Mining Web Query Logs to Analyze Political Issues

We present a novel approach to using anonymized web search query logs to analyze and visualize political issues. Our starting point is a list of politically annotated blogs (left vs. right). We use this list to assign a numerical political leaning to queries leading to clicks on these blogs. Furthermore, we map queries to Wikipedia articles and to fact-checked statements from politifact.com, as well as applying sentiment analysis to their search results. With this rich, multi-faceted data set we can obtain novel graphical visualizations of issues as well as discover connections between the different variables.
Our findings include (i) an interest in "the other side'' where queries about Democrat politicians have a right leaning and vice versa, (ii) evidence that "lies are catchy'' and that queries pertaining to false statements are more likely to attract large volumes, and (iii) the observation that the more right-leaning a query it is, the more negative sentiments can be found in its search results.