Covid: Analysis, Reflection, and Moving Forward

7 May

5/4/22

Welcome back to the Odyssey! My exams are finally over, so I will start posting to the blog more regularly again. Today, I want to go a bit more in depth about the results I obtained from my Covid, how the project went overall, what I learned, and how I will be applying these skills to my research on the German elections.

Reference Information

In order to evaluate the model’s findings I need to check how the hypothesized changes in perception correlate with real world events. It will be interesting to see what events had an impact on the perception of Covid related issues and how strong these impacts were. I am using the vaccination rates and Covid cases over the span of the pandemic as well as a general timeline of major Covid related events in Germany. Ideally, I would have conducted a survey of the German population to check the accuracy/quality of the model’s findings, however since I do not have the resources to do that I will be relying on KhudaBukhsh’s findings (his model was successful in tracking community perception).

Covid Cases and Vaccinations

Timeline

March 8, 2020: first death linked to COVID-19 is registered and the virus is reported in all of Germany's 16 federal states
March 2020: The German government issued worldwide travel warnings, and borders are closed to people from non-EU countries
March 22, 2020: first partial lockdown, international travel restrictions, and remote working begins. Reaction: praises from Germans and acceptance of restrictions.
April 2020: economy stagnates, 156 billion euro relief package is created, and panic buying ensues
May 4, 2020: First lockdown is over after 7 weeks
June, 2020: “Querdenker” (“lateral thinker”) movement gets stronger. They protest against the remaining restrictions which they claim are an infrigement of their fundamental civil liberties
August, 2020: police in Berlin have to break up a demonstration comprised of nearly 40,000 Querdenkers and far-right militant group (Reichsbürgers) members
End of August, 2020: Second wave starts to begin. 1,000 cases a day in August and 20,000 cases a day in September
October, 2020: Berlin imposes a curfew
November 2, 2020: Another Lockdown begins. Meetings in public are limited to two households and a maximum of 10 people. Many businesses in the catering, hospitality and tourism sectors again have to close down, as they had in spring
January, 2020: Vaccine deployment begins but is behind schedule and there are many logistical challenges. Germany no longer stands as a model for how to combat the virus.
January 6, 2021: Third wage begins and a strict lockdown is enacted
April 2021: Germany reaches 80,000 deaths. A reform of the Infection Protection Act in late April increased federal government powers, allowing it to mandate pandemic measures in hard-hit districts
May: The lockdown starts having an impact and infections fall. Third wave is broken. Merkel has her first vaccine dose.
June 2021: The government promises that by the autumn all German residents will have had the opportunity to be vaccinated
July 2021: While many areas of Germany roll back restrictions amid low COVID-19 case numbers, the country's incidence rate began to rise steadily again due to the delta variant
November 2021: Free testing is re-introduced due to a high number of cases after it had been phased out a month earlier.
December 2021 and January 2022: Cases rise drastically due to the omicron variant.

Sources:

https://www.dw.com/en/covid-how-germany-battles-the-pandemic-a-chronology/a-58026877

https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Germany

Successful Queries

Overall Opinion on Covid

Title: “I think that Covid is [MASK].”
Labels: over, terrible, to blame, gone, harmless (left to right)

What I found the most interesting about this query is that the main concern among Germans isn’t necessarily the danger or impact of Covid, rather it is by and large whether the pandemic is over or not. My personal hypothesis is that because Germany’s medical infrastructure was able to more or less handle the pandemic, the biggest worry for citizens is how the virus affects their day to day lives (lockdowns, virtual working, etc.). It would be fascinating to extend my research by comparing Germany’s response with another country that has a higher deaths per capita to see if words like ‘dangerous’ and ‘life-threatening’ are more prominent. Comparing my findings with new Covid cases per month in Germany highlights the short term mindset and optimism bias in German citizens as spikes in case numbers are directly correlated with a decrease in the perception that Covid is “over” while low Covid case numbers are directly correlated with spikes in confidence that Covid is over.

Opinions on Lockdowns

Title: “I think that this lockdown is [MASK].”
Labels: good, important, over, wrong, shit (left to right)

Like in the Covid opinion query, the main concern for Germans regarding lockdowns is when they will be over, and this opinion is heavily correlated with number of cases and the timeframe of the lockdowns enacted in Germany. “Lockdown” was not one of the keywords that I used to scrape tweets and hence the success of this query highlights the strength of the model to understand concepts that are directly related to the topic at hand but not explicitly in the scope of the research. It is also interesting to note that although the positive and negative opinions (“good”, “important”, “wrong”, and “shit”) of the lockdown were similar in strength throughout the pandemic, the opinion that the lockdown is “wrong” was stronger than the opinion the lockdown is “good”, indicating that the German population doesn’t believe that the benefits of the lockdown outweigh its costs.

