Gender Prediction for Chinese Social Media Data
By Wen Li, Markus Dickinson. 2017
17 citations recorded by the source corpus.
Social media provides users a platform to publish messages and socialize with others, and microblogs have gained more users than ever in recent years. With such usage, user profiling is a popular task in computational linguistics and text mining. Different approaches have been used to predict users’ gender, age, and other information, but most of this work has been done on English and other Western languages. The goal of this project is to predict the gender of users based on their posts on Weibo, a Chinese micro-blogging platform. Given issues in Chinese word segmentation, we explore character and word n-grams as features for this task, as well as using character and word embeddings for classification. Given how the data is extracted, we approach the task on a per-post basis, and we show the difficulties of the task for both humans and computers. Nonetheless, we present encouraging results and point to future improvements.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Ugur Kursuncu
- Manas Gaur
- Amit P. Sheth
- Yang Trista Cao
- Krishnaprasad Thirunarayan
- Ismailcem Budak Arpinar
- Hal Daumé Iii
- Trevor Darrell
Citations in the corpus
- Predictive Analysis on Twitter: Techniques and Applications (2018, methodology): “tter data for health-related, drug abuse, and harassment activities. Existing approaches utilized statistical features [115] and seldom involved background knowledge along with social information. In [116], a dataset from Sina Weibo, which is a counterpart of the micro-blogging platform Twitter, in ”
- Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “Verhoeven, Škrjanec, and Pollak (2017) N N Y Y − Y Y N Larson (2017) N Y Y Y Y N N Y Koolen and van Cranenburgh (2017) N N Y N − N Y − Tatman (2017) N N Y Y − Y Y N Soler-Company and Wanner (2017) N N Y Y − Y Y N Ljubešić, Fišer, and Erjavec (2017) N N Y N − Y Y − Litvinova et al. (2017) N N Y N − Y”