Quelle: Danzarella.com Die hier vorgestellte Arbeit wurde von Dan Zarella im Jahr 2009 veröffentlich.

1. DanZarrella’s
The Science of
ReTweets
2009

2. Contents
Why ReTweets Matter 4 Psychology
RID Content Types 17
More Followers = More ReTweets? RID Attributes 18
Distribution of ReTweets per Follower 5 LIWC Attributes 19
RTpF of Suggested Users 6
Timing
ReTweeting & Links Time of Day 20
Link Occurrence in ReTweets 7 Day of Week 21
ReTweetability of URL Shorteners 8
About the Author & Data 22
Lingustics
Most ReTweetable Words & Phrases 9
Least ReTweetable Words 10
Average Syllables per Word 11
Readability Grade Levels 12
Word Occurrence & Novelty 13
Parts of Speech 14
Punctuation Occurrence 15
Punctuation Types 16

3. “Ideas shape the course
of history.”
-John Maynard Keynes
2009 © Dan Zarrella DanZarrella.com 3

4. Why ReTweets Matter
I spend a lot of time working on ReTweets, because I believe them to be one of the most impor-
tant developments in modern communications, extending far beyond the Twittersphere.
Like “The Matrix” was composed of computer code, the real world is made of infectious infor-
mation. Your chair, your desk, the computer you’re reading this on, the food you’ll eat today, the
money you’ll earn: they all began as ideas jumping from person to person. None of it would exist
if the concept wasn’t contagious.
Ideological epidemics have made and lost fortunes, they have saved countless lives and caused
horrific wars, they have birthed and destroyed nations. Clearly the most powerful weapon
known to man would be the ability to create powerful mental viruses at will. The very course
of human history would be at your whim.
You don’t spread ideas just because they are “good;” you spread them because of some other
trigger or set of triggers has been pulled in your brain. And that trigger fires the biggest gun
ever seen.
And yet, a reliable, repeatable method of crafting a contagious idea has not emerged.
The advent of the web changed how memes spread: it made them spread faster, it exposed
them to more people, and it removed many of the constraints imposed by the limits of human
memory. But there is one change that dwarfs them all: observability.
We can now compare millions of viral ideas to uncover the building blocks of contagiousness.
ReTweets may seem like a small idea, and they are in some ways. But that small idea is the first
real window into how ideas spread from person to person. We can study the linguistic traits, the
topical characteristics, the epidemiological dynamics, and the social network interactions that
take place when a person spreads a meme.
Not only can this information help us create more contagious Tweets, but many of the lessons
learned through ReTweets will be applicable to viral ideas in other mediums.
For the first time in human history we can begin to gaze into the inner workings of the con-
tagious idea. That most powerful force can now be put under our microscope and probed for its
secrets.
2009 © Dan Zarrella DanZarrella.com 4

5. Distribution of ReTweets per Follower
When I started thinking about how to get more ReTweets, my first thought was
to get more followers; clearly, more followers would mean more ReTweets. To
check this assumption, I looked at ReTweets per Followers (RTpF), the number
of ReTweets per day divided by the number of followers.
The graph above shows the distribution of RTpF in the top 9000 most followed
users in my database; I’ve graphed the actual distribution line in blue, with a 30-
point moving average over it in black.
Here we see that while most users had an RTpF of under 1% in my dataset,
some users showed much larger ratios, possibly indicating that there are a class
of users who are more “ReTweetable” than others.
This means that while users with more followers will get more ReTweets, some
users are able to get lots of ReTweets without lots of followers; their content
must be more contagious.
2009 © Dan Zarrella DanZarrella.com 5

6. RTpF of Suggested Users
Non-Suggested Suggested
Twitter maintains a list of users as suggested people for new users to follow; the
people on this list gain tens of thousands of new followers every day and are
among the most followed people on Twitter.
I looked at 200 suggested users and compared them to the 200 most followed
users not on the list. Since many of the suggested users are the most followed
people on Twitter, they had a much higher average number of followers. So I
compared the two groups using the RTpF metric.
The result is clear: suggested users are far less ReTweetable. I think this is likely
due to the fact that many of the followers gained by those users on the suggest-
ed list are new Twitter users and may be less ReTweet-savvy.
2009 © Dan Zarrella DanZarrella.com 6

