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Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter
Doctors and nurses in these weeks and months are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. The discourse around the current epidemic makes use of war-related metaphors too, not only in public discourse and in the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a large corpus tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSUNAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY frame covers a wider portion of the corpus, among the figurative frames WAR, a highly conventional one, is the frame used most frequently. Yet, this frame does not seem to be apt to elaborate the discourse around some aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options-or a metaphor menu-may facilitate the communication of various aspects involved in the Covid-19-related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and beliefs during the current pandemic.
Journal Article
Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak
Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary \"infoveillance\" approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms \"H1N1\" versus \"swine flu\" over time; 2) conduct a content analysis of \"tweets\"; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool.
Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords \"swine flu,\" \"swineflu,\" and/or \"H1N1.\" using Infovigil, an infoveillance system. Tweets using \"H1N1\" increased from 8.8% to 40.5% (R(2) = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%). 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data.
This study illustrates the potential of using social media to conduct \"infodemiology\" studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.
Journal Article
Sentiment of Emojis
There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4% of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar.
Journal Article
A Typology of Crowdwork Platforms
Despite growing interest in the gig economy among academics, policy makers and media commentators, the area is replete with different terminology, definitional constructs and contested claims about the ensuing transformation of work organisation. The aim of this positional piece is to provide a timely review and classification of crowdwork. A typology is developed to map the complexity of this emerging terrain, illuminating range and scope by critically synthesising empirical findings and issues from multidisciplinary literatures. Rather than side-tracking into debates as to what exactly constitutes crowdwork, the purpose of the typology is to highlight commonalities rather than distinctions, enabling connections across areas. The framework serves as a heuristic device for considering the broader implications for work and employment in terms of control and coordination, regulation and classification, and collective agency and representation.
Journal Article
Improving topic modeling performance on social media through semantic relationships within biomedical terminology
Topic modeling utilizes unsupervised machine learning to detect underlying themes within texts and has been deployed routinely to analyze social media for insights into healthcare issues. However, the inherent messiness of social media hinders the full realization of this technique’s potential. As such, we hypothesized that restricting medical concepts in social media texts to specific related semantic types and applying topic modeling to these concepts could be a feasible approach to overcome the challenge of traditional topic modeling for social media texts. Therefore, we developed a semantic-type-based topic modeling pipeline to discover self-reported health-related topics. This pipeline integrated semantic type information and Systematized Medical Nomenclature for Medicine (SNOMED) precoordinated expressions into a traditional topic modeling approach to enhance effectiveness in clustering meaningful, distinct topics. Using social media texts regarding statins for illustration, we evaluated the efficacy of this new approach and validated a newly identified topic using real-world clinical data. Based on expert evaluations, this approach resulted in more novel, distinguishable, and meaningful health-related topics compared to traditional topic modeling. In addition, our electronic health record validation for a newly identified topic in two real-world clinical databases indicated that statin users had a higher prevalence of depression or anxiety compared to matched non-users. Our results indicate that this new topic modeling pipeline can improve the extraction of themes from noisy online discussions, thereby contributing to deeper insights for healthcare research.
Journal Article
Toward a Consensus Description of Vocal Effort, Vocal Load, Vocal Loading, and Vocal Fatigue
Purpose: The purpose of this document is threefold: (a) review the uses of the terms \"vocal fatigue,\" \"vocal effort,\" \"vocal load,\" and \"vocal loading\" (as found in the literature) in order to track the occurrence and the related evolution of research; (b) present a \"linguistically modeled\" definition of the same from the review of literature on the terms; and (c) propose conceptualized definitions of the concepts. Method: A comprehensive literature search was conducted using PubMed/MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scientific Electronic Library Online. Four terms (\"vocal fatigue,\" \"vocal effort,\" \"vocal load,\" and \"vocal loading\"), as well as possible variants, were included in the search, and their usages were compiled into conceptual definitions. Finally, a focus group of eight experts in the field (current authors) worked together to make conceptual connections and proposed consensus definitions. Results: The occurrence and frequency of \"vocal load,\" \"vocal loading,\" \"vocal effort,\" and \"vocal fatigue\" in the literature are presented, and summary definitions are developed. The results indicate that these terms appear to be often interchanged with blurred distinctions. Therefore, the focus group proposes the use of two new terms, \"vocal demand\" and \"vocal demand response,\" in place of the terms \"vocal load\" and \"vocal loading.\" We also propose standardized definitions for all four concepts. Conclusion: Through a comprehensive literature search, the terms \"vocal fatigue,\" \"vocal effort,\" \"vocal load,\" and \"vocal loading\" were explored, new terms were proposed, and standardized definitions were presented. Future work should refine these proposed definitions as research continues to address vocal health concerns.
