The effect of wording on message propagation: Topic-and author

The effect of wording on message propagation: Topic-and author
The effect of wording on message propagation:
Topic- and author-controlled natural experiments on Twitter
Chenhao Tan
Dept. of Computer Science
Cornell University
[email protected]
Lillian Lee
Dept. of Computer Science
Cornell University
[email protected]
Consider a person trying to spread an
important message on a social network.
He/she can spend hours trying to craft the
message. Does it actually matter? While
there has been extensive prior work looking into predicting popularity of socialmedia content, the effect of wording per
se has rarely been studied since it is often confounded with the popularity of the
author and the topic. To control for these
confounding factors, we take advantage
of the surprising fact that there are many
pairs of tweets containing the same url and
written by the same user but employing
different wording. Given such pairs, we
ask: which version attracts more retweets?
This turns out to be a more difficult task
than predicting popular topics. Still, humans can answer this question better than
chance (but far from perfectly), and the
computational methods we develop can do
better than both an average human and a
strong competing method trained on noncontrolled data.
How does one make a message “successful”? This
question is of interest to many entities, including
political parties trying to frame an issue (Chong
and Druckman, 2007), and individuals attempting
to make a point in a group meeting. In the first
case, an important type of success is achieved if
the national conversation adopts the rhetoric of the
party; in the latter case, if other group members
repeat the originating individual’s point.
The massive availability of online messages,
such as posts to social media, now affords researchers new means to investigate at a very large
scale the factors affecting message propagation,
Bo Pang
Google Inc.
[email protected]
also known as adoption, sharing, spread, or virality. According to prior research, important features include characteristics of the originating author (e.g., verified Twitter user or not, author’s
messages’ past success rate), the author’s social
network (e.g., number of followers), message timing, and message content or topic (Artzi et al.,
2012; Bakshy et al., 2011; Borghol et al., 2012;
Guerini et al., 2011; Guerini et al., 2012; Hansen
et al., 2011; Hong et al., 2011; Lakkaraju et al.,
2013; Milkman and Berger, 2012; Ma et al., 2012;
Petrović et al., 2011; Romero et al., 2013; Suh et
al., 2010; Sun et al., 2013; Tsur and Rappoport,
2012). Indeed, it’s not surprising that one of the
most retweeted tweets of all time was from user
BarackObama, with 40M followers, on November
6, 2012: “Four more years. [link to photo]”.
Our interest in this paper is the effect of alternative message wording, meaning how the message
is said, rather than what the message is about. In
contrast to the identity/social/timing/topic features
mentioned above, wording is one of the few factors directly under an author’s control when he or
she seeks to convey a fixed piece of content. For
example, consider a speaker at the ACL business
meeting who has been tasked with proposing that
Paris be the next ACL location. This person cannot on the spot become ACL president, change the
shape of his/her social network, wait until the next
morning to speak, or campaign for Rome instead;
but he/she can craft the message to be more humorous, more informative, emphasize certain aspects instead of others, and so on. In other words,
we investigate whether a different choice of words
affects message propagation, controlling for user
and topic: would user BarackObama have gotten
significantly more (or fewer) retweets if he had
used some alternate wording to announce his reelection?
Although we cannot create a parallel universe
Table 1: Topic- and author-controlled (TAC) pairs. Topic control = inclusion of the same URL.
cactus music
t1 : FIRST ON CNN: After Petraeus scandal, Paula Broadwell looks to recapture ‘normal life.’
t2 : First on CNN: Broadwell photos shared with Security Clearance as she and her family fight media portrayal of her [same URL]
t1 : Workers, families take stand against Thanksgiving hours:
t2 : Staples, Medieval Times Workers Say Opening Thanksgiving Day Crosses the Line [same URL]
t1 : I know at some point you’ve have been saved from hunger by our rolling food trucks friends. Let’s help support them!
t2 : Food trucks are the epitome of small independently owned LOCAL businesses! Help keep them going! Sign the petition [same
in which BarackObama tweeted something else1 ,
fortunately, a surprising characteristic of Twitter
allows us to run a fairly analogous natural experiment: external forces serendipitously provide an
environment that resembles the desired controlled
setting (DiNardo, 2008). Specifically, it turns out
to be unexpectedly common for the same user to
post different tweets regarding the same URL —
a good proxy for fine-grained topic2 — within a
relatively short period of time.3 Some example
pairs are shown in Table 1; we see that the paired
tweets may differ dramatically, going far beyond
word-for-word substitutions, so that quite interesting changes can be studied.
