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

• URL: http://arxiv.org/abs/2111.06440v1
• Date: Thu, 11 Nov 2021 19:40:51 GMT
• Title: Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management
• Authors: Alexandre Parmentier, Robin Cohen, Xueguang Ma, Gaurav Sahu and Queenie Chen
• Abstract summary: We present an approach for predicting trust links between peers in social media. We propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset.
• Score: 61.88858330222619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset. We then discuss how improving the detection of trusted relationships in social media can assist in supporting online users in their battle against the spread of misinformation and rumours, within a social networking environment which has recently exploded in popularity. We conclude with a reflection on a particularly vulnerable user base, older adults, in order to illustrate the value of reasoning about groups of users, looking to some future directions for integrating known preferences with insights gained through data analysis.

Related papers

• Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations [35.77028281332307]
  We propose Contribution-Aware Multimodal User Embedding (CAMUE) for social networks. We show that our approach can provide personalized explainable predictions, automatically mitigating the impact of unreliable information. Our work paves the way for more explainable, reliable, and effective social media user embedding.
  arXiv  Detail & Related papers  (2024-09-04T02:17:32Z)
• JAB: Joint Adversarial Prompting and Belief Augmentation [81.39548637776365]
  We introduce a joint framework in which we probe and improve the robustness of a black-box target model via adversarial prompting and belief augmentation. This framework utilizes an automated red teaming approach to probe the target model, along with a belief augmenter to generate instructions for the target model to improve its robustness to those adversarial probes.
  arXiv  Detail & Related papers  (2023-11-16T00:35:54Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks [63.531790269009704]
  Social Internet of Things (SIoT) is a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things) Due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. We propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT.
  arXiv  Detail & Related papers  (2023-02-22T14:24:45Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• In the Eye of the Beholder: Robust Prediction with Causal User Modeling [27.294341513692164]
  We propose a learning framework for relevance prediction that is robust to changes in the data distribution. Our key observation is that robustness can be obtained by accounting for how users causally perceive the environment.
  arXiv  Detail & Related papers  (2022-06-01T11:33:57Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Multi-faceted Trust-based Collaborative Filtering [4.640835690336653]
  We propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders.
  arXiv  Detail & Related papers  (2020-03-25T15:27:06Z)
• Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks [0.4061135251278187]
  Trust can be defined as a measure to determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected.
  arXiv  Detail & Related papers  (2020-03-21T01:00:02Z)
• An Approach for Time-aware Domain-based Social Influence Prediction [4.753874889216745]
  This paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.
  arXiv  Detail & Related papers  (2020-01-19T10:39:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.