The number of news articles devoted to the US election is so large that no exhaustive analysis can be attempted by conventional means.
The US presidential election dominates the global media every four years, with news articles, which are carefully analysed by commentators and campaign strategists, playing a major role in shaping voter opinion.
Academics at the University of Bristol’s Intelligent Systems Laboratory have developed an online tool, Election Watch, which analyses the content of news about the US election by the international media.A paper about the project will be presented at the Proceedings of the 13th conference of the European Chapter of the Association for Computational Linguistics held in Avignon, France.
Election Watch automatically monitors political discourse about the 2012 US presidential election from over 700 American and international news outlets. The information displayed is based, so far, on 91,456 articles.
The web tool allows users to explore news stories via an interactive interface and demonstrates the application of modern machine learning and language technologies. After analysing news articles about the 2012 US election the researchers have found patterns in the political narrative.
The online site is updated daily, by presenting narrative patterns as they were extracted from news. Narrative patterns include actors, actions, triplets representing political support between actors, and automatically inferred political allegiance of actors.
The site also presents the key named entities, timelines and heat maps. Network analysis allows the researchers to infer the role of each actor in the general political discourse, recognising adversaries and allied actors. Users can browse articles by political statements, rather than by keywords. For example, users can browse articles where Romney is described as criticising Obama. All the graphical briefing is automatically generated and interactive and each relation presented to the user can be used to retrieve supporting articles, from a set of hundreds of online news sources.
Nello Cristianini , Professor of Artificial Intelligence, who is leading the project, said: "The number of news articles devoted to the US election is so large that no exhaustive analysis can be attempted by conventional means. Even if just focusing on the leading English-language outlets, there are hundreds of thousands of articles to analyse just for the primary phase. So any large-scale analysis of global coverage will necessarily need to make use of computational methods.
"However, most computational approaches to news content analysis are limited to sophisticated forms of keyword counting, be it for sentiment analysis, or topic detection, and relative statistical analysis. This will necessarily miss many aspects of the narration to which voters are exposed, and which may therefore be of interest to analysts."
The researchers aim was to access information that is closer to what a human analyst could extract, but still simple enough to be reliably extracted by computational means in a Big Data setting.
In this project, they automated techniques from Quantitative Narrative Analysis (QNA) so that they can be applied on a vast scale. This approach was aimed at identifying the actors and the actions that dominate a story, as well as basic units of narration: subject-verb-object triplets. While still very simple, this information captures a variety of relations that would be missed by classical means, and that are relevant to political discourse.
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