The issue of the Coronavirus has rapidly seeped into the minds of the general public over the course of the last month. The spread of the virus, which is said to have originated in a Chinese seafood and poultry market in December 2019, has been fast and large – the virus has managed to spread to 24 countries so far and the number of fatalities has climbed over 1000 (Taylor, 2020). The virus poses a big health concern, hence, multiple countries have, for instance, closed down borders with China as well as some have banned all flights arriving from the country.
Additionally, the people thought to be potentially affected by the virus have been placed in temporary quarantines. One of the major problems surrounding the news coverage of this epidemic has been how different media outlets choose to frame the issue in their reporting. Since it is a highly discussed topic, it has been often abused in order to push forward other seemingly unrelated news. Many news sources seem to also fail at basing their statements on scientific research, and by doing so they contribute to the unnecessary spreading of panic. YouTube’s recommendation algorithm can be, upon the initial analysis of the videos related to the Coronavirus outbreak published on the platform, viewed as a contributing factor to the spreading of false information as well. The algorithm’s tendency to recommend videos on the basis of their titles, the information provided in the subscription box, and the gathered number of views (Madrigal, 2018) plays in favor of those YouTube channels that opt to frame the issue rather controversially.
The data was retrieved using the YouTube Data Tools ‘Video Network’ module, which creates a network of relations between videos that was further analysed and visualised through a tool known as ‘Gephi’ to reflect the pattern of audience viewing preferences’. The data collection procedure was applied to a selected keyword query: ‘Coronavirus’, and was captured on the 17th of February 2020. The approach to our investigation was a combination of video network analysis and content analysis – this method of using different tools enables the study of large scale digital networks, such as Youtube’s recommendation system. Using network analysis we investigate the patterns and relationships of the social environment surrounding videos related to Coronavirus. Moreover, in order to analyze the content of the network, videos were categorized based upon their title relevance to the topic. Overall, the network had 1867 nodes and 45995 connections (which were further filtered down to 531 nodes and 18364 edges in order to increase the readability).
The generated network consisting of 531 nodes and five communities illustrates that the most recommended videos have a threatening and negative character containing titles like: ‘New Research suggests The Coronavirus May be Far Worse Than We Thought’ and ‘Is The Coronavirus Now Unstoppable? New Data Suggests So.’ The authors of such videos are mostly dogmatic and commentary – like, resulting in unreliable and fallacious information, and lack of scientific evidence. A channel called PeakProsperity, previously focusing on finance and crash courses, has produced over 12 videos covering the coronavirus, containing various conspiracies and “updates” on the virus, generating over 200.000 views each.
Moreover, one of the most recommended videos is called ‘Pangolin facing greater threat of extinction during coronavirus outbreak’ by CBS Evening New. It is further connected to content relating to Trump’s impeachment and numerous conspiracies. Additionally, some of the most recommended content is linked to ‘extreme’ Chinese street food and racism connected to the coronavirus outbreak. Other recommendations are solely based on conspiracies about patients breaking out of the quarantine and China losing control over the virus. Evidentially, Youtube’s recommendation system seemingly contributes to spreading fear rather than keeping the public informed.
It appears from the media’s coverage of the coronavirus the epidemic is right at our front doorstep. Due to the coronavirus being a current issue, statistics are being updated constantly, as there is a cumulative impact of statistics which tell various stories both through official statistics and unofficial statistics (predictions). Yet, sensational headlines in different forms of media across the field, in particular Youtube, have grabbed attention in a frightening and unnecessary way. To gain insight into the global worldview and trends of the coronavirus, Google Trends is a good way of looking into this. These statistics are based on Google users’ search behavior, which, even though may be limited to only google users, still holds its validity as it is a prominent internet search engine. More specifically, Google Trends offers to look at the development of the overall search interest on the issue over time as well as it gives the option to look at the interest rate of the Google searches depending on specific geographical locations. As Google Trends elaborates on the data formulation:
Values are calculated on a scale from 0 to 100, where 100 is the location with the most popularity as a fraction of total searches in that location, a value of 50 indicates a location which is half as popular. A value of 0 indicates a location where there was not enough data for this term.
Considering that both South Korea and Italy have been the primary locations of “recent spikes in the number of infections” (Elflein, 2020), it is then understandable that the data overview on Google Trends showcases the region interest to be the highest in Italy (100), followed by Singapore (80) and Luxembourg (40).
In order to get a professional opinion regarding the media coverage of coronavirus, we hope that Dr. Christopher Pell will agree to participate in the interview. His background in qualitative social and behavioural sciences as well as medical anthropology is of great value for our research as he can provide us with a better insight into how the social context affects the implementation of health interventions (in this case coronavirus). Dr. Pell has already expressed his opinion regarding media reporting of the virus, stating that “experts have difficulty with the rapidly developing circumstances and knowledge surrounding the outbreak. It is therefore not surprising that journalists also struggle with this” (University of Amsterdam, 2020).
During the interview, we are planning to ask the following questions:
How does the Dutch news media outlets cover the outbreak of the coronavirus? Would you say it differs from international media outlets? If so, how?
How should this issue be reported correctly in your opinion? What are the most common challenges/mistakes made by the media outlets?
What role does YouTube play in the coverage of the virus? Does it have a negative or positive effect on the spread of misinformation?
The findings of our research have demonstrated that the majority of video titles contain words with negative connotations. To what extent do media outlets use coronavirus as clickbait by using negative titles?
What could be done to prevent such spread of misinformation and can the public really trust mainstream media?
The preliminary findings of our investigation seem to have proven that one of the major problems surrounding the media coverage of this epidemic has been the way the issue is portrayed by media outlets. The lack of scientific evidence has left the room for unreliable and speculative information to continuously spread online. ‘Youtubers’ and other independent video content publishers are jumping on the bandwagon of using the coronavirus as clickbait for their own personal good (such as generating more views), thus increasing their personal income. Youtube’s recommendation algorithm plays a huge part in shaping the user’s interaction with the content surrounding the current issue, thus Youtube should take actionable steps in order to cut down possible misinformation on their platform.
Amsterdam, Universiteit van. 2020. ‘UvA Scientists Explain the Implications of the Coronavirus – University of Amsterdam’. https://www.uva.nl/en/content/news/press-releases/2020/03/uva-scientists-explain-the-implications-of-the-coronavirus.html.
Elflein, John. “Coronavirus (COVID-19) disease outbreak – Statistics & Facts.” Statista, 2 Mar. 2020,
Google Trends. n.d. https://trends.google.com/trends/explore?q=Coronavirus
Madrigal, Alexis C.. “How YouTube’s Algorithm Really Works.” The Atlantic, 8 Nov. 2018, https://www.theatlantic.com/technology/archive/2018/11/how-youtubes-algorithm-really-works/575212/
Taylor, Derrick B. “A Timeline of the Coronavirus.” The New York Times , 13 Feb. 2020, www.nytimes.com/2020/02/13/world/coronavirus-timeline.html.