3. Research methodology

It is also said that the value of trust changes when it is being applied differently. The Trust Referent Characteristic table developed by Mcknight [22] best describes the trust-related characteristics that a node may display in an online environment. In this research, the Trust Referent Characteristics table has been improved with additional identified values by analyzing datasets obtained from [23–25]. Table 1 shows the updated Trust Referent Characteristic

This research examines the integrity of user-generated contents (highlighted in grey) as a form of trust on the social networking sites. Since social networking sites contain large amount of unstructured texts, content integrity can be analyzed by adopting text analysis algorithms from many researchers [26–29]. Algorithmic details and latest research application updates can be found in Ref. [30, 31]; therefore, it will not be covered in this article. Diffusion equations used in this research are formulated using Kempe's [32, 33] activation function. Eq. (1) illus-

Pvðu, <sup>S</sup>Þ ¼ <sup>ƒ</sup>vð<sup>S</sup> <sup>∪</sup>fugÞ <sup>−</sup> <sup>ƒ</sup>vðS<sup>Þ</sup>

Trust-related characteristic Second-order conceptual category

Dynamic Competence

Foresight Predictability

Responsive Benevolence Honest Integrity

Careful Psychology, mentality

Contemplation Knowledgeable Personally attractive Prospect

<sup>1</sup> <sup>−</sup> <sup>ƒ</sup>vðS<sup>Þ</sup> (1)

table.

Competent Expert Experience

Predictable

Good moral Good will

Credible Reliable Dependable Openness

Benevolent, Caring

Shared understanding

Table 1. Trust referent characteristic table.

trates Kempe's function:

6 Recent Progress in Parallel and Distributed Computing

The process of profiling trust involves two stages where the first stage uses Texted Oriented Opinion Mining [30] algorithm to analyze user-generated text contents of each social node and return a trust probability score with a minimum value of 0 and a maximum value of 1, and the second stage is to analyze the cumulative objective score of each social node's text contents. The simulation uses Matlab r2016a in the experiment where genetic algorithm diffusion model (GADM) is the base algorithm that performs the influential diffusion. The Virtual Social Node (VSN) algorithm plays a simple yet important role in the influential diffusion process by simulating a virtual social network consisting of nodes and relationship links between nodes. The virtual social network simulated by VSN is structured as a ∪ b or c ∩ (a ∪ b) (Figure 2) such that A is the highest superset of all nodes in the social network, and a, b, c … n are subsets of A denoted as {a, b, c…n} ⊂ A and consist of a total of 5117944 social nodes. The influence diffusion adopts the bottom-up approach where it initiates from the lowest subset all the way to the highest superset. These algorithms had been published in Refs. [31, 30, 34] therefore will not be discussed in detail in this article.

Figure 2. Social network relationship diagram.

GADM operates in a way that an influence is diffused to any social nodes given the existence of a physical link between the source node and the recipient node. Any influence diffused by GADM is considered successful if the influence propagated and acknowledged by the recipient social node. A number of enhancements will be carried out on GADM. These enhanced algorithms are trust-enhanced genetic algorithm diffusion model (T-GADM) that includes trust values calculated from the uncovering of trusted social node process into the influential diffusion and calculation process. In addition, domain values are included into the enhanced T-GADM resulting into the domain specified trust-enhanced genetic algorithm diffusion model (DST-GADM). On top of the diffusion of influence from trusted social nodes, DST- GADM brings influence diffusion to a whole new level, where these influences will be diffused to target at recipient social nodes that shares the similar interest (or domains) with the source social node. The results generated by these algorithms are presented as probability values with a minimum value of 0 and a maximum value of 1, with an accuracy of 3 decimal points. Details of the algorithm design are discussed in Section4.
