Social media platforms now allow users to share images
alongside their textual posts. These image tweets make up a
fast-growing percentage of tweets, but have not been studied
in depth unlike their text-only counterparts.
We study a large corpus of image tweets in order to uncover what people post about and the correlation between
the tweet's image and its text. We show that an important functional distinction is between visually-relevant and
visually-irrelevant tweets, and that we can successfully build
an automated classier utilizing text, image and social context features to distinguish these two classes, obtaining a
macro F1 of 70.5%.