Federated Hierarchical Hybrid Networks for Clickbait Detection

TL;DR

This paper introduces a federated learning framework using hierarchical hybrid networks to detect clickbait across distributed data sources while preserving privacy, demonstrating improved effectiveness over existing methods.

Contribution

The paper proposes a novel federated hierarchical hybrid network model for clickbait detection that handles distributed data with different feature spaces without sharing raw data.

Findings

Effective clickbait detection on social media datasets

Outperforms state-of-the-art approaches

Preserves data privacy in distributed settings

Abstract

Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization. As the adverse effects of clickbait attract more and more attention, researchers have started to explore machine learning techniques to automatically detect clickbaits. Previous work on clickbait detection assumes that all the training data is available locally during training. In many real-world applications, however, training data is generally distributedly stored by different parties (e.g., different parties maintain data with different feature spaces), and the parties cannot share their data with each other due to data privacy issues. It is challenging to build models of high-quality federally for detecting clickbaits effectively without data sharing. In this paper, we propose a federated training framework, which is called federated hierarchical hybrid networks, to build clickbait detection models, where the titles and contents are stored by different parties, whose relationships must be exploited for clickbait detection. We empirically demonstrate that our approach is effective by comparing our approach to the state-of-the-art approaches using datasets from social media.