Critical literacy challenges us to question how what we read has been shaped by external context, especially when infor-mation comes from less established sources. While cross-checking multiple sources provides a foundation for critical literacy, trying to keep pace the constant deluge of new on-line information is a daunting proposition, especially for ca-sual readers. To help address this challenge, we propose a new form of technological assistance which automatically discovers and displays underlyingmemes: ideas embodied by similar phrases which are found in multiple sources. Once detected, these underlying memes are revealed to users via generated hypertext, allowing memes to be explored in con-text. Given the massive volume of online information today, we propose a highly-scalable system architecture based on MapReduce, extending work by Kolak and Schilit [11]. To validate our approach, we report on using our system to pro-cess and browse a 1.5 TB collection of crawled social media. Our contributions include a novel technological approach to support critical literacy and a highly-scalable system archi-tecture for meme discovery optimized for Hadoop [25]. Our source code and Meme Browser are both available online
