Name
Abstract | ||
|---|---|---|
Understanding political polarization is an important problem when one studies the culture of a democratic country. As a platform for discussing social issues, social media such as Twitter contains rich information about political polarization. In this paper, we propose an automatic method for discovering information from social media that can help people understand political polarization of the country. Previous researches have answered the "who" question, as they proposed methods for identifying ideal points of social media users. In our work, we make a step forward by answering the "what" question. Our method consists of two main techniques, namely, ideal point estimation and discriminative natural language processing. The inputs of our method are raw social media data, and the outputs are representative phrases for different political sides. Using real-world data from Twitter, we also verify that the representative phrases our method generates are consistent with our general knowledge of political polarization in Japan. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-82147-0_5 | KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, KSEM 2021, PT II |
Keywords | DocType | Volume |
Political polarization, Bayesian method, User profiling, Social media | Conference | 12816 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihong Zhang | 1 | 9 | 10.65 |
| Masumi Shirakawa | 2 | 51 | 7.69 |
| Takahiro Hara | 3 | 1819 | 193.85 |