Scope of Analysis
This analysis is a follow-on to prior work that examined text and emoji in several Categories (tags). This analysis differs in that we are focusing on the most loved posts (most Up Voted) and the least loved posts (most Flagged or Down Voted).
We will be considering data across all categories for the month of December 2017. This will include foreign languages and character sets.
Tools
This analysis is done with R and RStudio on an ailing 10 year old MacBook.
Extracting Data
We extract our data from Steemdata.com with this mongodb query and bring our data into the R analysis platform.
Extract Raw Posts
mdb <- mongo(collection="Posts",db="SteemData",url="mongodb://steemit:[email protected]:27017/SteemData")
raw1<- mdb$find(query='{"created": {"$gte": {"$date": "2017-12-01T00:00:00.00Z"}, "$lte": {"$date": "2017-12-31T00:00:00.00Z"} } }', fields='{"_id":0, "body":1, "net_votes":1}')
Extract Post Categories
mdb <- mongo(collection="Posts",db="SteemData",url="mongodb://steemit:[email protected]:27017/SteemData")
cats<- paste('[{ "$match": {"created": {"$gte": {"$date": "2017-12-01T00:00:00.00Z"}, "$lte": {"$date": "2017-12-30T00:00:00.00Z"} } } },{ "$group": { "_id": { "category" : "$category" }, "Post Count" : {"$sum" : 1 } } }, { "$project": {"_id": 0, "category": "$_id.category" , "Post Count" : 1 } } , { "$sort": { "Post Count": -1 } }]', sep="")
categories<- mdb$aggregate(cats)
Extracting the raw Post data took 1042.627 seconds and returned 856,438 Posts from 36,170 categories by 61,228 distinct users.
We see the most up voted post belonged to @sweetsssj for her bi-lingual travel post. She has clearly invested heavily in well edited narrative and high quality, well taken photos. She also engages extensively with her followers via comments.
On the other end of the spectrum the most downvoted post belonged to @infoshare222 with this repost of a rather dark YouTube video about the Free Masons.
These two examples represent the far ends of the content spectrum.
Target Selection
Assessing the distribution of votes we can focus in on the top decile (90th percentile or top 10%) and bottom deciles (10th percentile or bottom 10%).
Standard Deviation: 49.69996
Quantiles:
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-57 0 1 2 3 4 6 9 15 34 2823
Summary Statistics:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-57.00 2.00 4.00 16.54 12.00 2823.00
Subsetting Good and Bad Posts
Next we will subset our data into two datasets. The Top dataset will include posts with more than 34 up votes each (90th percentile boundary). The Bottom dataset will include those with zero or negative votes.
top <- raw1[raw1[, "net_votes"] > 34, ]
bot <- raw1[raw1[, "net_votes"] < 0, ]
The Top dataset now include 83,799 Posts with more than 34 up votes. This dataset is approximately 234MB in size. The top 3 most prolific users by post count in this Top Most Up Voted Category are,
| User | Posts |
|---|---|
| @jout | 323 |
| @barrydutton | 320 |
| @tisantana | 288 |
The Bottom dataset now includes 18,540 Posts with zero or negative votes. This is approximately 67MB and one third the size of the Top dataset. The top 3 most prolific users by post count in this Bottom Most Down Voted Category are,
| User | Posts |
|---|---|
| @neocoinalert | 1309 |
| @rippleinfo | 1153 |
| @privacycoinsnews | 1150 |
These two dataset will now be loaded into a Corpus; a data structure designed specifically for text analysis.
Note: I am performing some minor data manipulation to make it a little easier to work with later in the processing pipeline.
# Top Corpus1
cps1 <- as.data.frame(top)
cps1$id <- seq.int(nrow(cps1))
colnames(cps1) <- c("text", "id")
cps1 <- cps1[c("id", "text")]
Corpus1 <- quanteda::corpus(cps1)
# Bot Corpus1
cps2 <- as.data.frame(bot)
cps2$id <- seq.int(nrow(cps2))
colnames(cps2) <- c("text", "id")
cps2 <- cps2[c("id", "text")]
Corpus2 <- quanteda::corpus(cps2)
A corpus is the best data structure to perform pre-processing and data cleansing. In this case I'm employing the Quanteda text mining package which is known to perform considerably faster on large Corpi, than the more common and better known tm package.
This first step will attempt to remove URLs and numbers.