7. Link Occurrence in ReTweets
All Tweets ReTweets
I began to study the content of Tweets to identify traits that are correlated with
more contagious, or ReTweetable, content. The first such trait was the pres-
cence of a link.
I found that in a random sample of normal (non-ReTweet) Tweets, 18.96% con-
tained a link, whereas 3 times that many ReTweets (56.69%) included a link.
This means that not only are ReTweets an accepted way to spread off-Twitter
content, the prescence of a link may increase a Tweet’s chances of being shared.
2009 © Dan Zarrella DanZarrella.com 7

8. ReTweetability of URL Shorteners
bit.ly
ow.ly
su.pr
is.gd
301.to
cli.gs
twurl.nl
ff.im
tr.im
tumblr.com
blip.fm
twitpic.com
tinyurl.com
-6% -4% -2% 0% 2% 4% 6% 8% 10%
I know that most ReTweets contain a link, but there are hundreds of differ-
ent URL shortening services available to help you save space with that link.
I analyzed my database of over 30 million ReTweets and compared them to
over 2 million random Tweets to find which shorteners are the most (and least)
ReTweetable.
I calculated how much more or less often each URL shortening service appeared
in ReTweets than it did in normal Tweets and presented this value as a percent-
age. For instance, in my data 9.28% more ReTweets than random Tweets used
bit.ly. I took into account the fact that ReTweets tend to contain more links than
average Tweets and normalized the occurrence values.
I compared the percentage of occurrence for each shortener in random Tweets
to the same shortener in ReTweets, to control for the popularity of services like
bit.ly and tinyurl.com.
The short, post-Twitter shorteners, bit.ly, ow.ly, and is.gd were all more
ReTweetable than the older, longer, tinyurl.
2009 © Dan Zarrella DanZarrella.com 8

9. Most ReTweetable Words & Phrases
1. you 11. please retweet
2. twitter 12. great
3. please 13. social media
4. retweet 14. 10
5. post 15. follow
6. blog 16. how to
7. social 17. top
8. free 18. blog post
9. media 19. check out
10. help 20. new blog post
I compared common words and phrases in random Tweets and ReTweets to find
those words that occur in ReTweets more than they occur in normal Tweets.
The word “you,” while very common, seems to occur especially often in
ReTweets, indicating that if you’re talking to “me,” I am more likely to ReTweet
it.
Its really not surprising that “Twitter” ranks high, but this is a good reminder that
self-reference is always good for buzz in social media.
The words “please” and “please ReTweet” are very ReTweetable (“please
rt” also ranked highly). It’s hard to overstate how important it is to ask for the
ReTweet when you want it; calls to action work.
“New Blog Post” is the common prefix used when a person Tweets about, well,
a new blog post to their site. That this ranks so highly tells us that Tweeting your
posts is a very smart thing to do.
2009 © Dan Zarrella DanZarrella.com 9

10. Least ReTweetable Words
1. game 11. well
2. going 12. sleep
3. haha 13. gonna
4. lol 14. hey
5. but 15. tomorrow
6. watching 16. tired
7. work 17. some
8. home 18. back
9. night 19. bored
10. bed 20. listening
What about the words that are least likely to get your ReTweets?
There are a number of “-ing” verbs, including “going,” “watching” and “listen-
ing,” which reinforces my understanding that answers to the “What are you
doing?” question don’t get very many ReTweets.
The presence of “sleep,” “bed,” “night,” and “tired” indicate that people often
Tweet “goodnight” style messages, but generally don’t ReTweet them.
The relatively informal nature of many of the words on the list including “lol,”
“gonna,” and “hey,” show that simple or slang conversation is not ReTweetable.
The lesson learned here is that if you’re trying to get more ReTweets, don’t just
engage in idle chit-chat or Tweet about mundane activities.
2009 © Dan Zarrella DanZarrella.com 10

11. Average Syllables per Word
1.63
1.62
1.61
1.60
1.59
1.58
1.57
1.56
ReTweets Random Tweets
I tested the assumption that simplicity is a vital component of ReTweets (as it
has been observed in other viral-content types) and I found that random Tweets
have 1.58 syllables per word on average, while ReTweets have an average of 1.62
syllables per word. Longer, higher syllable-count words are typically more com-
plex, indicating that ReTweets may be more complex than their less viral coun-
terparts.
Be sure to notice the scale of the graph above, the difference is small, but it cer-
tainly challenged my hypothesis that ReTweets are less-complex.
2009 © Dan Zarrella DanZarrella.com 11

