資訊負荷已成為現今資訊時代的一大問題，在資訊量成長快速的狀況下，資訊過
濾的方式也叫無效率。另一方面，社會網絡中的使用者越來越傾向將網路上的內容排
序。本研究主要目的在探討是否可衡量社會網絡中的信任值，以及透過分群的方式是
否能有效協助分析。此外，本研究也提出信任值計算模式，此模式以社會網絡以及線
上評分系統為基礎。本研究透過社會距離的概念，輔以分群法為基礎協助區隔社會網
絡中的個體。並以分群後的個體間距離為信任值推算基礎。
研究結果顯示透過分群法應用在社會網絡中，是能夠推算出信任值。本研究所
提出的模式結合了不同變數如時間變數與評分值的概念，在計算信任值上以多維度的
角度來思考。研究結果也顯示較高的評分結合較短的社會距離夠產生較高的信任值，
較低的評分結合較長的社會距離則產生較低的信任值。這也證明了社會網絡中社會距
離的影響性。縱言之，本研究主要提供了多維度的信任值推算模式，主要考量變數包
含社會距離、權重、時間以及內容評分值，此外，不同層級的個體關係也有不同的重
要性。本研究期望對於社會網絡中信任的概念文獻提供初步的基礎，並且也提供在資
訊爆炸時代下資訊過濾方式的一個新思維。

Information overload is an increasing problem, and  as information available 
continues to grow in volume, current filtering techniques are proving inefficient.  
Social network users and people in general, tend to prioritize recommendations 
coming from people they are acquainted to. The purpose of this study was to 
investigate if it was possible to measure trust within individuals in a social network, 
as well as find out if data clustering methods could help to achieve said goal. Another 
aim was to develop a trust model that would estimate a trust value for content creators 
on an online rating system with social network capabilities. 
This research introduces the concept of social distance, which is drawn from 
clustering methods applied to the social network user base; and incorporates said 
distance in the estimation of trust, as well as user generated ratings. The trust value 
estimated will serve as a metric for filtering and sorting content of any kind based on 
the trustworthiness of the creator.
The results of the study revealed that it is possible to provide an estimate 
measure of trust within individuals in a social network and that clustering methods 
were of significant help into said evaluation as well as the integration of other 
variables affecting the building of trust. It was found that the model proposed by this 
study was able to integrate various variables and provide a more complete and 
integrated, multidimensional value to an estimated trust. Results also showed, that 
higher rating scores combined with shorter social distances provide satisfactory trust 
values, while the opposite happened for subjects presenting lower rating scores in 
combination with longer distances.
The principal conclusion was that our model provides a multidimensional 
estimated value for trust on content from the Internet, that integrates some of the 
variables necessary for the building of trust in online setting, as are: social distance, 
weight of relationship, time, and ratings from an online rating system; as well trust 
levels between individuals within a social network. This study contributes to the 
current literature on trust estimation and social networks role in such endeavors. This 
will provide also an alternative for current information overload issues as well.