Looking at these examples, can one in fact tell
from the wording which tweet in a topic- and
author-controlled pair will be more successful?
The answer may not be a priori clear. For example,
for the first pair in the table, one person we asked
found t1 ’s invocation of a “scandal” to be more
attention-grabbing; but another person preferred
t2 because it is more informative about the URL’s
content and includes “fight media portrayal”. In
an Amazon Mechanical Turk (AMT) experiment
(§4), we found that humans achieved an average
accuracy of 61.3%: not that high, but better than
chance, indicating that it is somewhat possible for
humans to predict greater message spread from
different deliveries of the same information.
Buoyed by the evidence of our AMT study that
wording effects exist, we then performed a battery
of experiments to seek generally-applicable, non1
Cf. the Music Lab “multiple universes” experiment to
test the randomness of popularity (Salganik et al., 2006).
Although hashtags have been used as coarse-grained
topic labels in prior work, for our purposes, we have no assurance that two tweets both using, say, “#Tahrir” would be attempting to express the same message but in different words.
In contrast, see the same-URL examples in Table 1.
Moreover, Twitter presents tweets to a reader in strict
chronological order, so that there are no algorithmic-ranking
effects to compensate for in determining whether readers saw
a tweet. And, Twitter accumulates retweet counts for the entire retweet cascade and displays them for the original tweet
at the root of the propagation tree, so we can directly use
Twitter’s retweet counts to compare the entire reach of the
different versions.
n1 = 5
n2 = 29
n1 = 46
n2 = 27
n1 = 2
n2 = 13
Twitter-specific features of more successful phrasings. §5.1 applies hypothesis testing (with Bonferroni correction to ameliorate issues with multiple
comparisons) to investigate the utility of features
like informativeness, resemblance to headlines,
and conformity to the community norm in language use. §5.2 further validates our findings via
prediction experiments, including on completely
fresh held-out data, used only once and after an
array of standard cross-validation experiments.4
We achieved 66.5% cross-validation accuracy and
65.6% held-out accuracy with a combination of
our custom features and bag-of-words. Our classifier fared significantly better than a number of
baselines, including a strong classifier trained on
the most- and least-retweeted tweets that was even
granted access to author and timing metadata.
Related work
The idea of using carefully controlled experiments
to study effective communication strategies dates
back at least to Hovland et al. (1953). Recent
studies range from examining what characteristics of New York Times articles correlate with high
re-sharing rates (Milkman and Berger, 2012) to
looking at how differences in description affect
the spread of content-controlled videos or images
(Borghol et al., 2012; Lakkaraju et al., 2013).
Simmons et al. (2011) examined the variation of
quotes from different sources to examine how textual memes mutate as people pass them along, but
did not control for author. Predicting the “success”
of various texts such as novels and movie quotes
has been the aim of additional prior work not already mentioned in §1 (Ashok et al., 2013; Louis
and Nenkova, 2013; Danescu-Niculescu-Mizil et
al., 2012; Pitler and Nenkova, 2008; McIntyre and
Lapata, 2009). To our knowledge, there have been
no large-scale studies exploring wording effects in
a both topic- and author-controlled setting. Employing such controls, we find that predicting the
more effective alternative wording is much harder
than the previously well-studied problem of pre4
And after crossing our fingers.
Our main dataset was constructed by first gathering 1.77M topic- and author-controlled (henceforth TAC) tweet pairs5 differing in more than just
spacing.6 We accomplished this by crawling timelines of 236K user ids that appear in prior work
(Kwak et al., 2010; Yang and Leskovec, 2011)
via the Twitter API. This crawling process also
yielded 632K TAC pairs whose only difference
was spacing, and an additional 558M “unpaired”
tweets; as shown later in this paper, we used these
extra corpora for computing language models and
other auxiliary information. We applied nonobvious but important filtering — described later
in this section — to control for other external factors and to reduce ambiguous cases. This brought
us to a set of 11,404 pairs, with the gold-standard
labels determined by which tweet in each pair was
the one that received more retweets according to
the Twitter API. We then did a second crawl to
get an additional 1,770 pairs to serve as a held-out
dataset. The corresponding tweet IDs are available
online at
wording-for-propagation.html. (Twitter’s terms of service prohibit sharing the actual
Throughout, we refer to the textual content of
the earlier tweet within a TAC pair as t1 , and of the
later one as t2 . We denote the number of retweets
received by each tweet by n1 and n2 , respectively.