12. Readability Grade Levels
6.6
6.5
6.4
6.3
6.2
6.1
6.0
5.9
5.8
5.7
5.6
ReTweets Random Tweets ReTweets Random Tweets
Flesch-Kincaid SMOG
I then compared two different types of reading grade level analysis metrics and
they revealed that ReTweets, in general, are less “readable” and require a high-
er level of education to understand.
A Flesch-Kincaid test gave ReTweets a reading grade level of 6.47 years of edu-
cation, while random Tweets only required 6.04 years. The similar SMOG test
(Simple Measure of Gobbledygook) indicated that ReTweets required 6.13 years
of schooling, with random Tweets only needing 5.88 years.
Again, take care to notice the scale of the Y-axis.
2009 © Dan Zarrella DanZarrella.com 12

13. Word Occurrence/Novelty
100
90
80
70
60
50
40
30
20
10
0
ReTweets Random Tweets
Another characteristic commonly found in viral content is novelty; that is, the
“newness” of the ideas and information presented. I created a measure of nov-
elty by counting how many other times each word in my sample sets occurred.
In the random Tweet sample, each word was found an average of 89.19 other
times, while in the ReTweet sample each word was only found 16.37 other times.
This shows us that while simplicity may not be very important to ReTweetability,
novelty certainly is.
2009 © Dan Zarrella DanZarrella.com 13

14. Parts of Speech
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
Noun, plural
Proper noun, singular
Verb, 3rd person singular present
Proper noun, singular
Adjective, superlative
Adjective, comparitive
Wh-adverb
Verb, past participle
Verb, base form
Verb, gerund or present participle
Verb, non-3rd person singular present
Verb, past tense
Noun, singular or mass
Adverb
Random Tweets ReTweets
Part of speech (POS) tagging is an analysis technique in which an algorithm is
used to label each word in a piece of content as a specific part-of-speech–noun,
verb, adjective, etc.
The graph above shows what percentages of words in each sample were labeled
as a specific part-of-speech. It lists only the most interesting parts from the much
larger list of POS tags.
Interesting points from this data include the noun and 3rd-person heaviness of
ReTweets, indicating a subject matter and headline type nature.
2009 © Dan Zarrella DanZarrella.com 14

15. Punctuation Occurrence
100%
95%
90%
85%
80%
75%
ReTweets Random ReTweets Random
With Colons Without Colons
I compared a random sample of “normal” Tweets to a sample of ReTweets and
found that 85.86% of Tweets contain some form of punctuation, and an over-
whelming 97.55% of ReTweets do as well.
Of course, the prevailing ReTweet format includes a colon to better display the
original Tweet, but even when ignoring this form of punctuation, ReTweets still
contain more punctuation than non-ReTweets (93.42% to 83.78%).
2009 © Dan Zarrella DanZarrella.com 15

16. Punctuation Types
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Colon
Period
Exclamation Point
Comma
Hyphen
Ellipsis
Question Mark
Semicolon
Random Tweets ReTweets
I then analyzed the frequency of specific types of punctuation and found that
hyphens, periods and colons are the most ReTweetable punctuation, occurring
far more commonly in ReTweets than in regular Tweets, while the rarest mark,
the semicolon, is the only unReTweetable punctuation mark.
2009 © Dan Zarrella DanZarrella.com 16

17. RID Content Types
12%
10%
8%
6%
4%
2%
0%
ReTweets Random ReTweets Random ReTweets Random
Emotional Primordial Conceptual
I used the two linguistic lexicons to analyze ReTweet content: RID and LIWC.
First is the more “Freudian” Regressive Imagery Dictionary (RID). This cod-
ing scheme is designed to measure the amount and type of three categories of
content: primordial (the unconscious way you think, like in dreams); conceptual
(logical and rational thought); and emotional.
The graph above shows that ReTweets contain less primordial and emotional
content than random Tweets and more conceptual content.
2009 © Dan Zarrella DanZarrella.com 17

18. RID Attributes
2.5%
2%
1.5%
1%
.5%
0%
Social Behavior Glory Instrumental Sound Vision Abstraction
Behavior
Random Tweets ReTweets
Looking at specific RID attributes, I saw that social and instrumental (constructive
words like build and create) behaviors are ReTweetable, while abstract thought
and sensation-based words are not.
2009 © Dan Zarrella DanZarrella.com 18

