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 data set. 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 rumors, 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.
Highlights
Online sources of information are increasingly relied upon by many
We consider trust to be subjective and believe it critical to move beyond the standard view of current multiagent systems trust modeling which adopts, for all agents in the network, a “one size fits all” approach to trust prediction; we reveal how recommendations that support personalization can lead to improved predictions
We evaluate the correctness of the recommender on a reserved testing set using mean absolute error (MAE) and root mean squared error (RMSE) metrics
Summary
Online sources of information are increasingly relied upon by many. According to yearly studies by the Pew Research Center, the percentage of American adults using the internet has jumped from 52% in 2000 to 90% in 2019 [3]. In addition to the established institutions that have made the jump from paper and TV to the web, many new blogs, content aggregators, and social networks have become a vital source in the information diet: up to 62% of American adults rely. While this democratization of information and influence may strike one as appealing, there are reasons to be concerned about this new paradigm. One promising new direction has been to recognize that multiple features of the data may be relevant, and that a proper weighting of these different contributing factors, when reasoning about trustworthiness, is important This is the basic premise of the very novel pursuit known as multi-faceted trust modeling [9,29]. This function shows the relative likelihood of the values for the parameter p, given the fixed parameters α and β
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
