Using Twitter to Track Immigration Sentiment During Early Stages of the COVID-19 Pandemic

Abstract

In 2020, the world faced an unprecedented challenge to tackle and understand the spread and impacts of COVID-19. Large-scale coordinated efforts have been dedicated to understand the global health and economic implicationsof the pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations, particularlymigrants and individuals of Asian descent, has largely been neglected. Understanding public attitudes towardsmigration is essential to counter discrimination against immigrants and promote social cohesion. Traditional datasources to monitor public opinion – ethnographies, interviews, and surveys – are often limited due to smallsamples, high cost, low temporal frequency, slow collection, release and coarse spatial resolution. New forms ofdata, particularly from social media, can help overcome these limitations. While some bias exists, social mediadata are produced at an unprecedented temporal frequency, geographical granularity, are collected globally andaccessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this paperaims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemicin Germany, Italy, Spain, the United Kingdom and the United States. Results show an increase of migration-relatedTweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence ofa significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset bycomparable increases in positive messages. Additionally, we presented evidence of growing social polarisationconcerning migration, showing high concentrations of strongly positive and strongly negative sentiments.

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

“…Others have investigated the public's opinion and awareness of COVID-19 related events (e.g., protests against lockdown, vaccination, and university reopening) and speeches/comments of political leaders (e.g., Donald Trump) ( Hu et al, 2021 , Jang et al, 2021 ). Current studies have been conducted in several countries such as the U.S. ( Jang et al, 2021 , Lyu et al, 2021 ), the U.K. ( Cheng et al, 2021 , Rahman and Islam, 2022 ), Australia ( Ewing and Vu, 2021 , Wang et al, 2022 ), India ( Barkur and Vibha, 2020 ), China ( Li et al, 2020a , Wang et al, 2020a ), Europe ( Kruspe et al, 2020 ), as well as across multiple countries ( Boon-Itt and Skunkan, 2020 , Matošević and Bevanda, 2020 , Rowe et al, 2021 ). Existing studies focus predominantly on solely English-based content, while a smaller proportion uses either content that is in Chinese and retrieved from Weibo (the largest social media platform in China) ( Li et al, 2020a , Wang et al, 2020a ) or non-verbal content (e.g., emoticons) ( Yamamoto et al, 2014 ); scarce attention has been allotted to sentiment analysis involving multilingual content (discussed in Section 4.1.2 ).…”

Using Twitter to track immigration sentiment during early stages of the COVID-19 pandemic

“…We are grateful to Mark Green for sharing some base code for data collection and pre-processing. We also acknowledge a pre-print of the current article (Rowe et al, 2021b; Rowe et al 2021c).…”

A Framework to Understand Attitudes towards Immigration through Twitter

“…Moreover, considerable latency may impact data releases, impairing our ability to regularly monitor changes in migration sentiment, identify and tackle prejudice comments against immigrants. However, we know that anti-migration sentiment and prejudice comments can surge during economic recessions [13] and pandemics [14].…”

“…Such content has the potential to cause harm to individuals. It often translates into social tension outside the digital world and has played a role in the spread of hate speech during the COVID19 pandemic [14]. With this context in mind, we aim to answer the following research questions: (RQ1) Can we identify, quantify and classify attitudes towards immigration from social network data?…”