19. LIWC Attributes
3.5%
3%
2.5%
2%
1.5%
1%
.5%
0%
Occupation
We
Media
Religion
Money
Insight
Negative Emotions
Swears
Senses
Tentative
Self-Reference
Random Tweets ReTweets
The next analysis I performed used LIWC (pronounced “Luke”). This is a lexicon
similar to RID, but based in more reviewed and accepted research and refined
over 15 years. LIWC measures the cognitive and emotional properties of people
based on the words they use.
LIWC analysis shows that Tweets about work, religion, money and media/celeb-
rities are more ReTweetable than Tweets about negative emotions, sensations,
swear words and self-reference.
2009 © Dan Zarrella DanZarrella.com 19

20. Time of Day (EST)
6.00%
6%
5.5%
5.50%
5%
5.00%
4.5%
4.50%
4%
3.5%
4.00%
3%
3.50%
2.5%
3.00%
2%
1.5%
2.50%
1%
2.00%
.5%
1.50%
0%
1 PM
2 PM
3 PM
4 PM
5 PM
6 PM
7 PM
8 PM
9 PM
12 AM
10 AM
11 AM
1 AM
2 AM
3 AM
4 AM
5 AM
6 AM
7 AM
8 AM
9 AM
11:00 PM
12:00AM
1:00AM
2:00AM
3:00AM
4:00AM
5:00AM
6:00AM
7:00AM
8:00AM
9:00AM
10:00AM
11:00AM
12:00 PM
1:00 PM
2:00 PM
3:00 PM
4:00 PM
5:00 PM
6:00 PM
7:00 PM
8:00 PM
9:00 PM
10:00 PM
12
11
10
Random Tweets ReTweets
I compared the volume of ReTweeting that occurs during the day to the volume
of regular Tweets to find that ReTweeting is much more diurnal. While overall
Tweet volume peaks during business hours and evening, ReTweeting occurs
much more frequently between 3 PM and midnight.
If you want to get ReTweets, it makes sense to post your content during these
hours.
2009 © Dan Zarrella DanZarrella.com 20

21. Day of Week
19%
19.00%
18%
18.00%
17%
17.00%
16%
16.00%
15%
15.00%
14%
14.00%
13%
13.00%
12%
12.00%
11%
11.00%
Sun
Sun Mon
Mon Tues
Tue Wed
Wed Thu
Thu Fri
Fri Sat
Sat
Random Tweets ReTweets
I also compared the volume of activity that occurs on various days of the week.
Overall Tweeting activity peaks during the business week, as does ReTweeting
activity.
Monday and Friday are both ReTweetable days, in that a higher percentage of
ReTweeting activity for the week occurs than does regular Tweeting. Friday,
however, shows the highest volume of ReTweeting, and Thursday the highest
volume of standard Tweeting.
2009 © Dan Zarrella DanZarrella.com 21

22. About the Author
Dan Zarrella is an award-winning social, search, and viral marketing scientist and
author of the upcoming O’Reilly media book “The Social Media Marketing Book.“
Dan has written extensively about the science of viral marketing, memetics and
social communications on his own blog and for a variety of popular industry blogs,
including Mashable, CopyBlogger, ReadWriteWeb, Plagiarism Today, ProBlogger,
Social Desire, CenterNetworks, Nowsourcing, and SEOScoop.
He has been featured in The Twitter Book, The Financial Times, NYPost, The Bos-
ton Globe, Forbes, Wired, The Wall Street Journal, Mashable and TechCrunch. He
was recently awarded Shorty and Semmy awards for social media & viral marketing.
A frequent guest speaker and panelist, Dan has spoken at PubCon, Search Engine Strategies, Convergence ‘09,
140 The Twitter Conference, WordCamp Mid Atlantic, Social Media Camp, Inbound Marketing Bootcamp, and
The Texas Domains and Developers Conference. He currently works as an inbound marketing manager at Hub-
Spot.
About the Data
Over the course of 9 months, beginning in December of 2008, I’ve collected over 40 million ReTweets, including
Tweets that contain variations of “RT,” “ReTweet,” and “Via.” I’ve also collected a random sampling of over 10
million “regular” Tweets that may or may not be ReTweets. The Tweets come from Twitter’s search API and its
streaming API, both of which I have whitelisted access to. I use these 2 data sources and my own PHP scripts to
analyze and compare the characteristics of both.
2009 © Dan Zarrella DanZarrella.com 22