This class we begin our study of filtering with some basic ideas about its role in journalism. There’s just way too much information produced every day, more than any one person can read by a factor of millions. We need software to help us deal with this flood. In this lecture, we discuss purely algorithmic approaches to filtering, with a look at how the Newsblaster system works (similar to Google News.)
Topics: How bad information overload actually is. The Newsblaster system, a precursor to Google News. Clustering together stories on the same event. Sorting stories into topics. Personalization. The filter bubble, and the filter design problem.
- Who should see what when? Three design principles for personalized news, Jonathan Stray
- Tracking and summarizing news on a daily basis with Columbia Newsblaster, McKeown et al
- Are we stuck in filter bubbles? Here are five potential paths out, Jonathan Stray
- Guess what? Automated news doesn’t quite work, Gabe Rivera
- The Hermeneutics of Screwing Around, or What You Do With a Million Books, Stephen Ramsay
- Can an algorithm be wrong?, Tarleton Gillespie