Finding and Exploring Memes in Social Media

摘要

Critical literacy challenges us to question how what we read has been shaped by external context, especially when information comes from less established sources. While crosschecking multiple sources provides a foundation for critical literacy, trying to keep pace the constant deluge of new online information is a daunting proposition, especially for casual readers. To help address this challenge, we propose a new form of technological assistance which automatically discovers and displays underlying memes: 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 context. 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 process 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 architecture for meme discovery optimized for Hadoop [25]. Our source code and Meme Browser are both available online.