  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
social-media 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
non-controlled data.
