Information dissemination on China’s social media plays an increasingly important role in the rescue of sudden disasters. Based on posts and reposts of Weibo hashtags #Mutual support on Henan Floods#, we explored the role of reposting on sudden disasters.
Firstly, the project showed how rescue messages were disseminated on social networks by visualizing the reposting chain of a rescue post We found people with a small number of followers may cause subsequent spreads. Secondly, we analyzed locations from rescue messages of the initial posts. It revealed an urban-rural disparity that urban citizens are easier to spread help-seeking messages.
The context of the project is that China’s social media has played an increasingly essential and sophisticated role to disseminate rescue information not only on Zhengzhou floods as well as the outbreak of Covid-19 in Wuhan, thus we discussed a common phenomenon with a new perspective. We successfully illustrated that reposting by the average person is powerful through visualizing the reposting chain (26309 reposts) of a single Weibo. We believed that the conclusion of visualization could be a reference for our readers that the behavior of reposting could be powerful to spread essential information when faced with sudden-onset disasters instead of self-hesitation or lack of confidence.
Python: Crawl, clean, and analyze 26,309 reposts; parse the text of reposts. We set the rule that if “A” reposted a blog from “B”, then the reposting text of “A” would be marked as “a_sentence//@b_name: b_sentence”. Moreover, we generated a reposting chain for each user and found the source of messages, converting network data into a proper format for visualization.
Gephi: Generate a visual sketch of the reposting chain for 26309 reposting data.
What was the hardest part of this project?
The hardest part of the project was to find a proper way to reveal the essence of unexpected disasters besides in retrospect on the scene as well as figure out the best approach to tell a story through data and visualization. The project captured the new phenomenon of online help-seeking, restored its entire process, and responded to the most concerns of readers “Is my reposting helpful on unexpected disasters”. It was also formidable to scrape and process massive data while finding out the spread chain to answer the question. In addition, it was also a challenge to visualize the reposting network of 26309 nodes in a legible and captivating way.
What can others learn from this project?
We found a cozy story through massive data that suggested how a single rescue blog could lead to a large number of reposting and people who played the essential role. We believed other journalists can use the same approach to find stories through analyzing massive data.
This project can also serve as a bit of inspiration for other journalists that a complex problem can be broken down into a series of simple ones to solve.