I am a Ph.D. candidate in American politics and political methodology. My dissertation develops methods of text-based machine learning to assess political coverage and better understand the ability of the media to monitor elected officials on behalf of voters. I am currently working on using text-based machine learning to provide real time analytics of media coverage across the political Internet. My other ongoing research examines changes in ideology over time.
One project draws on over 1 million individual poll respondents to measure public attitudes towards the New Deal at the state level from 1936-1952. Another project scales the text of present day campaign websites to measure whether candidates moderate their campaign message between the primary and the general election. My past research has examined whether citizens vote based on candidate appearance (they do) and whether people who donate to presidential campaigns have any close friends who donated to the opposing party’s candidate (they do not).
Want to see how a website talks about a particular keyword? Try Modeling Today's News yourself.
The same machine learning approach I use to measure candidate criticism can also be used to identify interesting news stories. Check out M(ichae)L's News.