Extracting Semantic Representations of Sexual Biases from Word Vectors

Extracting Semantic Representations of Sexual Biases from Word Vectors

在台灣的標誌性論壇——PTT論壇中，帶有性別偏見的線上仇恨言論問題發展已久，詞向量等計算語言學的應用也常帶有性別偏見。本研究利用詞向量表徵（Word2vec），從PTT「母豬教」鄉民的言談來分析中文厭女言論的詞向量的語意分佈，發現母豬教徒相關言論的確帶有性別偏見與歧視。本研究採取質性加上量化分析，作為第一篇利用自然語言處理技術分析PTT論壇上的仇女歧視言論的研究。

Sexually biased cyberhate speech has become a fast-growing problem on PTT (a representative online forum in Taiwan). The applications of computational linguistics like word embeddings would also carry similar biases. This paper analyzed the distribution of word representations of netizens from mu zhu jiao (a cult that often produces misogynistic cyberhate speech). Word vector representations (word2vec) was utilized for scrutinizing semantic representations of texts found on PTT. The findings from the distributed semantic representation of mu zhu jiao implied a sexual bias against them. This paper serves as the first study which investigates the distribution of word representations of the abusive language on PTT forum with an NLP method by taking advantage of both quantitative and qualitative methods.