Corpus1 <- rm_url(Corpus1, replacement = " ", extract=FALSE, trim=FALSE, clean=TRUE)
Corpus1 <- tm::removeNumbers(Corpus1, ucp = TRUE)
Corpus2 <- rm_url(Corpus2, replacement = " ", extract=FALSE, trim=FALSE, clean=TRUE)
Corpus2 <- tm::removeNumbers(Corpus2, ucp = TRUE)
This next will remove Stop Words and punctuation.
tic("Remove stop words and puctuation")
steem_stops <- c("steem", "steemit", "steemian", "steemians", "resteem", "upvote", "upvotes", "post", "SBD", "jpg", "td", "n", "s", "t", "re", "nbsp", "p", "li", "br", "strong", "quot", "img", "height", "width", "src", "center", "em", "html", "de", "href", "hr", "blockquote", "h1", "h2", "h3", "960", "720", "div", "en", "que","la", "will", "y", "el", "https", "http", "do", "does", "did", "has", "have", "had", "is", "am", "are", "was", "were", "be", "being", "been", "may", "must", "might", "should", "could", "would", "shall", "will", "can", "un", "get", "los", "di", "se", "also", "una", "h", "b", "alt", "_blank", tm::stopwords("en"))
dfm1 <- dfm(Corpus1, tolower=TRUE, stem=FALSE, remove=steem_stops, remove_punct=TRUE)
dfm2 <- dfm(Corpus2, tolower=TRUE, stem=FALSE, remove=steem_stops, remove_punct=TRUE)
topfeatures(dfm1)
topfeatures(dfm2)
tic()
With a corpus free of extraneous words and characters we can now attmpt a cluster analysis using the topicmodel package.
tic("Create LDA Topic Models")
library(topicmodels)
dfm1LDAFit<- LDA(convert(dfm1, to = "topicmodels"), k = 5)
get_terms(dfm1LDAFit, 10)
dfm2LDAFit <- LDA(convert(dfm2, to = "topicmodels"), k = 5)
get_terms(dfm2LDAFit, 10)
toc()
Word Clusters in Top Up Voted Posts
We will employ the Latent Dirichlet Allocation Model (LDA) to perform our cluster analysis.
Interestingly, the algorithm seems to be grouping words by language set. Topics 2 and 4 appear to be a mix of French and Spanish. Topic 3 contains oriental characters. I think they're Chinese and Korean. I'd suggest Top 5 is Indonesian.
| Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 |
|---|---|---|---|---|
| "one" | "para" | "的" | "e" | "yang" |
| "like" | "por" | "tr" | "che" | "dan" |
| "time" | "con" | "target" | "il" | "ini" |
| "just" | "es" | "content" | "per" | "saya" |
| "people" | "las" | "und" | "le" | "untuk" |
| "now" | "lo" | "die" | "non" | "dengan" |
| "new" | "del" | "我" | "è" | "dari" |
| "see" | "mi" | "の" | "si" | "kita" |
| "make" | "como" | "day" | "con" | "tidak" |
| "day" | "su" | "sub" | "da" | "и" |
Word Clusters in Bottom Down Voted Posts
These appear to be from news press releases that are often time sensitive (short shelf life). Topic 1 appears to be alt-coin and crypto centric. There may be some NSFW content in Topic 3.
| Topic 1 | Topic 2 | Topic 3 | Topic 4 | Topic 5 |
|---|---|---|---|---|
| "source" | "service" | "market" | "payout" | "class" |
| "target" | "source" | "twerk" | "value" | "news" |
| "full" | "twitter" | "hot" | "ago" | "alert" |
| "story" | "@mack-bot" | "girl" | "total" | "target" |
| "read" | "read" | "meme" | "created" | "source" |
| "twitter" | "full" | "report" | "current" | "read" |
| "bitcoin" | "story" | "sexy" | "posted" | "pull-right" |
| "news" | "target" | "research" | "hours" | "full" |
| "ripple" | "news" | "time" | "time" | "story" |
| "ethereum" | "never" | "funny" | "days" | "pull-left" |
Create Document Frequency Matrix
Finally, we can create a document frequency matrix and perfom some spimple word counting. The results are illustrated in the Conclusions section below.
tic("Create DFM & Word Freq")
## Doc Freq Matrix
dfm1.1 <- sort(colSums(dfm1), decreasing=TRUE)
dfm2.1 <- sort(colSums(dfm2), decreasing=TRUE)
dfm1.1.wf <- data.frame(word=names(dfm1.1), freq=dfm1.1)
dfm2.1.wf <- data.frame(word=names(dfm2.1), freq=dfm2.1)
head(dfm1.1.wf, 10)
head(dfm2.1.wf, 10)
toc()
Conclusions
Highest Up Voted Posts
These words appear to be naturally spoken, casual words (as opposed to busines or techical words). They include generally positive and collective verbs. There is a marked lack of text markup characters compared to the Down Voted Posts below.
