Michael Dougal is a Ph.D. candidate in American politics. His dissertation uses text-based machine learning to examine whether the press fosters democratic accountability by sounding the alarm on out of step representatives and alerting otherwise inattentive voters that it is time for change. He shows that newspapers do not sound the alarm on out of step House incumbents, but instead provide representatives who vote against a majority of their constituents on important legislation with the same overwhelmingly neutral coverage as more faithful representatives.
He is currently working on using text-based machine learning to provide real time analytics of media coverage across the political Internet. His 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. His 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).