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Author

Sarah Shugars

Other affiliations: Northeastern University
Bio: Sarah Shugars is an academic researcher from New York University. The author has contributed to research in topics: Social media & Politics. The author has an hindex of 6, co-authored 15 publications receiving 104 citations. Previous affiliations of Sarah Shugars include Northeastern University.

Papers
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Journal ArticleDOI
TL;DR: A predictive model of debate is proposed that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two, and finds that winning sides employ stronger arguments.
Abstract: Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%) Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments

40 citations

Proceedings ArticleDOI
01 Jun 2018
TL;DR: A statistical model is proposed that jointly captures topics for representing user interests and conversation content, and discourse modes for describing user replying behavior and conversation dynamics that outperforms methods that only model content without considering discourse.
Abstract: Millions of conversations are generated every day on social media platforms. With limited attention, it is challenging for users to select which discussions they would like to participate in. Here we propose a new method for microblog conversation recommendation. While much prior work has focused on post-level recommendation, we exploit both the conversational context, and user content and behavior preferences. We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics. Experimental results on two Twitter datasets demonstrate that our system outperforms methods that only model content without considering discourse.

38 citations

Journal ArticleDOI
TL;DR: In the absence of clear, consistent guidelines about the COVID-19 pandemic in the United States, many people use social media to learn about the virus, public health directives, vaccine distributio...
Abstract: In the absence of clear, consistent guidelines about the COVID-19 pandemic in the United States, many people use social media to learn about the virus, public health directives, vaccine distributio...

23 citations

Journal ArticleDOI
TL;DR: It is shown that different subpopulations preferentially amplify elites that are demographically similar to them, and that they crowdsource different types of elite accounts, such as journalists, elected officials, and medical professionals, in different proportions.
Abstract: The ongoing, fluid nature of the COVID-19 pandemic requires individuals to regularly seek information about best health practices, local community spreading, and public health guidelines. In the absence of a unified response to the pandemic in the United States and clear, consistent directives from federal and local officials, people have used social media to collectively crowdsource COVID-19 elites, a small set of trusted COVID-19 information sources. We take a census of COVID-19 crowdsourced elites in the United States who have received sustained attention on Twitter during the pandemic. Using a mixed methods approach with a panel of Twitter users linked to public U.S. voter registration records, we find that journalists, media outlets, and political accounts have been consistently amplified around COVID-19, while epidemiologists, public health officials, and medical professionals make up only a small portion of all COVID-19 elites on Twitter. We show that COVID-19 elites vary considerably across demographic groups, and that there are notable racial, geographic, and political similarities and disparities between various groups and the demographics of their elites. With this variation in mind, we discuss the potential for using the disproportionate online voice of crowdsourced COVID-19 elites to equitably promote timely public health information and mitigate rampant misinformation.

23 citations

Journal ArticleDOI
TL;DR: Individuals acquire increasingly more of their political information from social media, and ever more of that online time is spent in interpersonal, peer-to-peer communication and conversation as mentioned in this paper.
Abstract: Individuals acquire increasingly more of their political information from social media, and ever more of that online time is spent in interpersonal, peer-to-peer communication and conversation. Yet...

18 citations


Cited by
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01 Jan 2013

1,098 citations

01 Jan 2016
TL;DR: In this paper, the authors present a psychological study of groupthink in foreign policy decisions and fiascoes, which they call "Victims of Groupthink" and "Fiascoes".
Abstract: Thank you for reading victims of groupthink a psychological study of foreign policy decisions and fiascoes. As you may know, people have look hundreds times for their chosen readings like this victims of groupthink a psychological study of foreign policy decisions and fiascoes, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious bugs inside their computer.

389 citations