We refer to the tweet with higher (lower) ni as the
“better (worse)” tweet.
Using “identical” pairs to determine how to
compensate for follower-count and timing effects. In an ideal setting, differences between
n1 and n2 would be determined solely by differences in wording. But even with a TAC pair,
retweets might exhibit a temporal bias because of
the chronological order of tweet presentation (t1
might enjoy a first-mover advantage (Borghol et
al., 2012) because it is the “original”; alternatively,
No data collection/processing was conducted at Google.
The total excludes: tweets containing multiple URLs;
tweets from users posting about the same URL more than five
times (since such users might be spammers); the third, fourth,
or fifth version for users posting between three and five tweets
for the same URL; retweets (as identified by Twitter’s API or
by beginning with “RT @”); non-English tweets.
>1K f’ers
>2.5K f’ers
>5K f’ers
>10K f’ers
>5K f’ers,<12hrs
2 3
b 2|n1)
dicting popular content when author or topic can
freely vary.
Related work regarding the features we considered is deferred to §5.1 (features description).
12 18 24 36 48
time lag (hours)
(a) For identical TAC pairs,
retweet-count deviation vs.
time lag between t1 and
t2 , for the author followercounts given in the legend.
0 0
(b) Avg. n2 vs. n1 for identical TAC pairs, highlighting
our chosen time-lag and follower thresholds. Bars: standard error. Diagonal line:
p 2 |n1 q “ n1 .
Figure 1: (a): The ideal case where n2 “ n1
when t1 “ t2 is best approximated when t2 occurs within 12 hours of t1 and the author has at
least 10,000 or 5,000 followers. (b): in our chosen
setting (blue circles), n2 indeed tends to track n1 ,
whereas otherwise (black squares), there’s a bias
towards retweeting t1 .
t2 might be preferred because retweeters consider
t1 to be “stale”). Also, the number of followers an
author has can have complicated indirect effects
on which tweets are read (space limits preclude
We use the 632K TAC pairs wherein t1 and
t2 are identical7 to check for such confounding
effects: we see how much n2 deviates from n1
in such settings, since if wording were the only
explanatory factor, the retweet rates for identical
tweets ought to be equal. Figure 1(a) plots how
the time lag between t1 and t2 and the author’s
follower-count affect the following deviation estimate:
p 2 |n1 q ´ n1 |,
0ďn1 ă10
p 2 |n1 q is the average value of n2 over
where Epn
pairs whose t1 is retweeted n1 times. (Note that
the number of pairs whose t1 is retweeted n1 times
decays exponentially with n1 ; hence, we condition on n1 to keep the estimate from being dominated by pairs with n1 “ 0, and do not consider
n1 ě 10 because there are too few such pairs to esp 2 |n1 q reliably.) Figure 1(a) shows that
timate Epn
the setting where we (i) minimize the confounding effects of time lag and author’s follower-count
and (ii) maximize the amount of data to work with
Identical up to spacing: Twitter prevents exact copies by
the same author appearing within a short amount of time, but
some authors work around this by inserting spaces.
is: when t2 occurs within 12 hours after t1 and
the author has more than 5,000 followers. Figure
1(b) confirms that for identical TAC pairs, our chosen setting indeed results in n2 being on average
close to n1 , which corresponds to the desired setting where wording is the dominant differentiating
Focus on meaningful and general changes.
Even after follower-count and time-lapse filtering,
we still want to focus on TAC pairs that (i) exhibit significant/interesting textual changes (as exemplified in Table 1, and as opposed to typo corrections and the like), and (ii) have n2 and n1 sufficiently different so that we are confident in which
ti is better at attracting retweets. To take care of
(i), we discarded the 50% of pairs whose similarity was above the median, where similarity was
tf-based cosine.9 For (ii), we sorted the remaining pairs by n2 ´ n1 and retained only the top and
bottom 5%.10 Moreover, to ensure that we do not
overfit to the idiosyncrasies of particular authors,
we cap the number of pairs contributed by each
author to 50 before we deal with (ii).
Human accuracy on TAC pairs
We first ran a pilot study on Amazon Mechanical Turk (AMT) to determine whether humans
can identify, based on wording differences alone,
which of two topic- and author- controlled tweets
is spread more widely. Each of our 5 AMT tasks
involved a disjoint set of 20 randomly-sampled
TAC pairs (with t1 and t2 randomly reordered);
subjects indicated “which tweet would other people be more likely to retweet?”, provided a short
justification for their binary response, and clicked
a checkbox if they found that their choice was a
“close call”. We received 39 judgments per pair in
aggregate from 106 subjects total (9 people completed all 5 tasks). The subjects’ justifications
were of very high quality, convincing us that they
all did the task in good faith11 . Two examples for
We also computed the Pearson correlation between n1
and n2 , even though it can be dominated by pairs with smaller
n1 . The correlation is 0.853 for “ą 5K f’ers, ă12hrs”,
clearly higher than the 0.305 correlation for “otherwise”.
