Evidence Aware Neural Pornographic Text Identification for Child Protection

Authors

  • Kaisong Song Alibaba Group, China
  • Yangyang Kang Alibaba Group, China
  • Wei Gao Singapore Management University, Singapore
  • Zhe Gao Ant Financial Services Group, China
  • Changlong Sun Alibaba Group, China
  • Xiaozhong Liu Indiana University Bloomington, USA

Keywords:

Other Social Impact

Abstract

Identifying pornographic text online is practically useful to protect children from access to such adult content. However, some authors may intentionally avoid using sensitive words in their pornographic texts to take advantage of the lack of human audits. Without prior knowledge guidance, real semantics of such pornographic text is difficult to understand by existing methods due to its high context-sensitivity and heavy usage of figurative language, which brings huge challenges to the porn detection systems used in social media platforms. In this paper, we approach to the problem as a document-level porn identification task by locating and integrating sentence-level evidence and propose a novel Evidence-Aware Neural Porn Classification (eNPC) model. Specifically, we first propose a basic model which locates porn indicative sentences in the document with a multiple instance learning model, and then aggregate the sentence-level evidence to induce document label with self-attention mechanism. Moreover, we consider label dependencies within local context. Finally, we further enhance the sentence representation with prior knowledge produced by an automatic porn lexicon construction strategy. Extensive experimental results show that our model exhibits consistent superiority over competitors on two real-world Chinese novel datasets and an English story dataset.

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Published

2021-05-18

How to Cite

Song, K., Kang, Y., Gao, W., Gao, Z., Sun, C., & Liu, X. (2021). Evidence Aware Neural Pornographic Text Identification for Child Protection. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14939-14947. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17753

Issue

Section

AAAI Special Track on AI for Social Impact