"Chinese Virus” as Anchor for Engaging with COVID-19 Information: Anchoring Bias Leading to Racism and Xenophobia

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

Analysis of Public Perception of Multiple Community Issues through Social Media Mining during a Pandemic

The COVID-19 pandemic affected almost every aspect of our lives. It rapidly changed the way we behave in our daily lives, including how we seek and access information. Social media has become pivotal for accessing information about the pandemic, though not all information available is reliable. Therefore, this study uses a social media mining approach to analyze the public’s sentiment during COVID-19 pandemic through social media posts (e.g. Twitter). Social media mining is crucial for understanding information behavior of individuals in a time when collective action is essential. Data is being collected through tweets streaming using terms related to coronavirus (“coronavirus” and “covid19”), and limited to tweets within the USA. Additionally, analysis on the aggregated tweets to understand emotional content of tweets was conducted alongside visual content (memes) related to the pandemic, which were collected for content analysis. Text mining and sentiment analysis serve as an avenue for understanding implicit meaning in social media posts, thus furthering a more complete understanding of messages transmitted via social media related to COVID-19. The analysis will be correlated with other aspects, such as timeline and pertinent activities. Understanding the process for collecting social media data during a world crisis (pandemic), creates a context where social media data can be analyzed through different perspectives, thus leading to a more in-depth understanding of efforts at communication about COVID-19 (education strategies, preventive behaviors, etc.), and the public’s response to the crisis