Technological developments, digital media, and advances in open government practices have made a vast amount of information available for social scientists. Most of this information is available as text. News portals disseminate political stories at unprecedented rates, politicians and political elites advertise their own messages through social media outlets and crowdsourcing provides new affordable and quick venues for asking citizens what they think about politics. With political texts at our fingertips, vexing research questions are emerging.
Extracting, organizing, and analyzing large amounts of textual information can be quite resource-intensive with many political scientists lacking the skills necessary for dealing with such data. Fortunately, recent developments of cutting edge computational technologies such as natural language-processing, machine learning, and information extraction techniques has made research utilizing text-as-data more accessible and appealing.
On the other hand, computational scholars equipped with novel technologies and linguistic solutions often have less experience with social science theories and less contextual knowledge about political data. There is a mutual benefit in connecting disparate worlds of computational text analysis and political science in analyzing political research problems. The aim of this conference is to facilitate this multidisciplinary cooperation.
We are interested in contributions on computational approaches in analysing political text such as government speeches, political debates, social media, media content, party manifestos and/or legislation.
This conference is looking for, but not limited to, contributions from the following topics with a focus on politically relevant data:
- text categorization
- topic modeling
- information extraction
- corpus analysis
- sentiment analysis
- stance classification and ideal point estimation
- argumentation mining
- political reputation analysis
- techniques for multilingual text analysis
- other language technologies