1. PURE
Learning Plan
Richard Lee, James Chen,

2. Project Overview Mentored by PhD candidate Jason Cho
Advice from Professor Eric Meyer of the University of Illinois Department of Journalism
Project in automatic bias-detection in newspaper articles
Split into two parts:
Similar article topic recognition
Easier, but less interesting
Automatic bias detection
Harder, but more interesting

3. What Have We Learned? This is a very, very difficult problem.
Project involves a variety of fields, including computer science, English, linguistics, mathematics, journalism, etc.
Relatively unique problem, no thorough solution has been attempted/researched
Similar research done, such as sarcasm detection, bias research (linguistics), etc.

4. Getting Up to Speed Tools Papers OpenNLP, Python NLTK
Stanford NLP Toolkit
Papers
Shedding (a Thousand Points of) Light on Biased Language
Recognizing stances in online debates
Extracting opinion targets in a single-and cross-domain setting with conditional random fields

5. Ideas
Investigating words that have a high correlation with bias, and seeing if the presence of them indicate bias
Take presumably biased articles, use named- entity recognition to parse relevant sentences (eg, all sentences with "Obama" present)
Use a combination of word-counting, part-of- speech tagging, and word sentiment determination to aggregate overall bias.