SenseTrust: A Sentiment Based Trust Model in Social Network
Abstract
:1. Introduction
1.1. Trust
1.2. Related Works
1.2.1. Trust Models Based on Network Topology
- Members whose levels of output are greater have higher levels of trust.
- If a person’s relation is more oriented to individuals with higher output levels, they are endowed with higher levels of trust.
- While centralization of individuals (those who are in a network center) has a positive impact on their levels of trust, the average levels of trust in all members decease as the entire network centralizes.
- Calculating a user’s skill in a specific subject: calculating the quality of reviews of users’ contents relying on reputation of those who had rated them or authors’ reputation.
- Determining level of dependence between users on thematic categories through calculating average ratings and users’ reviews in thematic categories, and also calculating level of trust via level of dependence in users to a subject and other skills concerning that subject.
1.2.2. Trust Models Based on Interaction
- Categorizing users’ activities in terms of information shared such as reviews, comments posted, ratings, etc. through measures such as number/sequence of reviews, number/sequence of rates, and average of number/length of comments posted, and so on.
- Categorizing binary interactions for different possible interactions/relations that may occur between two individuals; for example, between author and rater, author and author, and rater and rater.
- Popularity trust which refers to acceptance of a member in a community and shows a member’s trust from other members’ perspective.
- Participation trust that points to members’ participation in a community and reflects their trust in a community.
- Conversational trust that specifies length and/or sequence of relations between two members. Longer relations or those with longer sequences indicate more trust between two individuals.
- Publication trust which refers to publication of information received from a person in a network by another one. Publication of more information received from one person in a network by another individual shows their trust in that person’s information and implicitly reflects their trust in producers of that information.
1.2.3. Hybrid Trust Models
- Explicit social trust, which can be established based on conscious social relations. Whenever two users interact with each other, they exchange their lists of friends with each other and store them as graphs of friends. Trust is also created based on a friendship graph in which individuals assign highest level of trust with a value of one to each other through direct relationships.
- Implicit trust, which is created based on sequence and length of relationships between two users. For this purpose, two criteria can be used: Familiarity and similarity of nodes. Familiarity refers to duration of interactions/relations between two nodes and similarity is degree of compliance of two nodes in a familiarity circle.
2. Methodology
2.1. Sentiment and Trust Correlation: A Simple Test
- Yes: 97 users
- I do not know: 3 users
- No: 0 users
2.2. Reliability and Validity
2.3. Sentiment Analysis
2.4. Train RNTN for Trust Sentiment
2.5. Hidden Markov Model
- A set of states.
- Sequence of observations.
- State transition probabilities
- A sequence of observation likelihoods, also called emission probabilities.
- Initial state probabilities
2.6. SenseTrust Schema
- Textual content exchanged has a high volume of exchanges between social network users in the form of texts.
- To perform sentiment analysis on each statement, the outputs of researchers at Stanford University entitled as RNTN is used to discover the hidden sentiments.
- To analyze trust among social network users, based on sentiments discovered in the statements exchanged, Hidden Markov Model (HMM) is utilized.
- Both RNTN and HMM are trained with emails extracted from Enron Corporation undergoing crowdsourcing and labeling.
- To estimate trust among social network users:
- ○
- Statements exchanged among users are imported into the SenseTrust model.
- ○
- They are trained by RNTN and levels of sentiments in statements are discovered through sentiment analysis.
- ○
- Sequences of the values of hidden sentiments in statements are conveyed to the trained HMM to determine the level of trust among users.
- ○
- The SenseTrust output is trust among social network users with three levels of interpretation (Untrusted, Nature, Trusted).
- The SenseTrust model can estimate trust among users of most conventional social networking sites including Twitter, Facebook, etc. based on textual exchanges.
3. Experimental Results
4. Discussion
5. Conclusions and Future Works
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Websites List
References
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Sentiment Levels | ||||||
---|---|---|---|---|---|---|
Very Negative | Negative | Nature | Positive | Very Positive | ||
Trust Levels | Untrusted | 9 | 43 | 13 | 0 | 0 |
Nature | 0 | 3 | 93 | 4 | 0 | |
Trusted | 0 | 0 | 11 | 86 | 38 |
Real Trust Level (by Experts) | |||
---|---|---|---|
Total Tests | Condition Negative | Condition Positive | |
Proposed Models’ Predicted Trust Level | Predicted Positive | False Positive (FP) | True Positive (TP) |
Predicted Negative | True Negative (TN) | False Negative (FN) |
Name | Formula |
---|---|
Precision | |
Recall | |
F-Measure | |
Accuracy | |
Specificity |
Chats Labeled by Experts | ||||
---|---|---|---|---|
Trusted | Nature | Untrusted | ||
Chats Labeled by SenseTrust | Untrusted | 25 | 4 | 0 |
Nature | 5 | 23 | 4 | |
Trusted | 0 | 3 | 26 |
Trust Level | Precision | Recall | F-Measure | Accuracy | Specificity |
---|---|---|---|---|---|
UnTrusted | 0.86 | 0.83 | 0.84 | 0.89 | 0.92 |
Nature | 0.71 | 0.76 | 0.73 | 0.82 | 0.85 |
Trusted | 0.89 | 0.86 | 0.87 | 0.91 | 0.94 |
Average | 0.82 | 0.81 | 0.81 | 0.87 | 0.90 |
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Mohammadi, A.; Hashemi Golpayegani, S.A. SenseTrust: A Sentiment Based Trust Model in Social Network. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 2031-2050. https://doi.org/10.3390/jtaer16060114
Mohammadi A, Hashemi Golpayegani SA. SenseTrust: A Sentiment Based Trust Model in Social Network. Journal of Theoretical and Applied Electronic Commerce Research. 2021; 16(6):2031-2050. https://doi.org/10.3390/jtaer16060114
Chicago/Turabian StyleMohammadi, Alireza, and Seyyed Alireza Hashemi Golpayegani. 2021. "SenseTrust: A Sentiment Based Trust Model in Social Network" Journal of Theoretical and Applied Electronic Commerce Research 16, no. 6: 2031-2050. https://doi.org/10.3390/jtaer16060114
APA StyleMohammadi, A., & Hashemi Golpayegani, S. A. (2021). SenseTrust: A Sentiment Based Trust Model in Social Network. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2031-2050. https://doi.org/10.3390/jtaer16060114