Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management

@article{Parmentier2022PersonalizedMT,
  title={Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management},
  author={Alexandre Parmentier and Robin Cohen and Xueguang Ma and Gaurav Sahu and Queenie Chen},
  journal={International Journal of Data Science and Analytics},
  year={2022},
  volume={13},
  pages={399-425}
}
In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modeling. In particular, we propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. We focus on demonstrating how clustering of similar users enables a critical new functionality: supporting more personalized, and thus more accurate predictions for users… 
1 Citations
Online information disorder: fake news, bots and trolls
TLDR
Current challenges in the area of fake news identification are presented and contributions published in this editorial are discussed and discussed in the authors' special issue.

References

SHOWING 1-10 OF 52 REFERENCES
Multi-faceted Trust-based Collaborative Filtering
TLDR
A multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks is proposed and shown to outperform both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations.
Multi-faceted trust and distrust prediction for recommender systems
A Bayesian Multiagent Trust Model for Social Networks
TLDR
Empirical results are presented to demonstrate the effectiveness of the methods introduced, both in simulations featuring head to head comparisons with competitors, and in the context of some existing online social networks where ground truth data are available.
Personalized Multi-Faceted Trust Modeling in Social Networks
In this work we develop a comprehensive approach for multi-faceted trust modeling (MFTM) and use it to model trustworthiness of peers in online social networks. We then show how this data-driven
A Survey on Trust Modeling
TLDR
The foundations of trust models for applications in these contexts are outlined in terms of the concept of trust, trust assessment, trust constructs, trust scales, trust properties, trust formulation, and applications of trust.
The Current State of Online Social Networking for the Health Community: Where Trust Modeling Research May Be of Value
TLDR
The prevalence of misleading information in health-oriented online social networks and discussion boards is discussed, and the use of trust modeling, an approach examined by arti cial intelligence researchers in the sub eld of multi-agent systems, is advocated.
StereoTrust: a group based personalized trust model
TLDR
StereoTrust is proposed, a computational trust model inspired by real life stereotypes that can be used as a complimentary mechanism to provide the initial trust value for a stranger, especially when there is no trusted, common third parties.
Social Collaborative Filtering by Trust
TLDR
A model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations.
TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings
TLDR
This work proposes TrustSVD, a trust-based matrix factorization technique that is the first to extend SVD++ with social trust information and achieves better accuracy than other ten counterparts, and can better handle the concerned issues.
Trust in recommender systems
TLDR
This paper proposes that the trustworthiness of users must be an important consideration in guiding recommendation and presents two computational models of trust and shows how they can be readily incorporated into standard collaborative filtering frameworks in a variety of ways.
...
...