Understanding Visual Memes: An Empirical Analysis of Text Superimposed on Memes Shared on Twitter - Du, Yuhao; Masood, Muhammad Aamir; Joseph, Kenneth (Proceedings of the International AAAI Conference on Web and Social Media, 2020)
Visual memes have become an important mechanism through which ideologically potent and hateful content spreads on today's social media platforms. At the same time, they are also a mechanism through which we convey much more mundane things, like pictures of cats with strange accents. Little is known, however, about the relative percentage of visual memes shared by real people that fall into these, or other, thematic categories. The present work focuses on visual memes that contain superimposed text. We carry out the first large-scale study on the themes contained in the text of these memes, which we refer to as image-with-text memes. We find that 30% of the image-with-text memes in our sample which have identifiable themes are politically relevant, and that these politically relevant memes are shared more often by Democrats than Republicans. We also find disparities in who expresses themselves via image-with-text memes, and images in general, versus other forms of expression on Twitter. The fact that some individuals use images with text to express themselves, instead of sending a plain text tweet, suggests potential consequences for the representativeness of analyses that ignore text contained in images.