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Session Overview
Paper Session 02: COVID-19 [SDG 3]
Sunday, 25/Oct/2020:
11:00am - 12:30pm

Session Chair: Rong Tang, Simmons College, United States of America

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11:00am - 11:15am
ID: 272 / PS-02: 1
Long Papers
Topics: Data Science; Analytics; and Visualization
Keywords: linguistic register, ISO12620, and clustering, visualization

Searching Covid-19 by Linguistic Register: Parallels and Warrant for a New Retrieval Model

Gerald Benoit

UC Berkeley, USA

Keeping informed given rapid trend in data and resources about covid-19 is a new challenge. Different user groups (researchers/doctors, practitioners, public) vary in linguistic expression and vocabulary so a new retrieval framework might likewise vary to improve retrieval, expose unanticipated concepts, and establish a sustainable research stream. In this project a document collection about covid-19 was created, parsed according to ISO12620’s definition of linguistic register, and retrieval sets compared. Results suggest trends from other fields parallel register-oriented criteria; project exposes unexpected concepts across groups, uses of visualization, and warrants ling-register as a sustainable IR research stream.

11:15am - 11:30am
ID: 336 / PS-02: 2
Long Papers
Topics: Library and Information Science
Keywords: COVID-19, Information flows, Information environment, Research, Practice

What We Can Learn from Information Flows About COVID-19: Implications for Research and Practice

Waseem Afzal

Charles Sturt University, Australia

COVID-19 has become a global pandemic affecting billions of people. Its impact on societies worldwide will be felt for years to come. The purpose of this research is to examine information flows about COVID-19 to understand the information-specific underpinnings that are shaping understandings of this crisis. As a starting point, this research analyzes information about COVID-19 from a selection of information sources, including the World Health Organization (WHO), the National Health Commission of the People’s Republic of China (NHCPRC), and three news outlets with vast global coverage. The analysis reveals some distinctive information underpinnings about COVID-19, including 1) flows of information becoming regular and larger around certain dates, 2) preponderance of information imperfections such as incomplete information, misinformation, and disinformation, and 3) absence of information about some key turning points. The implications of these information imperfections in that they create information failures and, hence, ineffective approaches to dealing with this crisis warrant further investigation.

11:30am - 11:45am
ID: 350 / PS-02: 3
Long Papers
Topics: Social Media and Social Computing
Keywords: Information diffusion, network analysis, epidemic modeling, social media, COVID-19

COVID-19 Epidemic and Information Diffusion Analysis on Twitter

Ly Dinh, Nikolaus Parulian

University of Illinois at Urbana-Champaign, USA

The COVID-19 pandemic has impacted all aspects of our life, including the information spread on social media. Prior literature has found that information diffusion dynamics on social networks mirror that of a virus, but applying the epidemic Susceptible-Infected-Removed model (SIR) model to examine how information spread is not sufficient to claim that information spreads like a virus. In this study, we explore whether there are similarities in the simulated SIR model (SIRsim), observed SIR model based on actual COVID-19 cases (SIRemp), and observed information cascades on Twitter about the virus (INFOcas) by using network analysis and diffusion modeling. We propose three primary research questions: (1) What are the diffusion patterns of COVID-19 virus spread, based on SIRsim and SIRemp?; (2) What are the diffusion patterns of information cascades on Twitter (INFOcas), with respect to retweets, quote tweets, and replies?; and (3) What are the major differences in diffusion patterns between SIRsim, SIRemp, and INFOcas? Our study makes a contribution to the information sciences community by showing how epidemic modeling of virus and information diffusion analysis of online social media are distinct but interrelated concepts.

11:45am - 11:55am
ID: 155 / PS-02: 4
Short Papers
Topics: Social Media and Social Computing
Keywords: topic modeling, COVID-19, Twitter, temporal differences

Uncovering Temporal Differences in COVID-19 Tweets

Han Zheng, Dion Hoe-Lian Goh, Chei Sian Lee, Edmund Lee, Yin Leng Theng

Nanyang Technological University, Singapore

In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people’s discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light.

11:55am - 12:05pm
ID: 316 / PS-02: 5
Short Papers
Topics: Social Media and Social Computing
Keywords: COVID-19, Coronavirus, Pandemic, Twitter, Hate Speech

Stigmatization in Social Media: Documenting and Analyzing Hate Speech for COVID-19 on Twitter

Lizhou Fan1,2, Huizi Yu2,4, Zhanyuan Yin3,4

1Program in Digital Humanities, UCLA,USA; 2Department of Statistics, UCLA, USA; 3Department of Mathematics, UCLA, USA; 4Department of Economics, UCLA, USA

As the COVID-19 pandemic has unfolded, Hate Speech on social media about China and Chinese people has encouraged social stigmatization. For the historical and humanistic purposes, this history-in-the-making needs to be archived and analyzed. Using the query “china+and+coronavirus” to scrape from the Twitter API, we have obtained 3,457,402 key tweets about China relating to COVID-19. In this archive, in which about 40% of the tweets are from the U.S., we identify 25,467 Hate Speech occurrences and analyze them according to lexicon-based emotions and demographics using machine learning and network methods. The results indicate that there are substantial associations between the amount of Hate Speech and demonstrations of sentiments, and state demographics factors. Sentiments of surprise and fear associated with poverty and unemployment rates are prominent. This digital archive and the related analyses are not simply historical, therefore. They play vital roles in raising public awareness and mitigating future crises. Consequently, we regard our research as a pilot study in methods of analysis that might be used by other researchers in various fields.

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