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Title:"Chinese Virus” as Anchor for Engaging with COVID-19 Information: Anchoring Bias Leading to Racism and Xenophobia
Author(s):Muhamad, Juan S.; Muhamad, Jessica Wendorf; Tian, Meng; Günaydın, Fatih; Merle, Patrick; Huse, Laura-Kate; Wibowo, Muhamad P.; Aghrazi, Maedeh
Subject(s):Anchoring-bias
Social media mining
Public preference
Pandemic
Text mining
Sentiment analysis
Abstract:Information dissemination from official sources coupled with adoption of message by the public during a pandemic crisis (COVID- 19) are essential components of collective action aimed at combating virus spread. During the onset of the COVID-19 crisis in the USA, President Donald Trump referred to the Coronavirus outbreak as a result of a “Chinese virus.” The president justified his choice of words given that the virus “originated in China.” Although indeed the virus was reported as originating in Wuhan, China, concerns about the use of the term and xenophobic/racist feelings emerged as a result. Considering that individuals are constantly engaging with information about the severe repercussion of the pandemic; social distancing, constant hand washing, disinfecting surfaces, economic consequences of rapid spread, increased death toll, and changes in our modus vivendi, for example, labeling the pandemic might result in anchoring bias. Anchoring bias is a consequence of random and at times uninformed outset (initial information) influencing perception of subsequent information. Therefore, when individuals attempt to adjust to new information, features of the anchor (initial information) to make judgements of new evidence persist. Thus, “Chinese virus” might inform attitudes towards new information presented on social media. In order to understand repercussions of labeling the pandemic, data is being collected via Tweet stream about COVID-19 to understand emotional content of tweets (emotional content analysis). Terms used to define criteria include “coronavirus,” “corona virus,” “covid-19,” “covid19,” and “Chinese,” “Chinese-virus.” Additionally, by using location-based tweets, scope was limited to tweets within the USA.
Issue Date:2020-10-13
Series/Report:Information Use
Social Media
Data Mining
Big Data
Natural Language Processing
Data Visualization
Genre:Conference Poster
Type:Text
URI:http://hdl.handle.net/2142/108774
Date Available in IDEALS:2020-10-09


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