School of Humanities and Social Science Division of Social Science 214 Identifying Protest Events in China with Social Media Data Supervisor: ZHANG Han / SOSC Student: LI Xinyue / GCS-SOSC Course: UROP1100, Summer The use of Internet exploded since 1990s in China with tremendous growth in social media, which built up the channel and platform for Chinese citizens to express their opinions and grievances. Leaders emerge as people with same interest gather through social media and protest for their rights. For fear of large-scale protests, the government is putting more surveillance and control on the social media. Online posts related to sensitive topics are shielded to the public. This paper analyzes social media dataset about protests in China. The statistical analysis reveals that the three major problems underlying in Chinese society are land acquisition, home property rights, and unpaid wages. Violent or disruptive actions are most common forms of protests, as people find no solutions to solve their problems, and companies or governments tend to use extreme method to suppress the protests. The research result about social media and protests is worthy, as it has implication for the social problems rooted in China and the relationship among government, citizens, and social media. How to Use Machine Learning Predictions in Regression Model Supervisor: ZHANG Han / SOSC Student: CHO Young Beom / SOSC Course: UROP1100, Spring The paper is the progress report on UROP 1100 project, How to use Machine Learning Predictions in Regression Model supervised by professor Han Zhang. The objective of the project is to implement the CCER package written in the R to Python interface. The paper first shows the literature that was mainly referred to understand the conceptual and mathematical side of the correction model, then briefly explains the Python libraries which are used and their functionalities. The paper reports the current progress, ongoing issues of discrepancies in the optimization function between R and Python, and deciding which Bayesian modeling libraries to use. Finally, the paper concludes by introducing further tasks that can be developed to improve the package, in both the statistical modeling and ML prediction error side.
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