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Outline

Title
Abstract
Figures
Introduction
Objectives
Related Work
Existing Non-Chinese Datasets for Sexism Detection
Data Collection
Data Collection and Processing
Step I: Extract Weibo Data
Step III: Extract Comment Data
Step IV: Process Comment Data
Ethics of Data Collection
Data Annotation
Data Description
Dataset Structure
Dataset Statistics
Experiment Results and Analysis
Discussion
Error Analysis
Conclusion
References
All Topics
Computer Science

SWSR: A Chinese dataset and lexicon for online sexism detection

Profile image of Aiqi JiangAiqi Jiang

Online Social Networks and Media

https://doi.org/10.1016/J.OSNEM.2021.100182
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44 pages

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Abstract

Online sexism has become an increasing concern in social media platforms as it has affected the healthy development of the Internet and can have negative effects in society. While research in the sexism detection domain is growing, most of this research focuses on English as the language and on Twitter as the platform. Our objective here is to broaden the scope of this research by considering the Chinese language on Sina Weibo. We propose the first Chinese sexism dataset -Sina Weibo Sexism Review (SWSR) dataset -, as well as a large Chinese lexicon SexHateLex made of abusive and gender-related terms. We introduce our data collection and annotation process, and provide an exploratory analysis of the dataset characteristics to validate its quality and to show how sexism is manifested in Chinese. The SWSR dataset provides labels at different levels of granularity including (i) sexism or non-sexism, (ii) sexism category and (iii) target type, which can be exploited, among others, for building computational methods to identify and investigate finer-grained gender-related abusive language. We conduct experiments for the three sexism classification tasks making use of state-of-the-art machine learning models. Our results show competitive performance, providing a benchmark for sexism detection in the Chinese language, as well as an error analysis highlighting open challenges need- * This is to indicate the corresponding author.

Figures (11)
Table 1: Existing sexism-related datasets in multiple languages.
Table 1: Existing sexism-related datasets in multiple languages.
Figure 2: An example of Sina Weibo on weibo.cn
Figure 2: An example of Sina Weibo on weibo.cn
Figure 3: Overview of the data collection process.
Figure 3: Overview of the data collection process.
Table 2: Number of weibos collected for each keyword. In this step, we process the collected weibos prior to collecting the associated
Table 2: Number of weibos collected for each keyword. In this step, we process the collected weibos prior to collecting the associated
Table 3: Examples of sexism categories and target types in the dataset.
Table 3: Examples of sexism categories and target types in the dataset.
Figure 4: Distribution of sexism categories and target types in the dataset.
Figure 4: Distribution of sexism categories and target types in the dataset.
Table 4: Description of features in the weibo and comment datasets. Table 5: Statistics of the dataset.
Table 4: Description of features in the weibo and comment datasets. Table 5: Statistics of the dataset.
Figure 5: Distribution of user gender across two classes in the dataset.
Figure 5: Distribution of user gender across two classes in the dataset.
in Chinese. PCT denotes the percentage of each term. 6.2.8. Word Frequency Distribution
in Chinese. PCT denotes the percentage of each term. 6.2.8. Word Frequency Distribution
Table 9: Error analysis for misclassified examples. TL denotes true label and PL denotes ES Cay eee Mile petg Ree | We look at frequent errors across misclassified instances generated from
Table 9: Error analysis for misclassified examples. TL denotes true label and PL denotes ES Cay eee Mile petg Ree | We look at frequent errors across misclassified instances generated from

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