  Online social media has been a popular source for people to consume and share
news content. More recently, the spread of misinformation online has caused
widespread concerns. In this work, we focus on a critical task of detecting
fauxtography on social media where the image and associated text together
convey misleading information. Many efforts have been made to mitigate
misinformation online, but we found that the fauxtography problem has not been
fully addressed by existing work. Solutions focusing on detecting fake images
or misinformed texts alone on social media often fail to identify the
misinformation delivered together by the image and the associated text of a
fauxtography post. In this paper, we develop FauxWard, a novel graph
convolutional neural network framework that explicitly explores the complex
information extracted from a user comment network of a social media post to
effectively identify fauxtography. FauxWard is content-free in the sense that
it does not analyze the visual or textual contents of the post itself, which
makes it robust against sophisticated fauxtography uploaders who intentionally
craft image-centric posts by editing either the text or image content. We
evaluate FauxWard on two real-world datasets collected from mainstream social
media platforms (i.e., Reddit and Twitter). The results show that FauxWard is
both effective and efficient in identifying fauxtography posts on social media.
