Prediction of Political Leanings of Chinese Speaking Twitter Users
It addresses the lack of political stance prediction models for Chinese tweets, which is an incremental advancement for researchers and analysts in social media and political science.
This work tackles the problem of predicting political leanings of Chinese-speaking Twitter users by building a supervised classifier model, achieving high accuracy in classifying users into groups that approve or disapprove of the Chinese Communist Party based on their tweets.
This work presents a supervised method for generating a classifier model of the stances held by Chinese-speaking politicians and other Twitter users. Many previous works of political tweets prediction exist on English tweets, but to the best of our knowledge, this is the first work that builds prediction model on Chinese political tweets. It firstly collects data by scraping tweets of famous political figure and their related users. It secondly defines the political spectrum in two groups: the group that shows approvals to the Chinese Communist Party and the group that does not. Since there are not space between words in Chinese to identify the independent words, it then completes segmentation and vectorization by Jieba, a Chinese segmentation tool. Finally, it trains the data collected from political tweets and produce a classification model with high accuracy for understanding users' political stances from their tweets on Twitter.