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A computational approach to the study of political activities on social media
Clustering and classification
Sunflower student movement
|Issue Date: ||2018-07-11 15:27:14 (UTC+8)|
In recent years, due to the rise of social media websites on Internet and the popularity of mobile devices capable of Internet access, people can quickly publish their statuses and messages to social media anytime at any place. Internet has changed our lives; we use Internet in almost everything we do. As of 2017, Facebook had 2 billion monthly active users. It is the most popular social networking platform in the world. Therefore, we choose Facebook as our research platform.
This dissertation focuses on the analysis of political activities entirely. We use posts of fan pages to analyze political activities and then construct the interaction features and sentiment features of posts on Facebook. We use characteristics of features to analyze political activities. The sunflower student movement focuses on the interaction features. We use methods to search popular posts and to analyze information diffusion. Then, we mine important Facebook users. We get popular posts and find that three users are active and important users through sharing-reaction during the sunflower student movement. However, the sunflower movement cannot investigate the sentiment features because all fan pages fight against the Cross-Strait Service Trade Agreement (CSSTA). For the sentiment features, we study prediction of political tendency of posts. We also collect posts from political groups of fan pages in America; we build sentiment features for the prediction and evaluate prediction performance.
To summarize, in this dissertation, we propose novel methods to analyze social media datasets that contain valuable information but do not contain any network structure required by other methods.
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|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0097753506|
|Data Type: ||thesis|
|Appears in Collections:||[資訊科學系] 學位論文|
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