In computer science, this aspect of trust is called direct trust (trust based on direct interactions between two parties). The context-specific nature of trust is discussed in the social and psychological sciences [Rousseau et al.
Properties of Social Networks
We then discuss issues raised by technological advances in social network research and social network analysis. Social networks are of two types: (i) densely connected social networks, in which members have frequent communication and are tightly bounded, and (ii) loosely knit, loosely bounded networks, where only a few members have close communication. frequent and direct with each. others. CSSNs have created the now popular web-based social networks, as can be seen in Figure 5.
Homophiles in social networks are analogous to the phenomenon of "echo chamber" in the media [Jamieson and Cappella 2009]. Small world phenomenon. There are two different views on the "small world" phenomenon in social networks. This reinforces the homophilic nature of social networks that enable the creation of these numerous small worlds.
Importance of Trust in Social Networks
Milgram, the original developer of the "small world problem", famously identified "six degrees of separation" between two people in the world [Milgram 1967]. These works look at our world as a "small world" where everyone is connected to each other with a small chain of an average of 6 people in between. It reveals low chain completion rates in almost all experiments, suggesting that the vast majority of people in the world are highly disconnected, in contrast to the "small world" believers.
Another concept studied in social networks in the context of trust is the strength of ties and relationships. The concept of tie strength has been used and studied for various applications such as knowledge transfer [Levin et al. Although not explicitly mentioned, tie strength is one of the important dimensions for understanding trust in social networks, as tie strength implicitly defines a relationship of trust.
SOCIAL CAPITAL AND SOCIAL TRUST
What is Social Capital?
Gilbert and Karahalios [2009] apply the concept of tie strength to social media data, particularly Facebook datasets, and place friendships between members into strong or weak ties using binary means, that is, the relationship is categorized as strong or weak. In reality, however, bond strength can fall anywhere along the spectrum from weak to strong. The concept of tie strength has been applied and studied for various applications such as knowledge transfer [Levin et al. 2004].
In an online community, social capital refers to the value derived from interactions that support bonding between like-minded people. We thus define the social capital of an online community as the density of interactions that are beneficial to community members. Social capital is low when the number of interactions between members is high, but the nature of these interactions is not beneficial to individuals.
Social Capital versus Social Trust
But we follow Brunie's definition from the collective dimension, as it considers social capital from the perspective of society as a whole. It is clear from this definition that interactions play an important role in defining and measuring social capital. Our definition is in line with Putnam's [2000] view, which refers to social capital as a resource that can be drawn upon by individuals through building connections with others in social and community groups.
Social trust in a group is equal to the perceived trustworthiness of a typical member or the average trustworthiness of all members, characterized by the percentage of cooperative players in the group. The level of social trust is determined by the distribution of the cooperative tendency in the group and by the specific features of the game, which is why it varies between players and games. This is in contrast to common theories that the development of social trust is influenced by socio-developmental and political/institutional features of individuals and societies and can be somewhat established in individuals in their early life (i.e., the view taken from such as Huang [2007]).
Computing Social Trust from Social Capital
The results indicate that most of the variance in the multi-item confidence scale is accounted for by an additive genetic factor. This profile usually contains personal information that the user would like to share with other members of the community (eg contact information, hobbies, etc.). The third step is to interact with other community members and thereby contribute to social capital.
Interactions could be in the form of providing information or providing comments on the information or comments on the other members' comments [Liu et al. The last step is to calculate the social trust based on the social capital, which in turn can be used to improve the social capital of the network through recommendation systems [Chen and Chen 2001;. As shown in the figure, derived social trust can be seen as consisting of three stages:. i) collection of trust information, (ii) trust evaluation and (iii) trust communication.
TRUST INFORMATION COLLECTION MODELS
Experiences provide one aspect of trust information in social networks and must be considered together with the other aspects, namely attitudes and behaviour. Helbing [1994] proposes Boltzmann-like mathematical models for attitude formation and individual behavior in social networks. 2010a] also make this assumption in their work, in which they use a Boltzmann-like equation to capture the effect of sudden events in consumers' behavior and reflect this in the trust assessment.
2010] propose algorithmically quantifiable measures of trust based on communication behavior of members in a social network, with the premise that trust results in probabilistic communication behavior that is statistically different from random communication. Identifying such trust-like behavior then enables the system to track who trusts whom in the network. Future research should focus on a combination of all aspects for holistic analysis of trust in social networks.
SOCIAL TRUST EVALUATION MODELS
Network Structure/Graph-Based Trust Models
Models that exploit social network structure are typically based on the concepts of “Web of trust” or FOAF (Friend-Of-A-Friend). It represents the other members in the person's social network as nodes and the amount of trust he/she has for each of them as the edges. 2009] propose an approach to calculate trust in social networks using a set of trust chains and a trust graph.
Furthermore, it takes advantage of the trust composition capability in the form of merging relevant trust chains to form a basic set of trust chains. The model also includes the origins of trust in social networks, where a user can ask questions about the trust value given by the underlying system. The volume, frequency and even nature of the interaction are important indicators of trust in social networks.
Interaction-Based Trust Models
Second, the social neighborhood is used to calculate the effective trust flow for users who are not in the social neighborhood. Popularity trust is derived from metrics such as how many members follow, read and provide positive feedback on the member's posts. A combination of popularity trust and commitment trust forms the basis for determining social trust in society.
The model aims to increase the community's social capital by encouraging positive interactions within the community and, as a result, increasing social trust in the community. Similarly, dissemination of information obtained from one member to other members in the network indicates a high degree of trust in the information and implicitly in the member who created the information. Interaction-based social trust models consider interactions in the community to calculate trust, but ignore the social network structure.
Hybrid Trust Models
Engagement trust comes from metrics like how often a member visits a site/network, how many members they follow, and how many posts they read and comment on. Behavioral trust is calculated based on two types of trust: conversational trust and propagational trust. Conversational trust determines how long and/or how often two members communicate with each other.
Social network structure provides important information about how members in a community relate to each other and is an important source of information for calculating social trust. Therefore, social trust models must consider both graph structures and interactions within the social networks to calculate social trust.
TRUST DISSEMINATION MODELS
Trust-Based Recommendation Models
Visualization Models
The graph shows the trust value and trust strength, calculated based on the number of transactions/responses between two users. The language is intended to help understand how to model and reason about trust when developing information systems. Trust visualization approaches are very useful for social networking providers to analyze and determine the level of trust in the community.
In addition, the providers can take preventive actions such as introducing new interesting and relevant material/information if the level of trust in the community falls below a certain threshold. Trust visualization allows the provider to control the social network to encourage positive behavior and discourage disruptive behavior.
ANALYSIS
This can help the social network provider to identify if there is a need to stimulate the community by providing relevant material, or by introducing competitions, games, etc. Any social network will therefore have to address the privacy concerns of the members in order to be unconditionally accepted by its members and ultimately be successful. Therefore, the social network will have to provide a centralized trust model taking into account the interactions in the community.
All these aspects must be considered when selecting and developing a trust model in any social networking application. Another important issue in trust management of social networks is the identification of when a social network community becomes a trust community as defined in section 1. At what levels of trust between members, trust between members and the service and trust between members and the provider can the social network be considered as a true community of trust is a very interesting and challenging question.
CONCLUSIONS
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