Please join us for our NLP Seminar next Monday, April 16, at 4:00pm in 202 South Hall.
Speaker: Amber Boydstun (Associate Professor of Political Science, UC Davis)
Title: How Surges in Dominant Media Narratives Move Public Opinion
Studies examining the potential effects of media coverage on public attitudes toward policy issues (e.g., abortion, capital punishment) have identified three variables that, depending on the issue, can wield significant influence: the tone of the coverage (positive/negative/neutral), the frames used (e.g., discussing the issue from an economic vs. a moral perspective), and the overall level of media attention to the issue. Yet, to date, no study has examined all three variables in combination. We fill this gap by building a theoretical argument for why, despite the important variance across different issues, in general a single measure should be able to predict significant shifts in public opinion: surges in media attention to “dominant media narratives,” or stories that consistently frame the issue the same way (e.g., economic) using the same tone (e.g., anti-immigration) relative to other competing narratives. We test this hypothesis in U.S. newspaper coverage to three very different policy issues—immigration, same-sex marriage, and gun control—from 1992 to 2012. We use manual content analysis linked with computational modeling, tracking tone (pro/anti/neutral), emphasis frames (e.g., economic, morality), and overall levels of attention. Using time series analysis of public opinion data, we show that, for all three issues, previous surges in dominant media narratives significantly shape opinion. In short, when media coverage converges around a unified way of describing a policy issue, the public tends to follow. Our study adds to the fields of political communication and public opinion and marks an advance in computational text analysis methods. (Joint work with Dallas Card and Noah Smith)