The Russo-Ukraine war on social platforms: Is the United States also responsible? Taiwan most concerned about China threat?

Entry type: Single project

Country/area: Taiwan

Publishing organisation: READr

Organisation size: Small

Publication date: 2022-03-22

Language: Chinese

Authors: Chen Pei-Yu, Syu Siang-Yun, Wu Man-Ru


READr is not just a data newsroom, it is also a digital innovation team. The development of information news in Taiwan media is still not perfect at present. Although the READr is only a small information newsroom, we still try hard to have an indicative impact on the development of data journalism in Taiwan.

We always hope to make breakthroughs in every topic.Without the framework of traditional thinking, the team can make the report more creative and also keep the news professional by presenting stories in a true and complete way.

Project description:

We analyzed discussions on the Russo-Ukrainian War and found that the Ukrainian government is actively creating a image of defending the country on social media, while the United States has also become a global accountability target. In Taiwan, discussions were divided into two major positions, depending on the United States and suspecting the United States.

Impact reached:

In the early stages of the Russo-Ukrainian War, Taiwan became one of the global Twitter discussion focuses. Just like Ukraine, Taiwan are also a neighboring country of a great power, and the great power is keeping a close eye on us. When we analyze these social post data in different social community, whether on Facebook or Twitter, it also helps us and every Taiwan readers to re-examine Taiwan’s internal consensus before facing external threats, and the parts that have not yet reached consensus. These will be very important for Taiwan as a whole in the future.

Techniques/technologies used:

We collected more than 9 million discussions on the Russo-Ukrainian War on Twitter within a week through Twitter’s open API, and then further analyzed them through multiple ways such as the number of posts, word segmentation, classification, and popular tweets, to see the attitudes of global and Taiwanese netizens towards this event.

On the other hand, we also collected posts on the Russo-Ukrainian War from Taiwanese internet users on Facebook, analyzed them by time and word segmentation, and supplemented them with manual tagging and classification to observe changes in public opinion.

Context about the project:

To be honest, processing more than 9 million data is not an easy task, in addition to basic calculations, we also encounter problems such as too complex topics or difficult-to-clean location field data. Fortunately, our reporters have been able to observe more obvious discussion topics by constantly trying to improve word analysis, and by using popular community posts directly for manual tagging, and through political observation in Taiwan which is important in the face of possible war scenarios in the future.

What can other journalists learn from this project?

Compared to other data news, social media data such as posts, users and hashtag is the easiest to obtain but also the easiest to overlook. What insights can we get from the massive amount of daily social posts? This is what our reporters are also working hard to do, whether it is through different ways of word analysis, or what perspective to take on these data and the meaning they represent.

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