Digital Research Methods for Developing a New “Cultural Power Index”
by Brian Yecies and Jack Yang
How do we really test the reception of cultural products in international markets? How useful are conventional industry analytics such as box office statistics, online likes, user ratings on social networking platforms, IMDB votes, and recommendation sites like ebay and Amazon, as well as Rotten Tomatoes? How can we trust the reports and algorithms behind these sources? And how can we trust other “soft power” indexes without knowing their internal workings? These are some of the issues we face in trying to construct what we call the Cultural Power Index for the Australian Research Council Discovery Project “Digital China: from cultural presence to innovative nation”.
Despite the seeming wealth of composited intelligence offered by reputational indexes such as the “Soft Power 30”, Nation Brands Index and Good Country Index, the jury is still out on whether we can zoom in on a qualitative barometer of change that transcends conspicuous social, cultural and industrial trends.
In this Digital China project, we aim to emulate these and other types of indexes by creating a big data analytical tool – a Cultural Power Index dashboard for measuring and visualizing the social, cultural and industrial impacts of digital platforms, with special regard to the “going out” strategies of contemporary Chinese culture. This will be no small task, given the limited size of the project team and the scarcity of publicly available resources – relative to the expansive funding and human capital (upwards of 10,000 computer programmers and data entry personnel) behind popular real-time commercial research platforms such as the Bloomberg Launchpad.
Nonetheless, each member of the project team is utilizing their individual expertise across a number of fields – the political economy of the media, creative industries, cultural policy studies, user experience, big data analytics, and Diasporic media – to mount a comprehensive and critical assessment of China’s current internationalization strategies. By the end of this 2017-2019 Australian Research Council Discovery Project, our team of seven scholars will quantify this data and display a Cultural Power Index as a barometer within an interactive dashboard. Easy to say, hard to do.
In short, our project addresses the emergence of a “Digital China”. Combining digital methods, interviews, focus groups and surveys with both professionals and everyday platform users in Australia, Hong Kong SAR, Singapore, and South Korea (hereafter Korea), the project aims to generate cutting-edge analysis and data for researchers, firms, practitioners and policymakers. These four locations illustrate different economic and cultural relationships with China and varied degrees of digital connectivity. Following the completion of the project, our dashboard will show how Chinese online and mobile Internet initiatives – including video sharing sites, social networking sites, e-commerce platforms, and other applications – are connecting with consumers, audiences and users in these regions and how “foreign” digital businesses are establishing and maintaining a presence in China.
To develop such an interactive tool from scratch, we are harvesting, categorizing, analyzing and visualizing Chinese- and English-language data generated by a range of key users, organizations, policymakers and platforms – all of which are shaping China’s digital transformation. Specifically, we have developed a smart-learning analysis platform for handling massive amounts of social-media data. The platform consists of three parts: data collection, analysis, and visualization. The data collection module aims to access and extract contents from different web-based public media data sources such as Twitter, Douban, Weibo and Taobao, Alibaba’s giant e-commerce platform. The extracted contents, such as text comments and reviews, ratings and rankings/votes/likes and user profiles, are stored in a database that can be mined in a number of different ways, including automated machine learning. A human analyst will assist with three major tasks: classification, clustering, prediction and sentiment analysis; qualitative and quantitative analysis of coded texts to understand the social connections between consumers and producers; and geospatial analysis – searching for population clusters in specific regions and over particular time periods.
Presently, and behind the scenes at the SMART Infrastructure Facility at the University of Wollongong, our big data research is focusing on user behaviour patterns on multiple Chinese- and English-language platforms. To investigate this domain, first, huge data samples are harvested via a big data scraping crawler. Second, we then manipulate features from raw samples to facilitate analysis of various film or product details, review comments, and user profiles. Third, we utilize an algorithm for content-mining functions.
In layman’s terms, through the analytical tools available to us via the SMART Infrastructure Facility, we are employing various statistical techniques and methodologies for big data analysis. These include topic modelling; defining the elements of key opinion leadership; discovering unusual data clusters; text data mining; and documenting user/follower networks and their interactions. The primary aim is to identify, harvest, categorise and then analyse key words and phrases in millions of posts that are associated with various aspects of the production, circulation and consumption of creative industry contents and their subsequent reception across the Chinese diaspora. In so doing we are investigating issues, debates and other factors that are conspicuously related to soft power, in addition to numerous key firms, practitioners and policy stakeholders from a range of information and communication technology industries.
Ultimately, the cultural power index that we are developing will be used to characterize the relationships between the creation, dissemination and reception of Chinese media entertainment and popular culture contents and social media posts – in different geolocations (China, Hong Kong, Taiwan, Singapore, Australia and South Korea) and at different times, thereby giving us a better understanding of Digital China than is currently available through conventional studies and research methods, as well as published indexes.
References The Soft Power 30 index, published annually by Portland Communications and the USC Center on Public Diplomacy, distils trends based on the categories of government, digital, culture, enterprise, engagement and education, aggregated with survey polling data on: cuisine, tech products, friendliness, culture, luxury goods, foreign policy and liveability. See: McClory, Jonathan (18 July 2017). “The Soft Power 30: A Global Ranking of Soft Power, 2017” (PDF). Portland. Retrieved 1 June 2018.  Both the Nation Brands Index and Good Country Index were initially developed by Simon Anholt. While the former attempts to assess multiple countries’ viewpoints across the categories of culture, governance, people, exports, tourism, investment and immigration, the latter quantifies a nation’s global contributions to: science and technology, culture, international peace and security, world order, planet and climate, prosperity and equality, and health and well-being. See: https://nation-brands.gfk.com/; and https://goodcountry.org/.