDM problems with probabilistic information focusing on determining experts' weights from SNA. In this section, we determine experts' weights by utilizing experts' trust relationship information. ...
Unlike other linguistic modellings, probabilistic linguistic terms can clearly describe the importance of different linguistic terms. With respect to group decision-making (GDM) problems, it is convenient for experts to express their evaluation opinions with probabilistic linguistic preference relations (PLPRs), which can transform experts’ quantitative descriptions into qualitative probabilistic linguistic terms. The processes of consistency-adjustment and expert weights determination play a key role in GDM. Therefore, this paper aims at the design of a novel probabilistic linguistic GDM method with consistency-adjustment algorithm and trust relationship-driven expert weight determination model. First, we redefine the multiplicative consistency of PLPRs, which only involves changing the probabilities of linguistic terms. A new distance between PLPRs is presented to calculate the consistency index. Then, we propose a convergent consistency-adjustment algorithm to improve the consistency of a PLPR to an acceptable consistency level. Subsequently, a trust relationship-driven expert weight determination model is developed to derive the experts’ weights in a social network environment. Finally, a probabilistic linguistic GDM method is designed to determine the reliable ranking of alternatives. The advantages and applicability of the proposed method are illustrated by a case study concerning an evaluation of logistics service suppliers and associated comparative analyses.
... 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. ...
The persona approach is a communicative design technique for understanding users. With increasing access to analytics data, personas are more and more generated from online user statistics using big data and algorithmic approaches. This integration offers new opportunities to shift personas from flat files of data presentation to that of interactive interfaces for analytics systems. We illustrate this transition towards the concept of “persona as interface” with a persona analytics system, Automatic Persona Generation (APG). In pushing advancements of both persona and analytics conceptualization, development, and use, the APG system presents a multi‐layered “full‐stack” integration. APG affords three levels of user data presentation, which are (a) the conceptual persona, (b) the analytical user metrics, and (c) the foundational user data. Moving to a “personas as interfaces” approach offers the benefits of both personas and analytics systems and addresses some of the shortcomings of each. We provide results from user experiments of and use cases for APG. The result is a better tool than either persona or analytics along for user understanding.
Advanced ovarian cancer is the deadliest gynecologic cancer in World. The survival rate is not significant, and it depends on how well cancer responds to treatment. Generally, the treatment decision depends on Gynecologic oncology experts, which treatment is best for the patient. However, a Gynecologic oncology expert’s limited rationality and personality traits strongly impact a patient’s treatment decision. In addition, according to the American Cancer Society, a patient’s treatment decision may change if Gynecologic oncology experts change. To solve this kind of medical problem, we design a group decision-making model (GDM) based on linguistic preference relations (LPRs) with multiple confidence levels like confidence and doubting levels of experts to analyze experts’ treatment decision opinions digitally for improving the survival rate of patients. In a GDM, consistency analysis and obtaining expert weights are two keys. Hence, this paper design an LPRs with confidence and doubting levels (LPRs-CDLs) based GDM model under a unique consistency improvement algorithm and Pythagorean linguistic trust relations social network analysis driven expert weight determination. First, we define the additive consistency of LPRs-CDLs based on minimum confidence and maximum doubting levels. Second, a consistency reaching process (CRP) algorithm with convergence theorem is offered to improve the consistency of experts’ opinion decisions. Third, develop a trust propagation model based on Pythagorean linguistic trust relations under a social network environment for expert’s weights determination. Then, a social network trust relationship-based GDM model with LPRs-CDLs information is presented to the advanced ovarian cancer patient’s treatment final decision. Finally, we show the rationality of this study with some comparative analysis.
With increasing access to analytics data, personas are more commonly generated from online user analytics data, using algorithmic approaches and, less often, from assumptions. This integration of data and algorithms offers new opportunities to shift personas from flat files to interactive persona systems. We illustrate this transition with Automatic Persona Generation (APG), a persona system. In pushing advancements of both persona and analytics conceptualization, development, and use, APG presents a multi-layered, full-stack integration. APG affords three levels of user data presentation, which are (a) the conceptual data-driven personas, (b) the analytical user metrics, and (c) the foundational user data. Moving to a persona-as-interface approach offers the benefits of both personas and analytics, and their integration addresses many of the shortcomings of each. The result of a persona analytics system is a better user-understanding instrument than either personas or analytics alone.
Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools—data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understanding functionalities for anyone needing such insights.