Word Cloud Minimum Word Frequency : 5000
Lexical Dispersion Plot
A lexical dispersion plot allows us to visualize the occurrences of particular terms throughout a Post. These are sometimes referred to as “x-ray” plots. In this example we are looking at posts with more than 1,000 votes and their use of the top 3 most frequent words.
Frequent use of these top occurring words throughout a post are strong markers of a high performing (lots of up votes) post.
There appears to be an obvious distinction between two classes of top performing user.
- High frequency posters publishing short form, short shelf life content. These are the volume players.
- Low frequency posters publishing longer form content with longer shelf life. These are the value players
Lowest Down Voted Posts
This class of post seems to have a heavy emphasis on crypto and alt-coins. This is known to be one of the earliest steemit communities, comprised of day traders and market makers. They are a vocal and opinionated group.
Unsurprisingly, user @mack-bott appears among the most frequent terms. This user is a bot for flagging spam accounts.
The word cloud identifies several crypto-coin news services. These appear to be automated news submission engines (bots). It's these that are being targeted for down voting by @mack-bot and @patrice. In the early days these may have been useful to the crypt-community mentioned above. Today, the platform seems to have outgrown the need for these news (spam) bots and they've become widely disliked.
For example @bitcoinhub was scraping the internet for news and posting every 10 mins. Their last post was December 12th. Presumably the owner of this bot gave up the battle and conceded defeat to @mack-bot and @Patrice (amongst others)
We owe them a debt of gratitude for keeping this spam out of our feeds.
Word Cloud Minimum Word Frequency : 5000
Lexical Dispersion Plot
A lexical dispersion plot of the bottom performing posts (more than -15 down votes) shows curious results. Of the most frequent words ("source", "full", "target") only two appear in texts of the most down voted posts.
The user @nijeah appears to be a crypto trader posting market commentry and involved in a flame war with other users. Aggressive doxing and down voting seems to be at play here.
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Very interesting and useful information! Thank you for the awesome post. I will try to implement some of the info you provided in my future posts.
I like the writing of your article is very interesting people to comment on your article ,,,
I want tips and tricks from you ,, please help
I like your words that are highly motivated ,, I want to ask for tips and tricks
from you how to write interesting ,, please help.
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@originalworks
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A lot really went into this work. Nice
Thanks for noticing! I have plenty of time at the moment...
Nice
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Di upvote lah punya kita
Good analysis ...
hopefully you can give me advice
Greeting from me ..
Sure thing. What sort of advice are you seeking?
Nice :). I would be really, really happy, if you guys could look up my paperpiano and tell me, what you think of this :D.
Thank you
Piano Master
fascinating data, thanks
Looks amazing man please visit in my blog 👉
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Wew dollars nya
I am interesting with this post
Good analyze
Nice Job
wow you really are a braniac ... the fact you are able to shuffle all the data. I enjoy reading about which ones...another thing to consider is @jout just puts one sentence on his posts most of the time and it is to make you laugh as he has a meme or comic or youtube something he found on the web
my inclination is to give an upvote for a laugh
thanks for your studies
This is golden! Upvoted, followed.
Keep this stuff coming. I'm looking forward to your next analysis...
I can see why "source" is the doom word, makes my veins twist in annoyance when i see it headlining the spamland... very interesting to read about, good efforts! :D
What is the significance of the word “source”?
I just think it's a bit of a presumptious, rather manipulative and often bias word in the context of peoples blogs. It's an individual selfishly claiming they hold all the info you need, come along and upvote me me me, when clearly it's mostly linking to shit, fake and cheap posts... it's the source of all the problems hohoho :D
I agree that most blogs, particularly the “successful” ones are mostly self righteous, puffed up, pseudoscience, pontificating tosh.
Most of the authentic stuff doesn’t see the light of day
"pontificating tosh" hahah nice
It's a shame that the only people who care and are willing to make efforts are also the people that spends hours writing the quality posts. So time consumed. Whilst the self righteous and spammers will always be there to abuse a system and quickly in numbers. Still racking brainz on ideas of how to improve... the tagging words are interesting though, giving me things to think about. I like the psychology behind it. :)
I think auto-tagging might be viable. Where an algorithm looks at word clusters and associations then assigns a tag... would prevent tag spamming
Sounds like a good idea! ... How easy would it be to implement though? I definitely think the tagging angle is one of the prime solutions... really hard to think of much else that doesn't require lots of monitoring and efforts from individuals.
thanks for the quality content and nice analysis
in an interesting turn of events steemit wont let me upvote this post lol
Yeah, I’ve been experiencing some weirdness too. Perhaps because I accidentally upvoted my own post; I gather this isn’t allowed?
Yes thenk you
Very useful article. Excellent work! Thank you.