Journal Article
Diffusion of Lexical Change in Social Media
Computer-mediated communication is driving fundamental changes in the nature of written language. We investigate these changes by statistical analysis of a dataset comprising 107 million Twitter messages (authored by 2.7 million unique user accounts). Using a latent vector autoregressive model to aggregate across thousands of words, we identify high-level patterns in diffusion of linguistic change over the United States. Our model is robust to unpredictable changes in Twitter's sampling rate, and provides a probabilistic characterization of the relationship of macro-scale linguistic influence to a set of demographic and geographic predictors. The results of this analysis offer support for prior arguments that focus on geographical proximity and population size. However, demographic similarity - especially with regard to race - plays an even more central role, as cities with similar racial demographics are far more likely to share linguistic influence. Rather than moving towards a single unified \"netspeak\" dialect, language evolution in computer-mediated communication reproduces existing fault lines in spoken American English.
Journal Article
Public concerns about human metapneumovirus: insights from Google search trends, X social networks, and web news mining to enhance public health communication
The respiratory virus known as human metapneumovirus (hMPV) is linked to seasonal outbreaks and primarily affects elderly people and young children. Infodemiology, which uses digital data sources, including social media, online news, and search trends, is a useful substitute for monitoring public concerns and risk perceptions because surveillance gaps and underreporting impede public health interventions despite their clinical value. To assess public search interest, we analyzed global search behavior between June 1, 2024, and June 1, 2025, and examined over 1.3 million tweets collected during the peak outbreak period from January to March 2025. Our findings show a sharp rise in public interest following official reports of HMPV outbreak in China, with simultaneous search peaks across both hemispheres regardless of season. Search activity expanded to 177 countries and revealed sustained interest in Australia, Thailand, the United Kingdom, and the United States. Regional differences in terminology and platform usage were also observed, with non-English-speaking countries favoring the abbreviation “HMPV” and English-speaking regions more often using the full term. Additionally, discrepancies between search activity and social media engagement in some countries point to distinct patterns of public information-seeking behavior. These results underscore the importance of adapting health communication strategies to local language norms and preferred digital platforms. They also highlight the need for real-time monitoring and proactive responses to misinformation. Together, search and social media data offer a valuable lens for understanding public sentiment and improving the reach, accuracy, and impact of global outbreak communication.
Journal Article
A scientometric overview of CORD-19
As the COVID-19 pandemic unfolds, researchers from all disciplines are coming together and contributing their expertise. CORD-19, a dataset of COVID-19 and coronavirus publications, has been made available alongside calls to help mine the information it contains and to create tools to search it more effectively. We analyse the delineation of the publications included in CORD-19 from a scientometric perspective. Based on a comparison to the Web of Science database, we find that CORD-19 provides an almost complete coverage of research on COVID-19 and coronaviruses. CORD-19 contains not only research that deals directly with COVID-19 and coronaviruses, but also research on viruses in general. Publications from CORD-19 focus mostly on a few well-defined research areas, in particular: coronaviruses (primarily SARS-CoV, MERS-CoV and SARS-CoV-2); public health and viral epidemics; molecular biology of viruses; influenza and other families of viruses; immunology and antivirals; clinical medicine. CORD-19 publications that appeared in 2020, especially editorials and letters, are disproportionately popular on social media. While we fully endorse the CORD-19 initiative, it is important to be aware that CORD-19 extends beyond research on COVID-19 and coronaviruses.
Journal Article