Our work divides into three parts. The first focuses on the struggles and plight of Chinese cinemas and their practitioners after the outbreak of COVID-19 in 2020. The second points out that the state’s assistance policies are only a “drop in the bucket”. The final part finds that the pandemic is not the only reason for the enormous impact on the cinema industry.
It took us three months to complete the project, based on more than 500,000 pieces of data, 200,000 words of interview script, and field visits to more than 20 cinemas around China.
The publicity and originality of our works are strong. It reflects the real situation of movie theaters under the pandemic from the perspective of data and personal stories, and provides a more in-depth perspective that is different from mainstream reports.
Precisely because of this, our work was unanimously recognized and praised by the interviewees. Many interviewees, including movie theater staff and related scholars, forwarded our work and liked it.
Many interviewees said they were inspired. One interviewee was disappointed because the theater operated by the theater continued to lose money and did not receive effective government support. As a result, she fell into insomnia and was not understood by her family, and planned to leave the industry. After seeing our final report, she told us that because she discovered that her situation has also happened to many people, and many cinema workers like her stick to this line because of their love for the cinema, she feels very moved, and no longer intend to leave this line. After the theater resumed work, she posted a message on the social platform: Hello, spring.
Our work has won multiple awards. It won the second prize, the best visualization award, and the best creative award in the fifth China data journalism competition, the first prize in the University Data News Visualization Creative Competition, and selected for the 2020 People’s Daily Online Scholarship Outstanding Financial Media Works.
The data pipeline began with data collection, which drew on existing frameworks for web scraping with Python’s scrapy and splash. Web data contributed to our understanding of subject-matter in two main ways. First, we gathered data concerning the box office, location, seating capacity for over 90% of China’s official cinemas. They offered an insightful glance at the impact of suspension upon the film industry, and a historical review of its development in the last decade. Secondly, we built a text dataset with more than 1 million rows based on Weibo, China’s Twitter, to extract people’s opinions and sentiments regarding the nationwide closing of cinemas. This went a long way in tracking relevant emotion on social media as the pandemic deepened.
The data analysis process relied heavily on the computational power of R and Excel. The most common task was to convert unstructured web data into a cleaner form ready for further group summary and visualization, i.e., alternating between the “long” and “wide data” format. We also explored the distributional characteristics of the business conditions of theaters before the pandemic. With hierarchical clustering, we identified the income pattern that smaller theaters, to a greater extent, fell victim to the suspension. We also published code files and the data dictionary to ensure reproducibility.
What was the hardest part of this project?
The hardest part is to figure out how to organize all the details.
When going out for interviews, we followed the instructor’s advice to collect multimedia material as much as possible. After gathering all the material, however, we were in trouble facing more than 200,000 words, 590 photos, 50 video clips, and 10 audio records. How to tell a good story from those sources? Besides, due to the COVID-19, all of the team members were trapped at home, which adds difficulties to teamwork. To solve the problem, we spent more than 20 hours in online meetings, discussing the details in session and draw an online mind map together. After all the members were clear about the framework, the team was divided into three parts, responsible for writing, visualization, and data analysis respectively. It’s a big challenge for mutual understanding, trust, and support. But our team managed to do it.
After careful consideration, we decided to focus on persons. Data is just a tool and an assistant to tell the feature story. The principle line is the experience of those cinema practitioners under COVID-19: What did they do? How did they feel? Would the pandemic change their career and life completely? And data draws out a bigger picture: What happened to the whole cinema industry? What’s the future of it? We believe the meat and potatoes of good journalistic work are always issues concerning people. While with the broad background portrayed by data, such stories can become more vivid and attractive. The personal tragedy is embedded in history, with more social and humanistic value. In conclusion, it is an integrated journalism work creatively combining big data with the personal feature. The visualization, like the time axis and word cloud, is also aligned with this purpose.
What can others learn from this project?
First of all, this project has the significance of recording the times. When Covid-19 broke out, citizens’ lives and social production are greatly affected. To be specific, cinemas were all closed for the sake of epidemic prevention and national policies. As a result, not only the owner of cinemas but also cinema workers were greatly affected and barely had income during a tough time. We assume that it is the journalists’ priority to record the era and show human care for ordinary people. Thus, we choose to present this project themed on cinemas during Covid-19 in form of data journalism. We maintain that it was a valuable opportunity to record this hard time and how film practitioners managed to survive. Hence, we propose that other journalists could pay more attention to a specific group or industry in a specific period in selecting topics.
Secondly, the biggest feature of our project, which we feel most proud of, is the combination of feature writing, images, sound elements, and data. In our data news project, feature writing serves as the foundation, and multimedia elements enrich the presentation form of the work. Through a large amount of data mining and quantitative analysis, we hope to convey to the audience the current situation of the movie theater and the development of the entire movie industry in the form of data news. Plus, we took into account the interactive design, hoping to create an immersive experience for news users. We hope that interactive design and large-scale data mining would be useful for other journalists while producing data news projects.