Table Content
Ⅱ
Figure Content
Ⅲ
Chapter 1. Introduction
1
1.1 Background
1
1.1.1 The Problem of Information Overload
1
1.1.2 The importance of Social Media
3
1.1.3 Social media and consumer behavior
4
1.1.4 The concept of trust
8
1.2 Problem statement
10
1.3 Research questions
12
1.4 Purpose of the study
13
1.5 Value of the study
14
Chapter 2: Literature Review
16
2.1 Collaborative Filtering Algorithms
16
1.2.1 Online Recommender Systems (ORS)
16
1.2.2 Collaborative Filtering and Social Networks
17
2.2 Trust issues and Social Networks
18
2.2.1 Trust models within Social Networks
19
2.2.2 Common issues for Trust Models
19
2.3 Clustering Techniques
20
Chapter 3: Research Method
22
3.1 Research Framework
22
3.1.1 Self-Organization Maps (SOM)
24
3.1.2 The Social Network
27
3.2 The Trust Model
27
3.2.1 Social Distance
28
3.2.1.1 Calculation of social distance
29
3.2.2 The relationship between social distance and rating score 30
3.2.3 The Estimated Trust Value (ETV)
31
3.2.4 Measure of the weight variable w and the rating R 33
Chapter 4: Analysis
36
4.1 Settings for simulation
36
4. 1.1 Ranges for other variables
38
4.1.2 User alternatives
39
4.2 User Option 1 Results
43
4.2.1 User Option 1 Original Data
43
4.2.2 User Option 1 Clustering Results
44
4.2.2.1 User Option 1 Cluster #1
46
4.2.2.2 User Option 1 Cluster #2
46
4.2.2.3 User Option 1 Cluster #3
47
4.2.3 User Option 1 ETV Results
47
4.3 User Option 2 Results
49
II
4.3.1 User Option 2 Original Data
49
4.3.2 User Option 2 Clustering Results
51
4.3.2.1 User Option 2 Cluster #1
52
4.3.2.2 User Option 2 Cluster #2
53
4.3.2.3 User Option 2 Cluster #3
53
4.3.3 User Option 2 ETV Results
54
4.4 User Option 3 Results
55
4.4.1 User Option 3 Original Data
55
4.4.2 User Option 3 Clustering Results
57
4.4.2.1 User Option 3 Cluster #1
59
4.4.2.2 User Option 3 Cluster #2
60
4.4.2.3 User Option 3 Cluster #3
60
4.4.3 User Option 3 ETV Results
61
Chapter 5: Concluding Remarks
63
5.1 Cross Analysis
63
5.2 Conclusion
68
5.3 Managerial Implications
70
5.4 Research Limitations
71
References
73
Table Content
Table 4.1 Summary User Attributes Ranges 37
Table 4.2 User option #1 attributes 40
Table 4.3 User option #1 “network of friends” sample ranges 41
Table 4.4 User option #2 attributes 41
Table 4.5 User option #2 “network of friends” sample ranges 42
Table 4.6 User option #3 attributes 42
Table 4.7 User option #3 “network of friends” sample ranges 43
Table 4.8 Attributes and ranges for User Option 1 43
Table 4.9 User Option 1 cluster means results 45
Table 4.10 User Option 1 cluster results 45
Table 4.11 User Option 1 ΣETV results by cluster 48
Table 4.12 User Option 1 variable results by cluster 48
Table 4.13 Attributes and ranges for User Option 2 49
Table 4.14 User Option 2 cluster means results 51
Table 4.15 User Option 2 cluster results 52
Table 4.16 User Option 2 ΣETV results by cluster 54
Table 4.17 User Option 2 variable results by cluster 55
Table 4.18 Attributes and ranges for User Option 3 56
Table 4.19 User Option 3 cluster means results 58
Table 4.20 User Option 3 cluster results 58
Table 4.21 User Option 3 ΣETV results by cluster 61
Table 4.22 User Option 3 variable results by cluster 61
Table 5.1 User options results for variable rb 63
Table 5.2 User option results for ETV 64
Table 5.3 User option results for all variables and ETV 65
Table 5.4 Average Σd and ETV values for each user option 66
Table 5.5 Average Σrb variable values and ETV for each user option 67
Figure Content
Figure 1.1. The increase on information vs. the amount of information we
are able to process.
1
Figure 1.2. Diagram of the uneven proportions of information. 3
Figure 1.3. Top 10 U.S. Social Networks and Blogs >> Unique Audience 4
Figure 1.4. Consumers’ preference for sources of product and service
information.
6
Figure 3.1. Research Framework 23
Figure 3.2 Concept of Self-Organization Maps 24
Figure 3.3 Examples of topological neighborhood (N,(t)) on an SOM 25
Figure 3.4 Representation of a three-dimensional grid-like SOM 25
Figure 3.5 Concept of Best Matching Unit (BMU) in learning process. 26
Figure 3.6 Type of data that will be collected from the SN 28
Figure 3.7 Step-by-step processes for the calculation of Social Distance 29
Figure 3.8 Representation of clusters in a data set. 30
Figure 3.9 Diagram of ETV variables 33
Figure 3.10 Rating System diagram and point values for R 34
Figure 4.1. Users attributes 37
Figure 4.2. User Attributes Ranges 37
Figure 4.3 SOM clusters for User Option 1 45
Figure 4.4 SOM clusters for User Option 2 51
Figure 4.5 SOM clusters for User Option 3 58
Figure 5.1 Average Σrb values for each user option 64
Figure 5.2 Average distances by user option 66
Figure 5.3 Average ETV by user option 67

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