Using time series and natural language processing to identify viral moments in the 2016

Josephine Lukito, Prathusha K Sarma, Jordan Foley, Aman Abhishek
2019 Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science   unpublished
This paper proposes a method for identifying and studying viral moments or highlights during a political debate. Using a combined strategy of time series analysis and domain adapted word embeddings, this study provides an in-depth analysis of several key moments during the 2016 U.S. Presidential election. First, a time series outlier analysis is used to identify key moments during the debate. These moments had to result in a long-term shift in attention towards either Hillary Clinton or Donald
more » ... Clinton or Donald Trump (i.e., a transient change outlier or an intervention, resulting in a permanent change in the time series). To assess whether these moments also resulted in a discursive shift, two corpora are produced for each potential viral moment (a pre-viral corpus and post-viral corpus). A domain adaptation layer learns weights to combine a generic and domain specific (DS) word embedding into a domain adapted (DA) embedding. Words are then classified using a generic en-coder+classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induced discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.
doi:10.18653/v1/w19-2107 fatcat:yrqlondehbamrpo43rfo3fj74a