Climate security dialogues on Twitter: An annotated dataset.

Climate security refers to the risks posed by climate change on nations, societies, and individuals, including the possibility of conflicts. As an emerging field of research and public debate, where conceptual definitions are not yet fully agreed upon, gaining insights into global discussions on climate security enables systematizing its various interpretations and framings, mapping thematic priorities, and understanding information gaps that need to be filled. Considering Twitter as an important digital forum for information exchanges and dialogue, the dataset was created through the development of a query strategy based on a snowball scraping technique, which collected tweets containing hashtags related to climate security between January 2014 to May 2023. The dataset comprises 636,379 tweets. Content analysis was performed using text mining and network analysis techniques to generate additional data on sentiment, countries mentioned in the body of tweets, and hashtag co-occurrences. With almost 10 years of data, the utility of this dataset lies in the ability to assess the discursive evolution of a particular topic since its inception.

"This was never about a virus": Perceptions of Vaccination Hazards and Pandemic Risk in #Covid19NZ Tweets.

In this paper, we draw on qualitative methods from the medical humanities and quantitative approaches from corpus linguistics to assess the different mappings of pandemic risks by Twitter (X) users employing the #Covid19nz hashtag. We look specifically at their responses to government measures around vaccines between August and November 2021. Risk, we reveal, was a major discursive thread in tweets during this period, but within our tweets, it was the vaccine rather than the virus around which hazard perception and response were grouped. We find that the discursive stance of those opposed to the vaccine evoked entangled medical and political hazards, untrustworthy experts, obscure information, restrictions on sovereignty, threats to children, and uncertain future dangers, all of which positioned them within what Ulrich Beck termed the world risk society. We also found that these narratives of risk manifested in specific Twitter styles, which employed a consistently larger number of hashtags. The lack of conjunctions between the hashtags, we argue, encouraged a disordered reading of doubt and precaution, as the hashtags presented triggering phrases whose interconnections were hinted at rather than specified. By contrast, those who tweeted in support of government measures were rhetorically led by solutions rather than risks, with one exception: their perception of those who were vaccine opposed. We use scholarship on risk and precautionary logic to map out the contrasting positions in tweets addressing Aotearoa New Zealand's pandemic experience during the closing months of 2021.

"You Aren't Alone": An Analysis of Trans Latinas' Use of Instagram.

Social media platforms, such as Instagram, provide space for marginalized groups to connect, learn about and express themselves, and cultivate community. Trans Latinas, a group target of violence and discrimination, resist by expressing themselves and building community through social media. As cisgender researchers, we explored how trans Latinas use #translatina on Instagram as a shared space to present themselves and their identities, to leverage this knowledge in our fields. We analyzed 134 posts in February and March of 2020 employing basic and interpretive content analyses while considering Goffman's theory of presentation of self. Results showed that trans Latinas mostly presented individually through posed selfies taken near the camera, using a straight camera angle, standing, not smiling, and making eye contact. Most users wore makeup, styled hair, and accessories. Analyzing written captions and photos, four themes were constructed to understand how trans Latinas presented their identities and connected with others: (1) expressions of beauty and femininity, (2) fostering community, (3) commercial or work, and (4) feeling good and confident. These results have implications for mental health and health promotion practices, as social media could serve as affirming spaces for trans Latinas to reinforce their self-determination, maintain a sense of self, and build community.

Cytopathology in the era of social media.

