Multilevel User Credibility Assessment in Social Networks

TL;DR

This paper introduces MultiCred, a multilevel user credibility assessment model for social networks that uses diverse features and deep learning to improve accuracy over binary classification methods.

Contribution

The paper develops a novel dataset and proposes MultiCred, a deep learning-based model for nuanced, multilevel credibility assessment using rich profile, tweet, and comment features.

Findings

MultiCred outperforms existing methods in accuracy metrics.

The dataset enables multilevel credibility evaluation.

Deep language models enhance textual analysis accuracy.

Abstract

Online social networks serve as major platforms for disseminating both real and fake news. Many users--intentionally or unintentionally--spread harmful content, misinformation, and rumors in domains such as politics and business. Consequently, user credibility assessment has become a prominent area of research in recent years. Most existing methods suffer from two key limitations. First, they treat credibility as a binary task, labeling users as either genuine or fake, whereas real-world applications often demand a more nuanced, multilevel evaluation. Second, they rely on only a subset of relevant features, which constrains their predictive performance. In this paper, we address the lack of a dataset suitable for multilevel credibility assessment by first devising a collection method tailored to this task. We then propose the \textit{MultiCred} model, which assigns users to one of several credibility tiers based on a rich and diverse set of features extracted from their profiles, tweets, and comments. MultiCred leverages deep language models for textual analysis and deep neural networks for non-textual data processing. Our extensive experiments demonstrate that MultiCred significantly outperforms existing approaches across multiple accuracy metrics. Our code is publicly available at https://github.com/Mohammad-Moradi/MultiCred.