Newspapers are very important for a society as they inform citizens about the events around them and how they can impact their life. Their importance becomes more crucial and indispensable in the times of health crisis such as the current COVID-19 pandemic. Since the starting of this pandemic newspapers are providing rich information to the public about various issues such as the discovery of a new strain of coronavirus, lockdown and other restrictions, government policies, and information related to the vaccine development for the same. In this scenario, analysis of emergent and widely reported topics/themes/issues and associated sentiments from various countries can help us better understand the COVID-19 pandemic. In our research, the database of more than 100,000 COVID-19 news headlines and articles were analyzed using top2vec (for topic modeling) and RoBERTa (for sentiment classification and analysis). Our topic modeling results highlighted that education, economy, US, and sports are some of the most common and widely reported themes across UK, India, Japan, South Korea. Further, our sentiment classification model achieved 90% validation accuracy and the analysis showed that the worst affected country, i.e. the UK (in our dataset) also has the highest percentage of negative sentiment.

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The COVID-19 pandemic brought about several challenges in addition to the virus itself. The rise of Islamophobic hate speech on social media is one such challenge. As countries were coping with economic collapse due to mass lockdown, hateful people, especially those associated with far-right groups, were targeting and blaming Muslims for the spread of the coronavirus. In India, where intense religious/communal polarization is taking place under the right-wing Bharatiya Janata Party (BJP)-led government, one such prominent instance of Islamophobia—the “Tablighi Jamaat Controversy” (TJC)—occurred. This article analyzes Facebook posts by public groups over a 5-month period (March–August 2020) to find the major actors and track their link-sharing behavior. We found that the Pro-BJP groups with a right-wing ideology spread Islamophobic hate speech, while other groups (anti-hate) worked to counter the hate. We also found that the hate disseminators were extremely active (three times faster) in sharing their content as compared with the anti-hate groups. Finally, our research indicated that the links most widely shared by the hate spreaders were mostly misinformation. These results explain the use of the Facebook platform to spread hate and misinformation, demonstrating how BJP’s pro-Hindu ideology and its attitude toward Muslims is directly and indirectly enabling these actors to spew hate against Muslims with no legal consequences.

For Japan—a country that has always been described with virtually no major natural resources such as oil, gas, and coal—the Middle Eastern region has a special place in its economic and foreign policy. In 2017, 39% of Japan’s energy came from oil, and 87% of Japan’s imported oil came from the Middle East, predominantly Saudi Arabia and the UAE. The above facts are enough to discern the critical significance of the Middle Eastern region for Japan. For Japan to have an unhindered supply of oil and other natural resources, it is pertinent that this region remains peaceful. In this scenario, the Middle East-related articles in Japan’s newspapers can help understand Japan’s perspective towards the Middle East. This paper would first apply the topic modelling approach non-negative matrix factorization (NMF) on Middle East-related articles from three newspapers of Japan. After discovering crucial topics, we would utilize traditional supervised machine learning algorithms to determine the overall and topic-specific sentiments from the collected headlines. Our topic modelling results discovered that the Japanese media widely reported issues like Islamic State, the refugee crisis, the Syrian civil war, Qasem Soleimani killing, and Iran nuclear deal. Further, the news related to Saudi Arabia, Syria, and Trump garnered high negative sentiment.

Currently, the significance of social media in disseminating noteworthy information on topics such as health, politics, and the economy is indisputable. During the COVID-19 pandemic, anti-vaxxers use social media to distribute fake news and anxiety-provoking information about the vaccine, which may harm the public. Here, we characterize the psycho-linguistic features of anti-vaxxers on the online social network Twitter. For this, we collected COVID-19 related tweets from February 2020 to June 2021 to analyse vaccination stance, linguistic features, and social network characteristics. Our results demonstrated that, compared to pro-vaxxers, anti-vaxxers tend to have more negative emotions, narrative thinking, and worse moral tendencies. This study can advance our understanding of the online anti-vaccination movement, and become critical for social media management and policy action during and after the pandemic.