In today's medical landscape, social media (SoMe) platforms have expanded their reach beyond mere communication and entertainment, making a significant impact in the pathology field, including cytopathology. In recent years, SoMe platforms have become increasingly adopted by cytopathologists, facilitating continued education, professional networking, enhancing patient engagement, and entertainment. This adoption has influenced the professional growth of cytopathologists, and at its best, has led to the establishment of a robust professional online presence and ultimately contributed to leadership positions, fellowship opportunities, and academic promotions. Moreover, the integration of SoMe into the academic field has shown a profound impact on the visibility of academic journals and has provided a platform for lower-impact factor journals to expand their reach, ultimately increasing article citation rates and positively contributing to journal impact factor growth. SoMe platforms created a modern avenue for conference networking that has revolutionized knowledge dissemination and enhanced real-time engagement. The advantages of SoMe have extended to a global scale, positively enhancing professional expertise sharing, facilitating effective communication and teleconsultation worldwide, and reaching developing countries. Drawing insights from the recent medical literature and the practical insight from the experts' personal experience, this article provides a comprehensive review of how SoMe and cytopathology intersect to create new opportunities, facilitating informed discussions, global collaboration, and advancements in the field of cytopathology. This article also delves into the challenges surrounding SoMe platform navigation and addresses ethical and regulatory concerns, providing guidelines on what to post and what not to post on SoMe platforms.

Examining the Dynamics of COVID-19 Misinformation: Social Media Trends, Vaccine Discourse, and Public Sentiment.

INTRODUCTION: COVID-19, known as coronavirus disease, has prompted a global reevaluation of societal norms. The World Health Organization (WHO) declared it a worldwide Public Health Emergency on January 30, 2020. Subsequently, governments and pharmaceutical firms developed vaccines, such as mRNA options from Pfizer and Moderna, alongside viral vector alternatives to combat the escalating COVID-19 case tally. Extensive inquiry was directed toward assessing vaccine efficiency. Nonetheless, vaccine discourse has surged across social media, prominently involving the anti-vaxxer community. This faction's hesitancy, rooted in reservations about efficacy, potential side effects, and conspiracy notions, contributes to an ongoing dialogue. OBJECTIVE: This investigation delves into social media's role in proliferating COVID-19 misinformation, utilizing tools like Python, Excel, and external resources to craft data visuals that elucidate trends influencing misinformation dissemination and its hypothetical ties to elevated COVID-19 cases. Scrutiny of Twitter trends illuminates the prevalence of the hashtag #covidvaccine, although the platform curbs anti-vaccine hashtags. RESULT: Analysis of sentiment across 207,006 tweets reveals a prevailing positive sentiment toward COVID-19 vaccines, coexisting with lingering skepticism. Google trends reflect increased anti-vaccine ideology queries, notably post-FDA vaccine approval in December 2020, indicating public doubt. CONCLUSION: While limitations encompass data granularity, geographic origins of false tweets, bot account quantification on Twitter, and comprehensive digital resources, this study pioneers reference for forthcoming investigations. Its objective is to mitigate the diffusion of misinformation.

Use of #NutritionFacts to promote evidence-based nutrition information: X (formerly Twitter) hashtag analysis study.

Nutrition is a key determinant of health, and the dissemination of reliable nutrition information to consumers is of great importance for public health. Especially with the rise of digital communication technologies and the wide-spread online misinformation, the provision of qualitative science-based information related to diet is of great importance. The NutritionFacts.org has been established as a prominent online source of evidence-based nutrition information. In this work we aimed to investigate the use of the associated hashtag #NutritionFacts on X (formerly Twitter) over a 5 years period, from 10th of April 2018 to 10th of April 2023. The conducted analysis with the use of Symplur Signals revealed that 18,998 tweets mentioning #NutritionFacts were posted by 6,136 X users, generating a total of 50,348,223 impressions (views). Both institutional and individual accounts were broadly participating in the dissemination of #NutritionFacts tweets, and the user location profiling indicated wide international engagement with the hashtag. This work indicates that #NutritionFacts has been established as an important hashtag utilized on X for the dissemination of evidence-based information related to nutrition.

Staying connected: Alzheimer's hashtags and opportunities for engagement and overcoming stigma.

