Weibo Archive for China's Anti-Corruption Campaign
Collection Overview:
Title: Weibo Archive for China's Anti-Corruption Campaign
Arrangement: The data is available as one downloadable file in different format such as Excel (xlsx) and HTML.
Languages: Chinese
Abstract:
The weibo archive collection contains a data set of tweets collected from the Weibo microblogging platform about China's anti-corruption campaign, a major political movement launched by the Chinese President Xi Jinping right after he was named after General Secretary of the CPC in November 2012. This Collection collected tweets relating to the nonofficial voices on anti-corruption campaign since July 2016.
Scope and Contents of Materials
The weibo archive collection contains a data set of tweets collected from the Weibo microblogging platform about a new, effective anti-corruption campaign initiated by President Xi Jiping in China in 2013. This data set contains tweets archived since July 2016.
The tweets are gathered via the Weibo API using Social Feed Manager developed by the George Washington University Libraries, an open source software to retrieve social media data from Twitter, Weibo and other platforms.
This data archive is available for download. For information on accessing the archive, see the Access and Restrictions section of this finding aid.
Subject/Index Terms
Administrative Information:
Access Restrictions: Due to Weibo Terms of Service, this data archive is accessible only to the George Washington University. For specific questions, please contact grc@gwu.edu
Use Restrictions: Due to Weibo Terms of Service, this data archive is only accessible onsite at the George Washington University.
Acquisition Method: This collection was acquired and collected by the George Washington University using the Social Feed Manager.
Processing Information: This collection was a collaboration between Johns Hopkins University, Georgetown University and George Washington University. Project members include: Yunshan Ye, Cathy Zeljak, Ding Ye, Daniel Kerchner, Victor Tan, Laura Wrubel, Justin Littman and Yan He.