Prediction of Political Leanings of Chinese Speaking Twitter Users

TL;DR

This paper introduces a supervised classification model for predicting the political leanings of Chinese-speaking Twitter users, utilizing Chinese text segmentation and vectorization, and demonstrates high accuracy in stance detection.

Contribution

It is the first to develop a Chinese political tweet stance prediction model, addressing language-specific challenges with segmentation and offering a new tool for political analysis.

Findings

High accuracy in classifying political stances

Effective Chinese text segmentation with Jieba

First Chinese-language political stance prediction model

Abstract

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.