From political manifestos to social networksThe automation of political discourse analysis using contextual information

Zusammenfassung

Due to the rise of the social networks, political parties and politicians have found new ways of establishing their position on an issue apart from traditional political manifestos. From this phenomenon, a new research area has emerged, the automation of political discourse analysis on Social Networks. To do so, this PhD dissertation has taken advantage of a widely used content analysis methodology for political manifestos, The Manifesto Project. With annotated manifestos since 2001, this methodology uses a codification which allows the analysis of political parties policy preferences regarding 56 topics, providing the scientific community with parties’ policy positions derived from the content analysis. Therefore, this PhD dissertation focuses on two main tasks: firstly, to automate the annotation process of political manifestos, in order to facilitate that same process to political scientists and secondly, to use this model as a basis to perform a political discourse analysis on Twitter using the previously mentioned Manifesto Project's methodology. To do so, we have taken advantage of two types of contextual information available in the two circumstances of the application of this research work: manifestos and Twitter. The first contextual data is what has been said previously, in the case of election manifestos the previous phrase or statement, and on twitter the preceding tweet. The second contextual information is which political party is the sender of the statement. Regarding the use of contextual information in order to improve manifestos automated classification, we have improved state of the art results in 4 out of 7 languages. With regard to Tweets' classification, we can affirm that annotated manifestos can be used as complementary data for this task, being the fine-tuned model with annotated tweets the best performing one. Moreover, contextual information does also improve the performance of the models when tweets are classified. Using this approach, we have analysed the 2016 United States presidential elections on Twitter.