@inproceedings{lukito-etal-2019-using,
title = "Using time series and natural language processing to identify viral moments in the 2016 {U}.{S}. Presidential Debate",
author = "Lukito, Josephine and
K Sarma, Prathusha and
Foley, Jordan and
Abhishek, Aman",
editor = "Volkova, Svitlana and
Jurgens, David and
Hovy, Dirk and
Bamman, David and
Tsur, Oren",
booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2107",
doi = "10.18653/v1/W19-2107",
pages = "54--64",
abstract = "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 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 encoder+ classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induce discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.",
}
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<abstract>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 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 encoder+ classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induce discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.</abstract>
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%0 Conference Proceedings
%T Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate
%A Lukito, Josephine
%A K Sarma, Prathusha
%A Foley, Jordan
%A Abhishek, Aman
%Y Volkova, Svitlana
%Y Jurgens, David
%Y Hovy, Dirk
%Y Bamman, David
%Y Tsur, Oren
%S Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F lukito-etal-2019-using
%X 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 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 encoder+ classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induce discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.
%R 10.18653/v1/W19-2107
%U https://aclanthology.org/W19-2107
%U https://doi.org/10.18653/v1/W19-2107
%P 54-64
Markdown (Informal)
[Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate](https://aclanthology.org/W19-2107) (Lukito et al., NLP+CSS 2019)
ACL