Comments were collected mid May 2015 using the PRAW Python library, by downloading all comments for the latest one thousand topics available (Reddit limit) for /r/belgium, /r/Denmark, /r/germany, /r/italy, /r/Switzerland and /r/thenetherlands.
I did not really validate the date, results, etc. so interpret at your own risk.
library(plyr) # for mapvalues()
library(ggplot2)
library(ineq)comments.raw <- read.csv('/home/mhermans/comments_full.csv')
comments <- comments.raw
comments$X <- NULL # drop useless row id nrs
#length(comments$comment_id)
#length(unique(comments$comment_id)) # duplicates? accidental re-crawl?
comments <- comments[!duplicated(comments$comment_id),] # drop dpublicates
comments$date <- as.Date(as.POSIXct(comments$date, origin="1970-01-01")) # epoch time to Date object
comments$negative <- ifelse(comments$score <= 0, 1, 0) #
head(comments)## comment_id comment_len date gilded score subreddit_url
## 1 cq7lgbs 264 2015-04-10 0 24 /r/belgium/
## 2 cqidwu0 534 2015-04-20 0 3 /r/belgium/
## 3 cr05xwu 92 2015-05-06 0 1 /r/belgium/
## 4 cq7lhcx 120 2015-04-10 0 3 /r/belgium/
## 5 cq7mig8 31 2015-04-10 0 10 /r/belgium/
## 6 cqidxlm 46 2015-04-20 0 5 /r/belgium/
## user_name negative
## 1 Chimiel82 0
## 2 dj-shortcut 0
## 3 Ask_The_Dust 0
## 4 inxi 0
## 5 SK2P1 0
## 6 dj-shortcut 0users <- as.data.frame.matrix(table(comments$user_name, comments$subreddit_url))
subreddits <- colnames(users)
users$top_subreddit <- apply(users, 1, which.max)
users$top_subreddit <- mapvalues(users$top_subreddit, 1:6, subreddits)
users$comments_total <- rowSums(users[,subreddits])
users$user_name <- rownames(users)
rownames(users) <- NULL
downvotes_user <- aggregate(
negative ~ user_name,
data=comments, sum)
users <- merge(users, downvotes_user, by='user_name')
rm(downvotes_user)
users$comments_neg_frq <- users$negative
users$negative <- NULL
users$comments_neg_prop <- users$comments_neg_frq / users$comments_total
meanscore_user <- aggregate(comments[,c('score', 'comment_len')], list(comments$user_name), mean)
names(meanscore_user) <- c('user_name', 'comment_score_mean', 'comment_len_mean')
users <- merge(users, meanscore_user, by='user_name')
rm(meanscore_user)
users$comments_neg_prop_cat <- cut(users$comments_neg_prop, c(-1, 0,.1, 0.25, .5, 0.75, 1))
head(users)## user_name /r/belgium/ /r/Denmark/ /r/germany/ /r/italy/
## 1 ______-__-______ 0 0 3 0
## 2 ---______________-- 0 0 3 0
## 3 011101010111001101 14 0 0 0
## 4 01314150 0 35 0 0
## 5 01938 0 0 4 0
## 6 02S 0 0 0 0
## /r/Switzerland/ /r/thenetherlands/ top_subreddit comments_total
## 1 0 0 /r/germany/ 3
## 2 0 0 /r/germany/ 3
## 3 0 0 /r/belgium/ 14
## 4 0 0 /r/Denmark/ 35
## 5 0 0 /r/germany/ 4
## 6 0 2 /r/thenetherlands/ 2
## comments_neg_frq comments_neg_prop comment_score_mean comment_len_mean
## 1 0 0.0000 3.000 499.7
## 2 0 0.0000 3.000 263.0
## 3 0 0.0000 1.286 183.8
## 4 4 0.1143 2.029 303.8
## 5 1 0.2500 31.250 332.2
## 6 0 0.0000 6.500 125.5
## comments_neg_prop_cat
## 1 (-1,0]
## 2 (-1,0]
## 3 (-1,0]
## 4 (0.1,0.25]
## 5 (0.1,0.25]
## 6 (-1,0]nrow(comments) # total nr. of unique comments## [1] 127772summary(comments)## comment_id comment_len date gilded
## cjsli6d: 1 Min. : 0 Min. :2014-08-17 Min. :0e+00
## cjsmafs: 1 1st Qu.: 55 1st Qu.:2015-03-29 1st Qu.:0e+00
## cjsq5x7: 1 Median : 122 Median :2015-04-21 Median :0e+00
## cjsuhpd: 1 Mean : 229 Mean :2015-04-04 Mean :4e-04
## cjswgr0: 1 3rd Qu.: 262 3rd Qu.:2015-05-04 3rd Qu.:0e+00
## cjsy2pg: 1 Max. :9994 Max. :2015-05-15 Max. :2e+00
## (Other):127766
## score subreddit_url user_name
## Min. :-122.00 /r/belgium/ :25852 [deleted] : 2295
## 1st Qu.: 1.00 /r/Denmark/ :19765 Urgullibl : 1171
## Median : 2.00 /r/germany/ :15630 historicusXIII: 1024
## Mean : 3.53 /r/italy/ :20939 JebusGobson : 685
## 3rd Qu.: 4.00 /r/Switzerland/ :15675 Lucky-o-jelly : 659
## Max. : 251.00 /r/thenetherlands/:29911 Inquatitis : 584
## (Other) :121354
## negative
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0948
## 3rd Qu.:0.0000
## Max. :1.0000
## #table(comments$subreddit_url) # nr. of comments per country subreddit
summary(comments$date) # scrape date-range## Min. 1st Qu. Median Mean 3rd Qu.
## "2014-08-17" "2015-03-29" "2015-04-21" "2015-04-04" "2015-05-04"
## Max.
## "2015-05-15"# mean values per country subreddit
aggregate(
comments[,c('date', 'comment_len', 'score', 'gilded')],
list(subreddit=comments$subreddit_url), mean)## subreddit date comment_len score gilded
## 1 /r/belgium/ 2015-03-26 220.7 2.523 0.0006576
## 2 /r/Denmark/ 2015-04-28 229.4 3.434 0.0005059
## 3 /r/germany/ 2015-04-10 272.0 3.893 0.0001280
## 4 /r/italy/ 2015-05-02 210.9 3.596 0.0003343
## 5 /r/Switzerland/ 2015-01-01 241.3 2.943 0.0002552
## 6 /r/thenetherlands/ 2015-04-21 220.0 4.547 0.0002675
3 Comment downvote patterns
Comments are coded as “negative”, is score is 0 or lower (Reddit stopped returning raw comment scores?).
3.1 Occurence of comment downvotes
3.2 Comment length and downvotes
General expectation is a negative relation between downvotes and comment lenght. Short, low-effort comments are (in principle) more likely to be downvoted, long comments indicate effort/contributions and are (in principle) more likely to be upvoted. A smaller difference between the average lenght of downvoted and non-downvoted comments might indicated “downvotes for disagreement”.
3.3 Downvote concentration within users
We can calculate a Gini-coefficient for the number of downvoted comments in a country subreddit. A value closer to 1 means that the downvoted comments are concentrated with one or a few users, most likely trolls. Lower values might indicate a more indiscriminate downvoting pattern, e.g. “downvotes for opinion”.