Opinions on Masks

Title: “I [MASK] Masks.”
Labels: need, hate, wear, love, like (from left to right)

Germans’ opinions on masks had the clearest long term trends. Disregarding fluctuations, Germans became more accustomed to wearing masks over the pandemic. At the same time, the both the dislike for and favorable opinion of masks decreased the longer the pandemic went on. This illustrates that over time, mask wearing became the new normal.

Opinions on the “Querdenker” Movement

Title: “I think that the ‘Querdenker’ are [MASK].”
Labels: important, stupid, good, at fault, not important

The model shows that there is a clear negative perception of the Querdenkers throughout the pandemic, with initial support in the beginning of Covid waning and eventually dying out. It is also seems as though the omicron variant put a major strain on the movement with German’s blaming the current situation (increase in cases) on them.

Opinions on the vaccinated and unvaccinated

Title: “The unvaccinated are [MASK].”
Labels: at fault, querdenker, vaccinated, stupid, contagious

Title: “The vaccinated are [MASK].”
Labels: immune, at fault, stupid, dead, contagious

The perception of the vaccinated and unvaccinated didn’t have any noticeable long term trends, however the second lockdown (enacted in November) coupled with logistical challenges in vaccine deployment led to the unvaccinated being blamed for the current situation. Additionally, the beginning of the vaccine rollout was greeted with a lot of support as the vaccinated were seen as immune from Covid, however this perception went away over time. In addition, it seems as though the omicron variant had a severe polarizing effect on vaccine perception as both the unvaccinated and vaccinated were seen to be at fault for the rise in cases. It is also important to note that the unvaccinated were associated with the Querdenker movement throughout the pandemic, illustrating the bias and social stigma attached to choosing to be unvaccinated.

overall notes: i don’t think going month by month was a good idea since opinoins esspecially online, change very quickly. It would be interesting to test one week intervals because they might also provide a much smoother graph. this would require a lot mreo training though

I thought that I would see more long term trends regarding opions towrads covid, but is seems as though people are much more sensitive to the short term. The model is also much more sensitive to the hrot term as people will only speak what is currently on their mind. It would be interesting to see whether a different approach would be able to pick up longer term trends (maybe each model just gets newer information, as in the majority of the data is from the previous months but then new information (new tweets) get asdded for each month. sort of a moving average. would be interesting to play around with how the wieghting works (how strong is it)

Unsuccessful Queries, Overall Takeaways, and Moving Forward

Although a lot of my queries led to very interesting analysis, I was a bit disappointed by the overall quality of the findings. For the queries below there seems to be very little structure indicative of changes in perception and it seems more like very volatile noise. I think that a part of this problem is that I am tracking the change by month, a timeframe over which people’s mindsets can drastically change. Since the trends that I have been able to observe are much more meso/micro trends rather than long term macro trends, analyzing the tweets week by week makes a lot more sense. In addition, I should have realized that opinions are very reactionary, especially on a social media platform like twitter, and thus my research is much more likely to be able to illustrate how the German population perceives certain policies/events (mask mandates, lockdowns, travel restrictions being enacted, etc.) rather than long term changes in the perception of concepts related to covid.

In the queries below the major problem is that the confidence of the model’s outputs are far too low and thus it doesn’t make sense to make any inferences from the data. This low confidence is indicative of the fact that the model didn’t fine tune well to these concepts, which is most clearly demonstrated in “Spaziergang” query, where the token most likely to follow the query is a period. Although the query “The biggest problem in Germany is [MASK]” has a high confidence and shows long term trends, the query itself is an issue as all the data is about covid and thus the model will obviously show “covid” as the biggest problem in Germany.

This issue is the tip of the iceberg of a much deeper problem. One of the main reasons for analyzing the perception of Covid related topics is to be able to compare how Covid is perceived in the general public discourse and how it relates in importance to other current issues of the time. However, by training the model solely on data specifically about covid, I lose this perspective. Essentially, I will always be able to find enough data to train the model, however the actual amount of data there is indicative of how important the issue is, and thus by not taking that into account I am unable to perform an accurate analysis.

Originally, I had planned to move on to the Election research about Annalena Baerbock. However, I feel like there is a lot more potential in this project and I wouldn’t really be doing it any justice if I leave it how it is. The plan as it currently stands is to scrape new data from Facebook, Twitter, and/or Youtube using the comments of news media posts rather than a key-word approach. A model will then be trained for every week since the start of the pandemic (I will also be doing this with the data that I currently have to see if it improves results). I will also try to gain more insight about long term trends by using a sort of moving average approach, where I finetune one model on the first timestep of data and make predictions with this model, then keep finetuning the model on the next timestep of data, and so forth. I think this will yield more stable results and produce a model that behaves more like a human brain in that previous opinions are still taken into account but are less significant than new opinions.

Luckily, my exams are over which means I have a lot more time on my hands and will be posting a lot more regularly. Stay tuned!