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This book focuses on the surprising generative possibilities which digital and smart technologies offer media consumers, citizens, institutions and governments in making publics and places, across topics as diverse as Twitter audiences, . ; Ying Jiang

The prevailing consumerism in Chinese cyberspace is a growing element of Chinese culture and an important aspect of this book. Chinese bloggers, who have strongly embraced consumerism and tend to be apathetic about politics, have nonetheless demonstrated political passion over issues such as the Western media's negative coverage of China. In this book, Jiang focuses upon this passion — Chinese bloggers' angry reactions to the Western media's coverage of censorship issues in current China — in order to examine China's current potential for political reform. A central focus of this book, then, is the specific issue of censorship and how to interpret the Chinese characteristics of it as a mechanism currently used to maintain state control.

Spatial data is the fundamental of borderland analysis of the geography, natural resources, demography, politics, economy, and culture. As the spatial region used in borderland researching usually covers several neighboring countries' borderland regions, the data is difficult to achieve by one research institution or government. VGI has been proven to be a very successful means of acquiring timely and detailed global spatial data at very low cost. Therefore VGI will be one reasonable source of borderland spatial data. OpenStreetMap (OSM) has been known as the most successful VGI resource. But OSM data model is far different from the traditional authoritative geographic information. Thus the OSM data needs to be converted to the scientist customized data model. With the real world changing fast, the converted data needs to be updated. Therefore, a dynamic integration method for borderland data is presented in this paper. In this method, a machine study mechanism is used to convert the OSM data model to the user data model; a method used to select the changed objects in the researching area over a given period from OSM whole world daily diff file is presented, the change-only information file with designed form is produced automatically. Based on the rules and algorithms mentioned above, we enabled the automatic (or semiautomatic) integration and updating of the borderland database by programming. The developed system was intensively tested.

Spatial data is the fundamental of borderland analysis of the geography, natural resources, demography, politics, economy, and culture. As the spatial region used in borderland researching usually covers several neighboring countries' borderland regions, the data is difficult to achieve by one research institution or government. VGI has been proven to be a very successful means of acquiring timely and detailed global spatial data at very low cost. Therefore VGI will be one reasonable source of borderland spatial data. OpenStreetMap (OSM) has been known as the most successful VGI resource. But OSM data model is far different from the traditional authoritative geographic information. Thus the OSM data needs to be converted to the scientist customized data model. With the real world changing fast, the converted data needs to be updated. Therefore, a dynamic integration method for borderland data is presented in this paper. In this method, a machine study mechanism is used to convert the OSM data model to the user data model; a method used to select the changed objects in the researching area over a given period from OSM whole world daily diff file is presented, the change-only information file with designed form is produced automatically. Based on the rules and algorithms mentioned above, we enabled the automatic (or semiautomatic) integration and updating of the borderland database by programming. The developed system was intensively tested.

The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source.

Rapid urbanization in China and global climate change have increased urban flood exposure in Wuhan, and the increased flood risk has reduced property values in flood-prone areas. The central government of China is promoting the application of the sponge city concept to reduce urban flood risk and improve the environment in cities. Wuhan is one of the pilot cities of this initiative. A shortage of funds is one of the main obstacles to sponge city construction, as is the lack of a suitable business model. To test residents' willingness to pay for sponge city construction, this research analyzed the impact of sponge city construction on the housing values of areas covered by sponge city interventions. The authors conducted interviews and analyzed secondary data to gauge residents' awareness and perceptions of sponge city interventions. The results show that more than half of residents inWuhan are willing to pay for sponge city measures, but the amount they are willing to pay is limited. Residents are more willing to pay for improvements of their living environment than for flood reduction measures.

AbstractIn this new age of globalization, regions attempt to attract foreign direct investment (FDI) in order to achieve regionally balanced development. We revisit existing theories of regional development and FDI by analyzing recent data sets on FDI, employment, and trade in China, Southeast Asia, and South Asia. Using Chinese provincial data in 2004, 2008, and 2013 and applying panel estimations, our econometric results demonstrate that FDI remarkably influenced the concentration of employment in manufacturing, financial, and business services industries within the three Chinese macro‐regions. We also find that FDI is ever transient, always moving away from high‐cost to low‐cost production bases across different regions. This transient nature of FDI is spatially selective and biased, and not able to generate the trickle‐down effects to other neighboring regions. That is why FDI recently moved from China to Southeast and South Asia rather than from its coastal to inland regions. Furthermore, we show that this nature of FDI generally leads to polarization development for regions. As a synthesis or extension of the existing theories, we propose a leapfrog polarization pattern and strategy for vast developing countries in considering their regional development strategies.