WHO’s Covid-19 Origins Tracing

Country/area: China

Organisation: China Global Television Network

Organisation size: Big

Publication date: 30/07/2021

Credit: Yan MEI, Yan REN, Xin LIU, Xiaodi SHEN, Jing GAO, Nan ZHANG, Shiqu XIANG, Sudeshna SARKAR, Ruixin ZHANG, Yu DAI, Anqi ZHANG, Xingran XIONG


Headline Buster is a convergence media program that analyzes media headlines on China-related issues and tries to set the record straight in case of misrepresentation of facts. It is the flagship show of The Point with LIU Xin, an opinion program on current affairs on China Global Television Network (CGTN). It covers global news events, especially those related to China, through the views of scientists, scholars, senior officials, political scientists and others. LIU Xin, the host, has two decades of experience as a news anchor and overseas correspondent.

Project description:

The program analyzes what triggered the anti-China sentiments when the Covid-19 pandemic started. The conclusion is it was a mix of politics and media reports that the virus leaked from a Chinese lab. The data collected from media reports found the “lab leak” theory spiked due to two primary reasons: then US President Donald Trump promoting it despite no credible evidence, and an “exclusive” report by the Wall Street Journal, quoting a supposedly secret state intelligence source that was later found inconclusive. The program analyzes how the media set an agenda-driven narrative. 

Impact reached:

The program was viewed 3.7 million times in five days on TV and social media platforms.  

The data analysis provided an objective argument, showing another side of the Covid-19 story that the media has ignored to pursue a sensational conspiracy theory. One fallout of such reports was attacks on the Chinese and other Asian communities in the West. There were efforts at a smear campaign against China, with the Covid-19 virus dubbed the “China virus”.

Our program reminded people that despite Trump’s assertion that he had “credible evidence” the virus came from a lab in Wuhan city, there hasn’t been a shred of evidence. His successor President Joe Biden ordered a 90-day investigation by intelligence agencies and that too drew a blank. The intelligence report supposedly quoted by the Wall Street Journal for its sensational article also admits there is no evidence to support the lab leak theory.

The comments on our program show viewers understood the manipulation by the media and realized the difference between evidence and speculation. It created a platform for rational thinking where people looked at other pandemics and possible sources that haven’t been investigated but ought to be. The biggest impact was it made people think for themselves.

Interestingly, since then, the lab leak theory has not resurfaced. We feel our program, along with other responsible media, has a role in laying that fake theory to rest. Also, we are happy to note that when new variants of the Covid-19 virus resurfaced, no one called them after the countries where they were first reported. Again, along with other media, we think we have played a role, however small, in making people become aware of the importance of following science and rationality.

Techniques/technologies used:

We chose five leading outlets dailies for a word frequency analysis. We determined three keywords: Wuhan, China and lab, and checked how many major articles these five had carried, using those words, between December 2019, when the first Covid-19 cases were reported, and August 2020, when our program was livestreamed.

The search shortlisted 238 articles; there were more but we kept Opinion articles outside the ambit. Our data analyst used different codes to scan the five websites. We then used our exclusive Natural Language Toolkit developed for Headline Buster to draw up a list of high-frequency words.

To better understand the connection between the contents of the reports, the pandemic, and public opinion, we also added the dimension of time to the analysis, meaning the dates when the articles were published.

In addition, we calculated the degrees of similarity between words based on their vectors. For this we used the Word2vec model, an algorithm using a neural network model to assess word associations from a large corpus of text.

We used a graph based on the frequency of two words, “lab” and “Trump”. Interestingly, it shows both peaked about the same time, which is a pointer that both were used in conjunction. The most possible scenario was that Trump floated the lab leak theory and the media reported it, attributing it to him.

What was the hardest part of this project?

The most challenging part was convincing our audience that the anti-China accusations were started by people who were not scientists, there was no proof to support what they said, and yet people tended to believe in such damaging conspiracy theories at a time when judgment was easily swayed by fear, vulnerability and loss of security.

Since CGTN is a state-funded Chinese medium, it is often regarded as a tool for propaganda. But numbers and well-established data have a greater acceptability. In recent times, we have often seen how the Western media created false narratives, wittingly or due to vested interests. While Western readers are growing more critical of the media when it comes to domestic news, when it comes to China, they still tend to be swayed, especially when the media narrative is sensational. We have tried to show how bias can produce an entirely false perspective, which is then regarded as the truth, a trend that history has shown is exceedingly dangerous.

What can others learn from this project?

The key takeaway for journalists is to explore how to formulate counter-arguments when only one-sided or even distorted narratives dominate, and do it rationally and persuasively against great odds.

Project links: