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.
Finland has one of the last fully monopolistic gambling sectors in Europe. Unlike in most Western European countries, the monopoly is also consolidated and enjoys a wide support as opposed to ...license-based competition. This paper analyses whether this preference for monopoly provision is due to the particularities of the Finnish society or rather to those of the Finnish gambling sector. We do this by comparing public discourses in media texts (N=143) from 2014 to 2017 regarding monopolies operating in alcohol retail, rail traffic and gambling sectors. The results show that gambling appears to be special even in the Finnish national context. While the Finnish alcohol retail and railroad traffic markets have been liberalised during the study period, the gambling monopoly has been concurrently strengthened despite similar political and international pressures towards dismantling. The discussion suggests that the differing outcomes reflect the varying positions of monopolies, their stakeholders and the justifications put forward. Intertwined stakeholder interests in the gambling sector appear to amplify consensus politics and set gambling apart from the other cases.
Fostering sustainable consumption is crucial and understanding the discussion about sustainable consumption on social media serves this important goal. However, the literature lacks an up-to-date ...exploration of the topics and sentiments pertaining to the discussion about sustainable consumption on social media. In this study, to explore public discussion about sustainable consumption on social media, Twitter (currently X) posts (or tweets) were analyzed using topic modeling and sentiment analysis. The findings revealed that the discussion has a slightly positive sentiment and revolves around specific contexts of consumption (such as energy, fashion, plastic, and food consumption), issues of environmental and economic sustainability (such as circular economy and resource use), and sustainable cities, institutions, and regulation. Furthermore, public discussion about sustainable consumption mainly revolves around relatively minor changes to current consumption patterns and levels. This study contributes to literature on sustainable consumption and provides practical implications for those interested in fostering sustainable consumption.
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In this paper, the problem of function computation with privacy and secrecy constraints is considered. The considered model consists of three legitimate nodes (i.e., two transmitters, Alice and Bob, ...and a fusion center that acts as the receiver) that observe correlated sources and are connected by noiseless public channels, and an eavesdropper Eve who has full access to the public channels and also has its own source observations. The fusion center would like to compute a function of the distributed sources within a prefixed distortion level under a certain distortion metric. To facilitate the function computation, Alice and Bob will send messages to the fusion center. Different from the existing setups in function computation, we assume that there is a privacy constraint on the sources at Alice and Bob. In particular, Alice and Bob would like to enable the fusion center to compute the function, but at same time, they do not want the fusion center to learn too much information about the source observations. We introduce a quantity to precisely measure the privacy leakage to the fusion center. In addition to this privacy constraint, we also have a secrecy constraint to Eve and use equivocation of sources to measure this quantity. Under this model, we study the tradeoffs among message rates, private information leakage, equivocation, and distortion. We first consider a scenario that has only one transmitter, i.e., the source at Bob is empty, and fully single-letter characterize the corresponding regions. Then, we consider the more general case and provide both outer and inner bounds on the corresponding regions.
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.
Deliberative democracy’s core practice of political discussion is often claimed to entail beneficial ‘self-transformative’ effects on those partaking in it. We examine the assumption that political ...talk makes for ‘better citizens’ with a special focus on individuals’ orientations toward democracy and their own roles within it. We conceptualize these orientations as a triad of democratic citizenship that encompasses three pillars: (1) the attitudinal dimension of citizens’ support for the democratic political system whose members they are, (2) the normative dimension of views about ‘good’ citizenship, and (3) the behavioral dimension of active participation in this system’s political process. Our analysis offers a comprehensive perspective at how these orientations are affected by engagement and disagreement in political talk across four discursive spheres: (i) informal conversations of a private nature within strong network ties (family and friends), (ii) of a semi-public nature within weak network ties (acquaintances), and (iii) of a public nature outside social networks (strangers), as well as (iv) formalized public discussions at organized events. Drawing on two high-quality surveys from Germany, we find overall positive effects of engagement in informal-private conversations and formalized public discussions on citizenship orientations. The role of semi-public political talk within weak ties appears ambivalent, but its impact is overall rather weak. Strikingly, we observe strong indications that casual conversations with strangers weaken people’s support for the democratic system, participatory norms, and likelihood of active political engagement. Disagreement during political conversations also matters for democratic orientations, and its effects are always positive.
Contemporary Slovenian language standardisation includes the revision of the normative guide, a process taking place since 2013 within the Commission on Orthography. This article presents an overview ...of the scientific basis of this process as well as describes the systematic inclusion of different segments of the public in the phase of assessing the suitability of current orthographic rules and formulating new ones. This is due to an awareness that a normative guide can be accepted by the wider language community only through a convergence of differing opinions and codification based on arguments.
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