Understanding public opinion on complex controversial issues such as ‘Legalization of Marijuana’ is of considerable importance for a number of objectives. However, an individual’s position on a controversial issue is often not a binary support-or-oppose stance on the issue, but rather a conglomerate of nuanced opinions on various aspects of the issue. These opinions are often expressed qualitatively in free text in surveys or on social media. However, quantifying vast amounts of qualitative information remains a significant challenge. The goal of this work is to provide a new approach for quantifying qualitative data for the understanding of controversial issues. First, we show how we can engage people directly through crowdsourcing to create a comprehensive dataset of assertions (claims, opinions, etc.) relevant to an issue. Next, the assertions are judged for agreement and strength of support or opposition. The collected Dataset of Nuanced Assertions on Controversial Issues (NAoCI dataset) consists of over 2,000 assertions on sixteen different controversial issues. It has over 100,000 judgments of whether people agree or disagree with the assertions, and of about 70,000 judgments indicating how strongly people support or oppose the assertions. This dataset allows for several useful analyses that help summarize public opinion.
@InProceedings{WOJATZKI18.321, author = {Michael Wojatzki and Saif Mohammad and Torsten Zesch and Svetlana Kiritchenko}, title = "{Quantifying Qualitative Data for Understanding Controversial Issues}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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