Detecting crowdsourcing spammers in community question answering websites

Abstract

The growth of online crowdsourcing marketplaces has attracted massive normal buyers and micro workers, even campaigners and malicious users who post spamming jobs. Due to the significant role in information seeking and providing, CQA (Community Question Answering) has become a target of crowdsourcing spammers. In this paper, we aim to develop a solution to detect crowdsourcing spammers in CQA websites. Based on the ground-truth data, we conduct a hybrid analysis including both non-semantic and semantic analysis with a set of unique features (e.g., profile features, social network features, content features and linguistic features). With the help of proposed features, we develop a supervised machine learning solution for detecting crowdsourcing spammers in Community QA. Our method achieves a high performance with an AUC (area under the receiver-operating characteristic curve) value of 0.995 and an F1 score of 0.967, which significantly outperforms existing works.