Idf weighting was not employed because changes to frequent words are of potential interest. Urls, hashtags, @mentions and numbers were normalized to [url], [hashtag],
[at], and [num] before computing similarity.
For our data, this meant n2 ´ n1 ě 10 or ď ´15. Cf.
our median number of retweets: 30.
We also note that the feedback we got was quite positive, including: “...It’s fun to make choices between close
tweets and use our subjective opinion. Thanks and best of
the third TAC pair in Table 1 were: “[t1 makes] the
cause relate-able to some people, therefore showing more of an appeal as to why should they click
the link and support” and, expressing the opposite
view, “I like [t2 ] more because [t1 ] starts out with
a generalization that doesn’t affect me and try to
make me look like I had that experience before”.
If we view the set of 3900 binary judgments
for our 100-TAC-pair sample as constituting independent responses, then the accuracy for this
set is 62.4% (rising to 63.8% if we exclude the
587 judgments deemed “close calls”). However, if
we evaluate the accuracy of the majority response
among the 39 judgments per pair, the number rises
to 73%. The accuracy of the majority response
generally increases with the dominance of the majority, going above 90% when at least 80% of the
judgments agree (although less than a third of the
pairs satisfied this criterion).
Alternatively, we can consider the average accuracy of the 106 subjects: 61.3%, which is better than chance but far from 100%. (Variance was
high: one subject achieved 85% accuracy out of
20 pairs, but eight scored below 50%.) This result is noticeably lower than the 73.8%-81.2% reported by Petrović et al. (2011), who ran a similar experiment involving two subjects and 202
tweet pairs, but where the pairs were not topic- or
We conclude that even though propagation prediction becomes more challenging when topic
and author controls are applied, humans can
still to some degree tell which wording attracts
more retweets. Interested readers can try this
out themselves at
We now investigate computationally what wording features correspond to messages achieving a
broader reach. We start (§5.1) by introducing a set
of generally-applicable and (mostly) non-Twitterspecific features to capture our intuitions about
what might be better ways to phrase a message.
We then use hypothesis testing (§5.1) to evaluate
the importance of each feature for message propluck with your research” and “This was very interesting and
really made me think about how I word my own tweets. Great
job on this survey!”. We only had to exclude one person (not
counted among the 106 subjects), doing so because he or she
gave the same uninformative justification for all pairs.
The accuracy range stems from whether author’s social
features were supplied and which subject was considered.
Table 2: Notational conventions for tables in §5.1.
One-sided binomial test for feature increase
One-sided paired t-test for feature efficacy
(Do authors prefer to ‘raise’ the feature in t2 ?)
ÒÒÒÒ: pă1e-20 ÓÓÓÓ: pą1-1e-20
ÒÒÒ : pă0.001 ÓÓÓ : pą0.999
YES : t2 has a higher feature score than t1 , α “ .05
ÒÒ : pă0.01
ÓÓ : pą0.99
NO : t2 has a lower feature score than t1 , α “ .05
Ò : pă0.05
Ó : pą0.95
(x%): %pf2 ą f1 q, if sig. larger or smaller than 50%
˚: passes our Bonferroni correction
agation and the extent to which authors employ
it, followed by experiments on a prediction task
(§5.2) to further examine the utility of these features.
Table 3: Explicit requests for sharing (where only
occurrences POS-tagged as verbs count, according
to the Gimpel et al. (2011) tagger).
Features: efficacy and author preference
What kind of phrasing helps message propagation? Does it work to explicitly ask people to share
the message? Is it better to be short and concise or
long and informative? We define an array of features to capture these and other messaging aspects.
We then examine (i) how effective each feature is
for attracting more retweets; and (ii) whether authors prefer applying a given feature when issuing
a second version of a tweet.
First, for each feature, we use a one-sided paired
t-test to test whether, on our 11K TAC pairs, our
score function for that feature is larger in the better tweet versions than in the worse tweet versions,
for significance levels α “ .05, .01, .001, 1e-20.
