Hate in the Time of COVID-19: Racial Crimes against East Asian

We provide evidence of the impact of the COVID-19 pandemic on racial hate crime in England and Wales. Using various data sources, including unique data collected through Freedom of Information (FOI) requests from UK police forces, a difference-in-difference and event study approaches, we find that racial hate crime against East Asians increased by 70-100%, beginning in early February and persisted until November 2020. This effect was greatest in weeks leading up to the first national lockdown in the UK. The shock was then lower during lockdown, before increasing again in the summer 2020. We present evidence that hate crime increased as COVID-19 cases in China increased and following announcements from the government signalling that China or Chinese individuals posed a public health risk to the UK. This indicates that protectionism played an important role in the observed hate crime spike. The hate crime shock was also positively correlated with the salience of the national lockdown and government policies restricting certain freedoms. The effect was driven largely by changes in London. This suggests that retaliation further contributed to the rise in hate crime

@USA vs. @realDonaldTrump: The Decline of Democracy in 280 Characters or Less

From threats, to hate speech, to potential criminal statements, Donald Trump has made use of Twitter like no president or world leader before him. His presidency and communication strategy have been defined by his “tweetstorms” and a consequent slew of legal issues. The prolific rate of his tweeting has made large-scale analyses difficult as they quickly become dated. Nevertheless, this thesis has aimed for a more holistic analysis by uniquely linking trends in his tweeting to its perceived social consequences, situating this work in a long line of analyses of presidential rhetoric and media strategies. Moreover, it assesses Trump’s use of Twitter as an abuse of power and argues that it is degrading the fabric of our democracy. It focuses on three distinct aspects of his tweeting: the devaluation of truth, its rhetoric altering reality and degrading rule of law. Drawing on public opinion polls, psychology studies, and tweet-by-tweet analyses of rhetorical and legal implications, the findings of this work suggest that Trump’s tweeting is damaging our democracy on a variety of levels. This is the realization of thousands of years of worries — from Socrates to the Framers — of a populist demagogue who would incite the masses with fiery rhetoric. This thesis recommends improved civic education and social media literacy programs, and advocates holding social media platforms accountable for the information, or misinformation allowed on platforms that may have damaging effects on individuals or a society

Mining Public Opinion on COVID-19 Vaccines using Unstructured Social Media Data

The emergence of the novel coronavirus (COVID-19), and the necessary separation of populations led to an unprecedented number of new social media users seeking information related to the pandemic. Nowadays, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. This study investigated and compared public discourse related to COVID-19 vaccines expressed on two popular social media platforms, Reddit and Twitter. Approximately 9.5 million Tweets and 70 thousand Reddit comments were analyzed from dates January 1, 2020, to March 1, 2022, and analyzed through topic modeling, sentiment analysis, and semantic network analysis. Sentiment analysis through the fine-tuned DistilRoBERTa model revealed that even though Twitter content was overall more negative than content expressed on Reddit, relatively similar changes in sentiment occurred among users of both online platforms. Reversals in sentiment trends typically occurred within relative proximity to events such as vaccine development news, vaccine release, frequent discussion of side-effects, the discovery of new variants, and pandemic fatigue. Topic modeling and semantic network analysis provided insight into how public discourse related to COVID-19 and vaccinations, misinformation, and vaccine hesitancy evolved over 26 months. Though misinformation and mention of conspiracy theories were detected with the analysis, the occurrence of both was less frequent than expected. This work provides a framework that could be scaled and utilized by public health officials to monitor disease outbreaks in near real-time in large communities as well as smaller local groups. Hopefully, the results from this study will help to guide and facilitate the implementation of targeted digital interventions among vaccine-hesitant populations and provide insights to public health officials to inform decision-making and effective policy development

Strategic Competition: Russian and Chinese Influence in Latin America and the Caribbean

This second edition of the Global Security Review analyzes the changing political, cultural, and technological environments of the Twenty-First Century. Articles were provided by preeminent scholars such as Hal Brands, Ryan Berg, Margaret Myers, Vladimir Rouvinski, Betilde Muñoz-Pogossian, Diego Chaves-González, Louise Marie Hurel, Marcus Boyd and Samuel Henkin. They address issues including the ongoing strategic competition in the Western Hemisphere, China’s COVID-19 diplomacy in Latin America, Russian objectives in controlling the narrative through the media, the effect of natural disasters on migration, regional outlooks on cyber operations and norms, and the broadening scope of transnational organized crime networks

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine

Quantifying polarization across political groups on key policy issues using sentiment analysis

There is growing concern that over the past decade, industrialized democratic nations are becoming increasingly politically polarized. Indeed, elections in the US, UK, France, and Germany have all seen tightly won races, with notable examples including the 2016 Trump vs. Clinton presidential election and the UK's Brexit referendum. However, while there has been much qualitative discussion of polarization on key issues, there are few examples of formal quantitative assessments examining this topic. Therefore, in this paper, we undertake a statistical evaluation of political polarization for representatives elected to the US congress on key policy issues between 2021-2022. The method is based on applying sentiment analysis to Twitter data and developing quantitative analysis for six political groupings defined based on voting records. Two sets of policy groups are explored, including geopolitical policies (e.g., Ukraine-Russia, China, Taiwan, etc.) and domestic policies (e.g., abortion, climate change, LGBTQ, immigration, etc.). We find that out of the twelve policies explored here, gun control was the most politically polarizing, with significant polarization results found for all groups (four of which were P < 0.001). The next most polarizing issues include immigration and border control, fossil fuels, and Ukraine-Russia. Interestingly, the least polarized policy topics were Taiwan, LGBTQ, and the Chinese Communist Party, potentially demonstrating the highest degree of bipartisanship on these issues. The results can be used to guide future policy making, by helping to identify areas of common ground across political groups.Comment: 31 pages, 7 figure

Classification aware neural topic model and its application on a new COVID-19 disinformation corpus

The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide. To help tackle this, we developed computational methods to support COVID-19 disinformation debunking and social impacts research. This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework. We demonstrate that CANTM efficiently improves classification performance with low resources, and is scalable. In addition, the classification-aware topics help researchers and end-users to better understand the classification results