Developing a Fast Screening Method for Visual Images in Social Media Events: A Case Study of Twitter Data during 2016 Meinong Earthquake

• Main Authors: FENG,SHU-CHAO, 馮書昭
• Other Authors: Chen, Kung
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/t7gtwa

碩士 === 國立政治大學 === 資訊科學系 === 106 === Recently, thanks to various social media platforms and availability of mobile web, people have got used to interactive through the internet anywhere anytime. Activities on social media have become everyone's routine, such as searching or sharing information and communication. In the meanwhile, these activities make social media platforms a treasure for getting information. This boundless, real-time information strongly connected to our real life, which means, by locating the information of some position and moment, we are able to analyze specific events of the real world. This feature is especially important at analyzing disasters. At the beginning of disasters, getting information is vital for those authorized. By properly extract messages from social media, experts can realize more and much quickly about the disaster to take the next steps. This work provides a case study of 2016 Meinong Earthquake happened in Taiwan by exploring the data on Twitter. First of all, this work analyzes the metadata of tweets and show that tweets with images are more likely to be retweeted. Then, after using the computer vision services to label the images, this work provides the results and resistance of using label co-occurrence to cluster images. In the end, by crawling the user information of popular tweets' publisher, we can realize that besides the media of Taiwan, there are also media from other countries caring about this disasters. Moreover, we also know that most personal user publisher of popular tweets use Japanese. The reason might be that Japanese users are the second most in Tweet and location of Japan is relatively near Taiwan than other occidental countries.