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It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. The objective of this study is to examine COVID-19-related discussions, concerns, and sentiments using tweets posted by Twitter users. We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, \"coronavirus,\" \"COVID-19,\" \"quarantine\") from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. Popular unigrams included \"virus,\" \"lockdown,\" and \"quarantine.\" Popular bigrams included \"COVID-19,\" \"stay home,\" \"corona virus,\" \"social distancing,\" and \"new cases.\" We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.

The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion. This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC. Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords-cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov-while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling. The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility. Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.

Affectivity has become an operative concept for a variety of analyses of our everyday media-based public communications. However, it often remains unclear what affectivity is and how it can be used for analysing media-based public discussions. To clarify the role of affectivity in such analyses, I take a look back to the classical phenomenological analyses of affectivity provided by Edmund Husserl. I argue that based on Husserl’s analyses, affectivity is essentially a relation between the object and the affected subject evoking (sometimes emotional) responses in the subject. Accordingly, the role of affectivity in the opinion formation and other similar processes in media-based public discussions can be analysed as contingent sedimentations of the object’s such relations to the subject. As my analysis demonstrates, analyses of affectivity in the context of media-based communications do not capture their research object—affectivity—if affectivity is conceived as a feature of the media contents and not as a modality of experience.

This IEG evaluation, requested by the World Bank’s Board of Executive Directors, represents the first independent evaluation of the PSIA experience. The evaluation finds that:. • The PSIA approach has appropriately emphasized the importance of assessing the distributional impact of policy actions, understanding institutional and political constraints to development, and building domestic ownership for reforms. • PSIAs have not always explicitly stated their operational objectives (i.e., informing country policies, informing Bank operations, and/or contributing to country capacity). • PSIAs have had limited ownership by Bank staff and managers and have often not been effectively integrated into country assistance programs. • Quality assurance and Monitoring and Evaluation of the overall effectiveness of PSIAs have been weak. The evaluation recommends that the World Bank:. • Ensure that Bank staff understand what the PSIA approach is and when to use it. • Clarify the operational objectives of each PSIA and tailor the approach and timeline to those objectives. • Improve integration of the PSIA into the Bank’s country assistance program by requiring that all earmarked funding for PSIAs be matched by a substantial contribution from the country unit budgets. • Strengthen PSIA effectiveness through enhanced quality assurance.

During the COVID-19 pandemic, social media platforms emerged as both vital information sources and conduits for the rapid spread of propaganda and misinformation. However, existing studies often rely on single-label classification, lack contextual sensitivity, or use models that struggle to effectively capture nuanced propaganda cues across multiple categories. These limitations hinder the development of robust, generalizable detection systems in dynamic online environments. In this study, we propose a novel deep learning (DL) framework grounded in fine-tuning the RoBERTa model for a multi-label, multi-class (ML-MC) classification task, selecting RoBERTa due to its strong contextual representation capabilities and demonstrated superiority in complex NLP tasks. Our approach is rigorously benchmarked against traditional and neural methods, including, TF-IDF with n-grams, Conditional Random Fields (CRFs), and long short-term memory (LSTM) networks. While LSTM models show strong performance in capturing sequential patterns, our RoBERTa-based model achieves the highest overall accuracy at 88%, outperforming state-of-the-art baselines. Framed within the diffusion of innovations theory, the proposed model offers clear relative advantages—including accuracy, scalability, and contextual adaptability—that support its early adoption by Information Systems researchers and practitioners. This study not only contributes a high-performing detection model but also delivers methodological and theoretical insights for combating propaganda in digital discourse, enhancing resilience in online information ecosystems.

This study aims to investigate the complex relationship among entertainment contents, networked publics, and politics by offering an overview of literatures in the fields and suggests that the emotional dimensions are the key to understand the political possibilities of mediated public discussion. By analyzing the online comments of YouTube channel WIGS’s web series Lauren, this article reveals that audiences interpret and discuss this web series through mediated feelings of connectedness and thus are able to engage in public debate and political deliberation. I argue that, the connective affordances of YouTube and the emotional realism of the web-drama facilitated the web space of Lauren to function as an “emotional public sphere” where social solidarity was strengthened, political criticism was developed, and political activity could be motivated. Overall, this study reconceptualizes the place of entertainment media in democracy and everyday life and contributes to political communication and feminist media studies.

Political Communication and Deliberation takes a unique approach to the field of political communication by viewing key concepts and research through the lens of deliberative democratic theory. This is the first text to argue that communication is central to democratic self-governance primarily because of its potential to facilitate public deliberation. Thus, it offers political communication instructors a new perspective on familiar topics, and it provides those teaching courses on political deliberation with their first central textbook. This text offers students practical theory and experience, teaching them skills and giving them a more direct understanding of the various subtopics in public communication.

Russian media have recently (re-)gained attention of the scholarly community, mostly due to the rise of cyber-attacking techniques and computational propaganda efforts. A revived conceptualization of the Russian media as a uniform system driven by a well-coordinated propagandistic state effort, though having evidence thereunder, does not allow seeing the public discussion inside Russia as a more diverse and multifaceted process. This is especially true for the Russian-language mediated discussions online, which, in the recent years, have proven to be efficient enough in raising both social issues and waves of political protest, including on-street spillovers. While, in the recent years, several attempts have been made to demonstrate the complexity of the Russian media system at large, the content and structures of the Russian-language online discussions remain seriously understudied. The thematic issue draws attention to various aspects of online public discussions in Runet; it creates a perspective in studying Russian mediated communication at the level of Internet users. The articles are selected in the way that they not only contribute to the systemic knowledge on the Russian media but also add to the respective subdomains of media research, including the studies on social problem construction, news values, political polarization, and affect in communication.

This article focuses on the theoretical and conceptual issues that reside at the intersection of deliberation and action. Looking at the Antigonish Movement and the USDA's farmer discussion groups and schools of philosophy in the 1930s and 1940s, the article identifies salient points about the ways in which institutional leaders developed programs that attended to concerns about the role of facts, values, and strategies by embedding deliberative talk within collaborative efforts through education and community development initiatives.

Artykuł dotyczy kampanii ideologicznej prowadzonej przez Adama Schaffa, zorganizowanej w powojennej Polsce na fali stalinizacji. Próbując dostosować radziecki „model” dyskusji publicznej do polskiego środowiska akademickiego, Schaff chciał „nauczyć” przedstawicieli lwowsko-warszawskiej szkoły logiki, jak prowadzić debatę naukową. Pisząc krytyczne recenzje prac polskich logików, grupa Schaffa, w skład której wchodzili młodzi naukowcy z Instytutu Kształcenia Kadr Naukowych, próbowała zmusić swoich przeciwników do zmiany podstawowych zasad praktyki akademickiej w nowych warunkach. Niemniej jednak projekt Schaffa nie powiódł się, ponieważ, w przeciwieństwie do sowieckich uczonych, uczestnicy dyskusji odnosili się do różnych cnót akademickich, które uniemożliwiały adaptację „radzieckiego modelu” dyskusji publicznej.---Article available under CC BY license.License text: https://creativecommons.org/licenses/by/4.0/legalcode