Implicit User Trust Modeling Based on User Attributes and Behavior in Online Social Networks

Khan, Jebran; Lee, Sungchang

doi:10.1109/access.2019.2943877

Cited by 22 publications

(6 citation statements)

References 54 publications

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“…To fight against such false or misleading information, several initiatives for manual fact-checking have been launched. Some notable fact-checking organizations include FactCheck.org, 5 Snopes, 6 PolitiFact, 7 and FullFact. 8 A large body of research focused on developing automatic systems for detecting the factuality of such information [146,147,148,149,150].…”

Section: Misinformationmentioning

confidence: 99%

See 1 more Smart Citation

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Alsmadi¹,

Ahmad²,

Nazzal³

et al. 2021

Preprint

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The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing (NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these ML and NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this paper, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications, namely (i) rumors detection, (ii) satires detection, (iii) clickbaits & spams identification, (iv) hate speech detection, (v) misinformation detection, and (vi) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.

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Section: Misinformationmentioning

confidence: 99%

“…The key advantage of such platforms is the wide scope of the audience that allows individuals to directly connect and share content. For example, expressing an opinion, sentiment, and emotion towards products, entities, individuals, and/or society [3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Alsmadi¹,

Ahmad²,

Nazzal³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In [3], a node uses triangular fuzzy numbers [43], to represent the weights of different trust criteria used to evaluate a trust value so that more accurate criteria are given higher weight values. Despite the abundance of literature on trust and reputation, the subjectivity issue has not been well investigated [27], particularly in the 5G context. Specifically, mechanisms to address the effect of ultra-densification, high heterogeneity, and high variability, to the accuracy of recommendations as a result of subjectivity is yet to be discovered.…”

Section: Using Subjectivity Of Data In Trmmentioning

confidence: 99%

Trust and Reputation Management for Securing Collaboration in 5G Access Networks: The Road Ahead

et al. 2020

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Trust represents the belief or perception of an entity, such as a mobile device or a node, in the extent to which future actions and reactions are appropriate in a collaborative relationship. Reputation represents the network-wide belief or perception of the trustworthiness of an entity. Each entity computes and assigns a trust or reputation value, which increases and decreases with the appropriateness of actions and reactions, to another entity in order to ensure a healthy collaborative relationship. Trust and reputation management (TRM) has been investigated to improve the security of traditional networks, particularly the access networks. In 5G, the access networks are multi-hop networks formed by entities which may not be trustable, and so such networks are prone to attacks, such as Sybil and crude attacks. TRM addresses such attacks to enhance the overall network performance, including reliability, scalability, and stability. Nevertheless, the investigation of TRM in 5G, which is the next-generation wireless networks, is still at its infancy. TRM must cater for the characteristics of 5G. Firstly, ultra-densification due to the exponential growth of mobile users and data traffic. Secondly, high heterogeneity due to the different characteristics of mobile users, such as different transmission characteristics (e.g., different transmission power) and different user equipment (e.g., laptops and smartphones). Thirdly, high variability due to the dynamicity of the entities' behaviors and operating environment. TRM must also cater for the core features of 5G (e.g., millimeter wave transmission, and device-to-device communication) and the core technologies of 5G (e.g., massive MIMO and beamforming, and network virtualization). In this paper, a review of TRM schemes in 5G and traditional networks, which can be leveraged to 5G, is presented. We also provide an insight on some of the important open issues and vulnerabilities in 5G networks that can be resolved using a TRM framework.

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“…Usually people get connected with others in online social networks (OSN) through their mobile phones or any web networks like LinkedIn, Facebook, Twitter, Google+ and so forth. Online social networks (OSN) is a web service which virtually connects likeminded people with same backgrounds, activities, and interests 1,2 . In a survey on social network users of America conducted by comScore, 3 90% of their time spending with mobile phones for Instagram activities.…”

Section: Introductionmentioning

confidence: 99%

A novel trust recommendation model in online social networks using soft computing methods

Sirisala¹,

Yarava

Reddy³

et al. 2022

Concurrency and Computation

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Summary Social network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.

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Implicit User Trust Modeling Based on User Attributes and Behavior in Online Social Networks

Cited by 22 publications

References 54 publications

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

Trust and Reputation Management for Securing Collaboration in 5G Access Networks: The Road Ahead

A novel trust recommendation model in online social networks using soft computing methods

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