Covid-19 has claimed 1.93 million lives. Not only the virus but also disinformation spreads globally and causes harm, and it even travels faster than the virus, kills people and increases the risk of racial discrimination. READr analyzes more than 5,000 fact-checking reports and gain insights into the status and trends of the infodemic.
This is the first complete investigative report in the world that analyzed the disinformation about COVID-19. We analyzed more than 5,000 fact-checking reports written by fact-checking organizations in the world to find out the characteristics of disinformation spread in different countries. Not only data analysis, we also interviewed fact checkers from various countries, and they shared the actual harm caused by these disinformation. We also verified the influence of these fake messages in tens of millions of tweets from Twitter, and found that disinformation in the “good news” category is more likely to spread, which was different from the sensational one.
In the part of data analysis, we used web-crawlers to get the fact-checking reports of IFCN(The International Fact-Checking Network), and used R to analyze the data. Manual classification of disinformation was the most time-consuming part of this project. We used TF-IDF word segmentation to help reporter find the same type of disinformation. Also, we used some functions of Google Spreadsheet, like translation, to minimize the time spent on manual classification.
For the data visualization, we use Adobe Illustrator to design graphs and use Flourish to build interactive charts. We use Vue.js and vue-i18n to implement multi-language web page.
What was the hardest part of this project?
When the reporter analyzed more than 5,000 fact-checking reports, we found out that these disinformations were sometimes so ridiculious, and even rearrangement the combination of the terms will be a new disinformation, but some people still believe them. We wanted to convey this feeling to users, because the users may only received one or two disinformation, but the whole world was facing the impact of huge number of disinformation at the same time. With the help of web designers and engineers, we used “slot machines” to convey this feeling. Users could simply “create a disinformation” through click the bottom on the slot machine. Users could experienced that in the process of playing, even if the disinformations you create were absurd, they may be actual disinformations. And you only need to change the combination of terms to generate a new disinformation.
While the epidemic was still developing, that is, when there was hardly any academic resrearch (only a few weeks before our report published, a report contains 225 disinformation sample studies released by the Reuters Institute fot the Study of Journalism). To do this, the newsroom had to assume the responsibility of research, and this is exactly what data journalism should do.
What can others learn from this project?
When users reading the report, they not only absorb new knowledge, the format of the report could also convey more feelings to users. We should grasp every second when users enter the webpage of the report, so that they could get something from every mouse-click or stay.