Given that we did 39 tests in total, there is a risk
of obtaining false positives due to multiple testing (Dunn, 1961; Benjamini and Hochberg, 1995).
To account for this, we also report significance results for the conservatively Bonferroni-corrected
(“BC”) significance level α = 0.05/39=1.28e-3.
Second, we examine author preference for applying a feature. We do so because one (but by no
means the only) reason authors post t2 after having
already advertised the same URL in t1 is that these
authors were dissatisfied with the amount of attention t1 got; in such cases, the changes may have
been specifically intended to attract more retweets.
We measure author preference for a feature by the
percentage of our TAC pairs13 where t2 has more
“occurrences” of the feature than t1 , which we denote by “%pf2 ą f1 q”. We use the one-sided binomial test to see whether %pf2 ą f1 q is significantly larger (or smaller) than 50%.
For our preference experiments, we added in pairs where
n2 ´ n1 was not in the top or bottom 5% (cf. §3, meaningful
changes), since to measure author preference it’s not necessary that the retweet counts differ significantly.
YES (59%)
YES (56%)
Table 4: Informativeness.
length (chars)
proper noun
YES (54%)
YES (56%)
YES (51%)
YES (55%)
NO– (45%)
NO– (48%)
YES (53%)
Not surprisingly, it helps to ask people to share.
(See Table 3; the notation for all tables is explained in Table 2.) The basic sanity check we
performed here was to take as features the number
of occurrences of the verbs ‘rt’, ‘retweet’, ‘please’,
‘spread’, ‘pls’, and ‘plz’ to capture explicit requests (e.g. “please retweet”).
Informativeness helps. (Table 4) Messages that
are more informative have increased social exchange value (Homans, 1958), and so may be
more worth propagating. One crude approximation of informativeness is length, and we see that
length helps.14 In contrast, Simmons et al. (2011)
found that shorter versions of memes are more
likely to be popular. The difference may result
from TAC-pair changes being more drastic than
the variations that memes undergo.
A more refined informativeness measure is
counts of the parts of speech that correspond
to content. Our POS results, gathered using a
Twitter-specific tagger (Gimpel et al., 2011), echo
those of Ashok et al. (2013) who looked at predict14
Of course, simply inserting garbage isn’t going to lead
to more retweets, but adding more information generally involves longer text.
Table 5: Conformity to the community and one’s
own past, measured via scores assigned by various
language models.
twitter unigram
twitter bigram
personal unigram
personal bigram
YES (54%)
YES (52%)
YES (52%)
NO– (48%)
ing the success of books. The diminished effect of
hashtag inclusion with respect to what has been reported previously (Suh et al., 2010; Petrović et al.,
2011) presumably stems from our topic and author
Be like the community, and be true to yourself
(in the words you pick, but not necessarily in
how you combine them). (Table 5) Although distinctive messages may attract attention, messages
that conform to expectations might be more easily accepted and therefore shared. Prior work has
explored this tension: Lakkaraju et al. (2013), in a
content-controlled study, found that the more upvoted Reddit image titles balance novelty and familiarity; Danescu-Niculescu-Mizil et al. (2012)
(henceforth DCKL’12) showed that the memorability of movie quotes corresponds to higher lexical distinctiveness but lower POS distinctiveness;
and Sun et al. (2013) observed that deviating from
one’s own past language patterns correlates with
more retweets.
Keeping in mind that the authors in our data
have at least 5000 followers15 , we consider two
types of language-conformity constraints an author might try to satisfy: to be similar to what
is normal in the Twitter community, and to be
similar to what his or her followers expect. We
measure a tweet’s similarity to expectations by its
according to the relevant language model,
1 ř
xPT logpppxqq, where T refers to either all
|T |
the unigrams (unigram model) or all and only bigrams (bigram model).16 We trained a Twittercommunity language model from our 558M unpaired tweets, and personal language models from
each author’s tweet history.
Imitate headlines. (Table 6) News headlines are
often intentionally written to be both informative
and attention-getting, so we introduce the idea of
This is not an artificial restriction on our set of authors; a
large follower count means (in principle) that our results draw
on a large sample of decisions whether to retweet or not.
The tokens [at], [hashtag], [url] were ignored in the
unigram-model case to prevent their undue influence, but retained in the bigram model to capture longer-range usage
(“combination”) patterns.
