In this final blog post I will attempt to conduct some comparative analysis between subreddit groups and pre- and post-pandemic years. As a reminder my research questions are:
Research Questions
* Did conversations of faith and religion strengthen, weaken, or remain the same among the faith subreddit groups: r/Christianity, r/Islam, and r/Judaism, during the pandemic years of 2020-2021 as compared to 2019?
* How did faith and religion permeate discussions of covid-19?
For more details on how documents are dispersed between groups and years. I looked at the following tables.
CHRISTIANITY COVID19 ISLAM JUDAISM
791 946 866 878
2019 2020 2021
448 1759 1274 
The first table shows the distribution of top posts by subreddit group across all three years, 2019 -2021. The second table is the total thread distribution by year. Finally, the last plot shows the distribution by subreddit by year.
Correlated Topic Model
Topic 1 Top Words:
Highest Prob: just, pray, please, thank, time, can, people
FREX: asking, surgery, tomorrow, shahada, process, dog, pornography
Lift: -1, -become, -built, -chanted, -finally, -gone, -had
Score: thank, pray, prayers, please, porn, just, post
Topic 2 Top Words:
Highest Prob: muslim, one, day, first, today, mosque, us
FREX: eid, art, mum, concentration, painted, risen, easter
Lift: -22, -6, -niv, -strength-love_, #3, #4, #5
Score: mum, muslim, mosque, forty, day, today, killed
Topic 3 Top Words:
Highest Prob: jewish, jews, people, us, can, like, israel
FREX: racism, shalom, racist, antisemitic, rabbi, me_, anti-semitism
Lift: legislation, mankind, onset, racial, represent, -dr, #_
Score: jews, jewish, israel, nbsp, racism, shalom, https://ncov2019.live/data
Topic 4 Top Words:
Highest Prob: god, love, jesus, like, life, just, church
FREX: atheism, lgbt, loves, internet, drew, jesus, started
Lift: -kobe, -matthew, -with, #1-, _and, _awhile, _couldnt
Score: god, love, felt, jesus, started, church, really
Topic 5 Top Words:
Highest Prob: covid-19, sars-cov-2, vaccine, coronavirus, patients, covid-19_, study
FREX: covid-19, sars-cov-2, vaccine, covid-19_, infection, clinical, vitamin
Lift: 2019_, 2020_, activity, admission, agency, agreement, analysis_
Score: covid-19, sars-cov-2, vaccine, covid-19_, vitamin, clinical, antibody Here we can see similar terms from the previous post’s word clouds and key terms for applying dictionary approaches in context.
Plot Examples
Plot of Topic Model by Prevalence
Plot of Labels
Plot of Two Perspectives- Topics 1 and 5
In the plot above, “covid” is by far the most dominant term between the two topics, followed by other covid-related themes. Interestingly, terms in Topics 1 and 5 are somewhat grouped together while “covid” specifically is isolated from the groupings.
Topic Names and Labels
The following is a list of topic names based on the score values from the topics above.
[1] "thank_pray_prayers_please"
[2] "mum_muslim_mosque_forty"
[3] "jews_jewish_israel_nbsp"
[4] "god_love_felt_jesus"
[5] "covid-19_sars-cov-2_vaccine_covid-19_"Here we can see there is possibly a topic relevant to each of the various subreddits groups: #2 - r/Islam, #3 - r/Judaism, #4 - r/Christianity, and #5- r/Covid19. The first topic could be for religious topics across the three faith groups.
Plot Topic Loess
Here I assume the pink lines arching upwards could be topics related to covid-19 since there was a great increase in threads related to the topic during 2020 and 2021. Similarly the other faith based topics may have had a continuous presence in the threads appearing somewhat flat in topic proportions. Note, a legend could easily solve this uncertainty; however, I had difficulty adding one to this plot.
Plotting Estimate of Effect
The following plots returns the estimated topic prevalence by year and topic name.
Lastly, this graphs above show the expected topic proportion over time. Most topics with the exceptions of topic #5 on covid and topic # 3 on Judaism see a decline over time.