Pandemic #Edchat: UK vs US
K. Bret Staudt Willet
July 29, 2020
Looking at #Edchat tweets during COVID-19 (February-May 2020)
We started by collecting #Edchat tweets between February 1 and May 31, 2020. Data from March 1-May 31 were already collected and cleaned, leaving only tweets from February 2020. These were collected with two additional TAGS trackers:
1/28/20 - 2/20/20 (65,872 tweets)
2/20/20 - 4/17/20 (182,555 tweets)
We remove duplicate tweets and then get additional tweet metadata with lookup_many_tweets() from the {tidytags} package.
This leaves us with 338523 tweets and 100 associated variables for each tweet.
With precisely defined start (2020-02-01) and end (2020-05-31 23:59:59) times, we now have 331538 tweets remaining.
Of these 331538 total tweets, there are 118163 original tweets (i.e., not retweets), or 35.64% of #Edchat tweets between March 1 and May 31, 2020. 12781 different users contributed these original tweets.
Of the 118163 original tweets, 16787 (14.21%) contained a question.
Daily tweets
Location of tweeters
We used the {mapsapi} package to query the Google Maps API and retrieve map placements for the locations that #Edchat tweeters listed in their profiles. This table shows the top-10 countries in terms of number of distinct locations identified.
## country n
## 1 USA 2821
## 2 Canada 363
## 3 UK 360
## 4 Australia 119
## 5 India 73
## 6 Ireland 47
## 7 New Zealand 37
## 8 Germany 26
## 9 Spain 26
## 10 Philippines 23From these locations, we identified the self-identified country of tweeters. The following table shows the number of #Edchat tweets from tweeters, binned by the country of tweeters.
## country n
## 1 USA 73859
## 2 UK 8111
## 3 Canada 6220
## 4 Australia 2432
## 5 Spain 1194
## 6 India 981
## 7 New Zealand 595
## 8 Ireland 577
## 9 China 390
## 10 Colombia 300Daily tweets comparison: UK vs. US
We found that there were 8111 original tweets from UK tweeters and 73859 original tweets from US tweeters during COVID-19, February 1-May 31, 2020.
Most common hashtags in the UK vs. US
In this study, we are interested in the topics of conversation happening in the UK vs. US during the COVID-19 pandemic. First, we compare the top-20 hashtags in the United Kingdom, along with their corresponding frequency in the United States.
## # A tibble: 16 x 8
## hashtag n_UK p_UK odds_UK n_US p_US odds_US log_odds_ratio
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 #sltchat 749 9.23 0.102 18 0.02 0.000244 6.03
## 2 #metacognition 467 5.76 0.0611 12 0.02 0.000162 5.93
## 3 #ukedchat 1860 22.9 0.298 147 0.2 0.00199 5.01
## 4 #pedagogy 468 5.77 0.0612 34 0.05 0.000461 4.89
## 5 #remotelearning 112 1.38 0.0140 6268 8.49 0.0927 -1.89
## 6 #distancelearning 66 0.81 0.00820 3757 5.09 0.0536 -1.88
## 7 #leadership 284 3.5 0.0363 531 0.72 0.00724 1.61
## 8 #edutwitter 1031 12.7 0.146 2127 2.88 0.0297 1.59
## 9 #teacher 427 5.26 0.0556 988 1.34 0.0136 1.41
## 10 #k12 268 3.3 0.0342 5578 7.55 0.0817 -0.872
## 11 #covid19 149 1.84 0.0187 3026 4.1 0.0427 -0.825
## 12 #edtech 1061 13.1 0.150 17595 23.8 0.313 -0.731
## 13 #teachers 598 7.37 0.0796 3102 4.2 0.0438 0.596
## 14 #learning 250 3.08 0.0318 1293 1.75 0.0178 0.579
## 15 #teaching 489 6.03 0.0642 2712 3.67 0.0381 0.521
## 16 #education 964 11.9 0.135 11016 14.9 0.175 -0.262
Word frequencies in the UK vs. US
First, we plotted the relative frequency of words tweeted by UK users compared to US tweeters.
Word frequencies in the UK vs. US
First, here is a list of 20 words that occur with very similar frequencies in tweets from UK and US users, according to the calculated log odds ratio for each word.
## # A tibble: 20 x 4
## word USA UK logratio
## <chr> <dbl> <dbl> <dbl>
## 1 strategy 0.000251 0.000251 0.000694
## 2 #mypchat 0.0000837 0.0000838 0.000694
## 3 cuts 0.0000837 0.0000838 0.000694
## 4 incredibly 0.0000837 0.0000838 0.000694
## 5 priorities 0.0000837 0.0000838 0.000694
## 6 setting 0.000228 0.000227 -0.000787
## 7 question 0.000682 0.000682 0.000941
## 8 #wellness 0.0000719 0.0000718 -0.00165
## 9 encourage 0.000276 0.000275 -0.00236
## 10 development 0.000540 0.000539 -0.00274
## 11 steam 0.000107 0.000108 0.00383
## 12 variety 0.000107 0.000108 0.00383
## 13 level 0.000465 0.000467 0.00467
## 14 #criticalthinking 0.0000601 0.0000598 -0.00492
## 15 tutorials 0.0000601 0.0000598 -0.00492
## 16 7pm 0.000119 0.000120 0.00493
## 17 event 0.000369 0.000371 0.00525
## 18 examples 0.000169 0.000168 -0.00632
## 19 message 0.000297 0.000299 0.00691
## 20 comment 0.000108 0.000108 -0.00710Second, here is a visualization of 20 words that occur with very disparate frequencies in tweets from UK and US users, according to the calculated log odds ratio for each word.
Trends in word usage
We are comparing many slopes here and some of them are not statistically significant, so let’s apply an adjustment to the p-values for multiple comparisons.
Now let’s find the most important slopes. Which words have changed in frequency at a moderately significant level in our tweets
Word trends by UK tweeters
Word trends by US tweeters
Words and topics in the UK vs. US
UK words
US words
UK topics
US topics
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
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## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2020-07-30
##
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