Table 6: LM-based resemblance to headlines.
headline unigram
headline bigram
YES (53%)
YES (52%)
Table 7: Retweet score.
rt score
verb rt score
noun rt score
adjective rt score
adverb rt score
proper noun rt score
NO– (49%)
YES (50%)
YES (51%)
NO– (48%)
scoring by a language model built from New York
Times headlines.17
Use words associated with (non-paired)
retweeted tweets. (Table 7) We expect that
provocative or sensationalistic tweets are likely
to make people react. We found it difficult to
model provocativeness directly. As a rough
approximation, we check whether the changes in
t2 with respect to t1 (which share the same topic
and author) involve words or parts-of-speech that
are associated with high retweet rate in a very
large separate sample of unpaired tweets (retweets
and replies discarded). Specifically, for each word
w that appears more than 10 times, we compute
the probability that tweets containing w are
retweeted more than once, denoted by rspwq. We
define the rt score of a tweet as maxwPT rspwq,
where T is all the words in the tweet, and the
rt score of a particular POS tag z in a tweet as
maxwPT &tagpwq“z rspwq.
Include positive and/or negative words. (Table 8) Prior work has found that including positive or negative sentiment increases message propagation (Milkman and Berger, 2012; Godes et al.,
2005; Heath et al., 2001; Hansen et al., 2011). We
measured the occurrence of positive and negative
words as determined by the connotation lexicon
of Feng et al. (2013) (better coverage than LIWC).
Measuring the occurrence of both simultaneously
was inspired by Riloff et al. (2013).
Refer to other people (but not your audience).
(Table 9) First-person has been found useful for
success before, but in the different domains of scientific abstracts (Guerini et al., 2012) and books
(Ashok et al., 2013).
To test whether the results stem from similarity to news
rather than headlines per se, we constructed a NYT-text LM,
which proved less effective. We also tried using Gawker
headlines (often said to be attention-getting) but pilot studies
revealed insufficient vocabulary overlap with our TAC pairs.
Table 8: Sentiment (contrast is measured by presence of both positive and negative sentiments).
Table 9: Pronouns.
1st person singular
1st person plural
2nd person
3rd person singular
3rd person plural
YES (51%)
YES (52%)
YES (57%)
YES (55%)
YES (58%)
Generality helps. (Table 10) DCKL’12 posited
that movie quotes are more shared in the culture
when they are general enough to be used in multiple contexts. We hence measured the presence of
indefinite articles vs. definite articles.
The easier to read, the better. (Table 11) We
measure readability by using Flesch reading ease
(Flesch, 1948) and Flesch-Kincaid grade level
(Kincaid et al., 1975), though they are not designed for short texts. We use negative grade level
so that a larger value indicates easier texts to read.
Final question: Do authors prefer to do what
is effective? Recall that we use binomial tests to
determine author preference for applying a feature
more in t2 . Our preference statistics show that author preferences in many cases are aligned with
feature efficacy. But there are several notable exceptions: for example, authors tend to increase the
use of @-mentions and 2nd person pronouns even
though they are ineffective. On the other hand,
they did not increase the use of effective ones
like proper nouns and numbers; nor did they tend
to increase their rate of sentiment-bearing words.
Bearing in mind that changes in t2 may not always
be intended as an effort to improve t1 , it is still interesting to observe that there are some contrasts
between feature efficacy and author preferences.
Predicting the “better” wording
Here, we further examine the collective efficacy
of the features introduced in §5.1 via their performance on a binary prediction task: given a TAC
pair (t1 , t2 ), did t2 receive more retweets?
Our approach. We group the features introduced
in §5.1 into 16 lexicon-based features (Table 3,
8, 9, 10), 9 informativeness features (Table 4), 6
language model features (Table 5, 6), 6 rt score
features (Table 7), and 2 readability features (Table 11). We refer to all 39 of them together as
Table 10: Generality.
indefinite articles (a,an)
definite articles (the)
YES (52%)
Table 11: Readability.
reading ease
negative grade level
YES (52%)
YES (52%)
custom features. We also consider tagged bag-ofwords (“BOW”) features, which includes all the
unigram (word:POS pair) and bigram features that
appear more than 10 times in the cross-validation
data. This yields 3,568 unigram features and 4,095
bigram features, for a total of 7,663 so-called
1,2-gram features. Values for each feature are normalized by linear transformation across all tweets
in the training data to lie in the range r0, 1s.18
For a given TAC pair, we construct its feature
vector as follows. For each feature being considered, we compute its normalized value for each
tweet in the pair and take the difference as the feature value for this pair. We use L2-regularized logistic regression as our classifier, with parameters
chosen by cross validation on the training data.
