In the midst of an election year, I’m reminded of the power of the political speech. It can inspire, spur people to action and even alter the course of history. Many of us are familiar from history class with John F. Kennedy’s appeal to Americans to ask what they can do for their country or Ronald Reagan’s call for the Soviet Union to “Tear down this wall!” But what is the context behind these often mythic speeches? Why were they given in the first place? Did they achieve their intended effect and how do they continue to impact us today?
JSTOR provides a rich corpus of scholarship to help answer these questions. For example, in The Volunteering Decision: What Prompts It? What Sustains It? by Paul C. Light, I discovered that President Kennedy’s soaring call for volunteerism prompted subsequent presidents to promote service and even create new government programs to support it. With this in mind, Labs sought to create a prototype tool that could help students and the general public discover the context behind some of the most important presidential speeches in U.S. history.
An early prototype matching quotes from some presidential speeches can be seen at http://labs.jstor.org/presidential-speeches
Using the JSTOR Labs Matchmaker algorithm, we matched famous lines in important presidential speeches to content in the JSTOR corpus. With the data in hand, we sought to try something different interface wise – we are labs after all! Our idea was to use the algorithmically created data and present it in a way that told more of a story than would be possible with simply a list of results. This presentation included incorporating high quality images of each president, the date and location of the speech and visual emphasis on the quote itself. Additionally, to support the narrative of a story, we limited the set of results to fewer than ten and included a featured article. The philosophy behind this choice was to emphasize rapid discovery and contextual learning of the scholarship rather than pure research. The choice to include a featured article is especially important as it gives the user one focal point to approach the scholarship that can transform into greater interest.
To narrow the matched articles took some experimentation. In one method, we ranked articles based on how their top-weighted key terms compared to the aggregated top-weighted key terms of a speech’s full body of related articles, or in other words, how similar an article was to the most prevalent themes and topics (e.g. political protest) of the greater body of articles. Another method ranked articles for a speech by a similarity score which represents how close the matched text is to the quoted line. We also experimented with logistic regression using a training data-set explicitly labeled with relevancy by hand. The end result of these efforts is a curated set of articles that provides solid context to each presidential speech.
An important next step for this project is user-testing – both for the interface as well as the hypothesis behind the story-driven design. Users provide valuable feedback and can help us understand better how to design stories and rich interfaces using the JSTOR corpus of millions of scholarly articles.