Enhancing Web Spam Detection through a Blockchain-Enabled Crowdsourcing Mechanism

TL;DR

This paper introduces a blockchain-based crowdsourcing mechanism to improve web spam detection by incentivizing accurate data labeling, resulting in higher quality training data and more effective machine learning models.

Contribution

It presents a novel blockchain-enabled incentivization framework for crowdsourcing data labeling to enhance spam detection accuracy.

Findings

Incentivized crowdsourcing improves data quality for spam detection.

Blockchain smart contracts ensure transparency and integrity in data submission.

Simulations demonstrate increased effectiveness of spam detection models.

Abstract

The proliferation of spam on the Web has necessitated the development of machine learning models to automate their detection. However, the dynamic nature of spam and the sophisticated evasion techniques employed by spammers often lead to low accuracy in these models. Traditional machine-learning approaches struggle to keep pace with spammers' constantly evolving tactics, resulting in a persistent challenge to maintain high detection rates. To address this, we propose blockchain-enabled incentivized crowdsourcing as a novel solution to enhance spam detection systems. We create an incentive mechanism for data collection and labeling by leveraging blockchain's decentralized and transparent framework. Contributors are rewarded for accurate labels and penalized for inaccuracies, ensuring high-quality data. A smart contract governs the submission and evaluation process, with participants staking cryptocurrency as collateral to guarantee integrity. Simulations show that incentivized crowdsourcing improves data quality, leading to more effective machine-learning models for spam detection. This approach offers a scalable and adaptable solution to the challenges of traditional methods.