(We also experimented with SVMs. The performance was very close, but mostly slightly lower.)
A strong non-TAC alternative, with social information and timing thrown in. One baseline result we would like to establish is whether the topic
and author controls we have argued for, while
intuitively compelling for the purposes of trying
to determine the best way for a given author to
present some fixed content, are really necessary
in practice. To test this, we consider an alternative binary L2-regularized logistic-regression classifier that is trained on unpaired data, specifically,
on the collection of 10,000 most retweeted tweets
(gold-standard label: positive) plus the 10,000
least retweeted tweets (gold-standard label: negative) that are neither retweets nor replies. Note
that this alternative thus is granted, by design,
roughly twice the training instances that our classifiers have, as a result of having roughly the same
number of tweets, since our instances are pairs.
Moreover, we additionally include the tweet author’s follower count, and the day and hour of
posting, as features. We refer to this alternative
classifier as TAC+ff+time. (Mnemonic: “ff” is
used in bibliographic contexts as an abbreviation
We also tried normalization by whitening, but it did not
lead to further improvements.
(a) Cross-validation and heldout accuracy for various feature sets. Blue lines inside (b) Cross-validation accuracy vs data size.
bars: performance when custom features are restricted to those that pass our Bon- Human performance was estimated from a
ferroni correction (no line for readability because no readability features passed). disjoint set of 100 pairs (see §4).
Dashed vertical line: TAC+ff+time performance.
Figure 2: Accuracy results. Pertinent significance results are as follows. In cross-validation, custom+1,2gram is significantly better than TAC+ff+time (p=0) and 1,2-gram (p=3.8e-7). In heldout validation,
custom+1,2-gram is significantly better than TAC+ff+time (p=3.4e-12) and 1,2-gram (p=0.01) but not
unigram (p=0.08), perhaps due to the small size of the heldout set.
for “and the following”.) We apply it to a tweet
pair by computing whether it gives a higher score
to t2 or not.
Baselines. To sanity-check whether our classifier
provides any improvement over the simplest methods one could try, we also report the performance
of the majority baseline, our request-for-sharing
features, and our character-length feature.
Performance comparison. We compare the accuracy (percentage of pairs whose labels were correctly predicted) of our approach against the competing methods. We report 5-fold cross validation
results on our balanced set of 11,404 TAC pairs
and on our completely disjoint heldout data19 of
1,770 TAC pairs; this set was never examined during development, and there are no authors in common between the two testing sets.
Figure 2(a) summarizes the main results. While
TAC+ff+time outperforms the majority baseline, using all the features we proposed beats
TAC+ff+time by more than 10% in both crossvalidation (66.5% vs 55.9%) and heldout validation (65.6% vs 55.3%). We outperform the average human accuracy of 61% reported in our Amazon Mechanical Turk experiments (for a different
data sample); TAC+ff+time fails to do so.
The importance of topic and author control can be seen by further investigation of
TAC+ff+time’s performance. First, note that
To construct this data, we used the same criteria as in
§3: written by authors with more than 5000 followers, posted
within 12 hours, n2 ´ n1 ě 10 or ď ´15, and cosine similarity threshold value the same as in §3, cap of 50 on number
of pairs from any individual author.
it yields an accuracy of around 55% on our
alternate-version-selection task,20 even though its
cross-validation accuracy on the larger most- and
least-retweeted unpaired tweets averages out to a
high 98.8%. Furthermore, note the superior performance of unigrams trained on TAC data vs
TAC+ff+time — which is similar to our unigrams but trained on a larger but non-TAC dataset
that included metadata. Thus, TAC pairs are a useful data source even for non-custom features. (We
also include individual feature comparisons later.)
Informativeness is the best-performing custom
feature group when run in isolation, and outperforms all baselines, as well as TAC+ff+time;
and we can see from Figure 2(a) that this is not
due just to length. The combination of all our 39
custom features yields approximately 63% accuracy in both testing settings, significantly outperforming informativeness alone (pă0.001 in both
cases). Again, this is higher than our estimate of
average human performance.
Not surprisingly, the TAC-trained BOW features (unigram and 1,2-gram) show impressive
predictive power in this task: many of our custom
features can be captured by bag-of-word features,
in a way. Still, the best performance is achieved
One might suspect that the problem is that
TAC+ff+time learns from its training data to overrely on follower-count, since that is presumably a good
feature for non-TAC tweets, and for this reason suffers when
run on TAC data where follower-counts are by construction
non-informative. But in fact, we found that removing the
follower-count feature from TAC+ff+time and re-training
did not lead to improved performance. Hence, it seems that
it is the non-controlled nature of the alternate training data
that explains the drop in performance.
by combining our custom and 1,2-gram features
together, to a degree statistically significantly better than using 1,2-gram features alone.
Finally, we remark on our Bonferroni correction. Recall that the intent of applying it is to
avoid false positives. However, in our case, Figure 2(a) shows that our potentially “false” positives — features whose effectiveness did not pass
the Bonferroni correction test — actually do raise
performance in our prediction tests.
Size of training data. Another interesting observation is how performance varies with data size.
For n “ 1000, 2000, . . . , 10000, we randomly
sampled n pairs from our 11,404 pairs, and computed the average cross-validation accuracy on the
sampled data. Figure 2(b) shows the averages over
50 runs of the aforementioned procedure. Our custom features can achieve good performance with
little data, in the sense that for sample size 1000,
they outperform BOW features; on the other hand,
BOW features quickly surpass them. Across the
board, the custom+1,2-gram features are consistently better than the 1,2-gram features alone.
Top features. Finally, we examine some of
the top-weighted individual features from our approach and from the competing TAC+ff+time
classifier. The top three rows of Table 12 show the
best custom and best and worst unigram features
for our method; the bottom two rows show the best
and worst unigrams for TAC+ff+time. Among
custom features, we see that community and personal language models, informativeness, retweet
scores, sentiment, and generality are represented.
As for unigram features, not surprisingly, “rt” and
“retweet” are top features for both our approach
and TAC+ff+time. However, the other unigrams
for the two methods seem to be a bit different in
spirit. Some of the unigrams determined to be
most poor only by our method appear to be both
surprising and yet plausible in retrospect: “icymi”
(abbreviation for “in case you missed it”) tends to
indicate a direct repetition of older information,
so people might prefer to retweet the earlier version; “thanks” and “sorry” could correspond to
personal thank-yous and apologies not meant to
be shared with a broader audience, and similarly
@-mentioning another user may indicate a tweet
intended only for that person. The appearance of
[hashtag] in the best TAC+ff+time unigrams is
consistent with prior research in non-TAC settings
(Suh et al., 2010; Petrović et al., 2011).
Table 12: Features with largest coefficients, delimited by commas. POS tags omitted for clarity.
Our approach
best 15 custom
twitter bigram, length (chars), rt
(the word), retweet (the word), verb, verb retweet score,
personal unigram, proper noun, number, noun, positive
words, please (the word), proper noun retweet score,
indefinite articles (a,an), adjective
best 20 unigrams
rt, retweet, [num], breaking,
is, win, never, ., people, need, official, officially, are,
please, november, world, girl, !!!, god, new
worst 20 unigrams
:, [at], icymi, also, comments,
half, ?, earlier, thanks, sorry, highlights, bit, point, update, last, helping, peek, what, haven’t, debate
best 20 unigrams
[hashtag], teen, fans, retweet,
sale, usa, women, butt, caught, visit, background, upcoming, rt, this, bieber, these, each, chat, houston, book
worst 20 unigrams
:, ..., boss, foundation, ?, „,
others, john, roll, ride, appreciate, page, drive, correct,
full, ’, looks, @ (not as [at]), sales, hurts
In this work, we conducted the first large-scale
topic- and author-controlled experiment to study
the effects of wording on information propagation.
The features we developed to choose the better of two alternative wordings posted better performance than that of all our comparison algorithms, including one given access to author and
timing features but trained on non-TAC data, and
also bested our estimate of average human performance. According to our hypothesis tests, helpful wording heuristics include adding more information, making one’s language align with both
community norms and with one’s prior messages,
and mimicking news headlines. Readers may
try out their own alternate phrasings at http:
// to see
what a simplified version of our classifier predicts.
In future work, it will be interesting to examine
how these features generalize to longer and more
extensive arguments. Moreover, understanding
the underlying psychological and cultural mechanisms that establish the effectiveness of these features is a fundamental problem of interest.
Acknowledgments. We thank C. Callison-Burch,
C. Danescu-Niculescu-Mizil, J. Kleinberg, P.
Mahdabi, S. Mullainathan, F. Pereira, K. Raman,
A. Swaminathan, the Cornell NLP seminar participants and the reviewers for their comments; J.
Leskovec for providing some initial data; and the
anonymous annotators for all their labeling help.
This work was supported in part by NSF grant IIS0910664 and a Google Research Grant.
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