Alzheimer's disease (AD) is a terminal, neurodegenerative disease, and consequently is difficult to communicate about as it is stigmatized, and discussions are rife with misconceptions. By situating AD conversations in the sociocultural space of the opportunity model of presence during the end-of-life process, a framework developed illustrating the potential trajectory from living with illness through death and into bereavement, we examined networked discussions surrounding Alzheimer's related hashtags on Twitter (N = 132,803) between January 1st, 2010, and September 29th, 2021. Using the mixed-method approach of the Analysis of Topic Model Network (ANTMN) framework, results revealed 30 topics clustered into five distinct themes: promotion, education, action, "You aren't alone", and dementia. Results indicated that discussions surrounding World Alzheimer's Day focused on changing stigma and promoting engagement in difficult conversations. The frequency of themes over time remained relatively stable. By understanding how Twitter's online discourse may be used to overcome stigmatized topics, we can continue to tailor messages to reduce stigma and provide support for those who experience similar health issues.

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation.

Color similarity has been a key feature for content-based image retrieval by contemporary search engines, such as Google. In this study, we compare the visual content information of images, obtained through color histograms, with their corresponding hashtag sets in the case of Instagram posts. In previous studies, we had concluded that less than 25% of Instagram hashtags are related to the actual visual content of the image they accompany. Thus, the use of Instagram images' corresponding hashtags for automatic image annotation is questionable. In this study, we are answering this question through the computational comparison of images' low-level characteristics with the semantic and syntactic information of their corresponding hashtags. The main conclusion of our study on 26 different subjects (concepts) is that color histograms and filtered hashtag sets, although related, should be better seen as a complementary source for image retrieval and automatic image annotation.

Enacting polyvocal scorn in #CovidConspiracy tweets: The orchestration of voices in humorous responses to COVID-19 conspiracy theories.

Despite the abundance of research into conspiracy theories, including multiple studies of Covid-19 conspiracy theories in particular, user reactions to conspiracy theories are an underexplored area of social media discourse. This study aims to fill this gap by examining a dataset of humorous responses to proliferating COVID-19 conspiracy theories based on a corpus of tweets bearing the pejorative hashtag #CovidConspiracy. We report the complex orchestration of heteroglossic discursive voices in these posts to reveal their rhetorical function, oriented towards expressing a negative stance and, in some cases, amounting to ridicule. The discursive effects of this interplay of voices entail imitation, parody, mockery and irony on the micro level, while on the interactional (macro) level, anti-conspiracy tweets jointly enact what we dub "polyvocal scorn". It expresses multiple users' trenchant critique and contempt for conspiracy theories, while the humour of the tweets serves to display the users' wit and superiority over conspiracy theorists.

Role of Hashtags in Educating and Promoting the Field of Hand Surgery on Social Media.

Purpose: This study identifies the hand surgery content being posted on Instagram, how hashtags are being used, and those posting to determine what is reaching the public. Methods: Top hand surgery-related hashtags on Instagram from June 2020 to August 2020 were identified by searching "hand surgery" and sorting by relevance. Hashtags were quantified by number and qualitatively assessed. Posts without a clear relationship were excluded. Hashtags relevant to hand surgery were analyzed by educational merit, medical specialty, patient or nonpatient, and demographics. Results: The top 25 hashtags contained 325,400 posts. The 3 hashtags with the highest number of posts were #carpaltunnel (64,700), #handsurgery (50,500), and #handtherapy (48,300). Most posts were educational (53.2%). Nonsurgical fields (66.5%) posted the most, followed by orthopedic (25.9%), and plastic hand surgeons (7.7%). Nonpatients (68.8%) posted more than patients. The top 3 languages of the posts were English (67.7%), Russian (9.4%), and Spanish (7.1%). However, when looking at the hashtags with more than half of the posts being made by hand surgeons, we observed that most (62.9%) of the posts were noneducational in content. Conclusions: Instagram posts on hand surgery topics are largely posted by nonexperts and are educational in content. There is a major opportunity for hand surgeons to educate and market effectively using hand surgery-related hashtags. Given the number of hand surgeries performed annually, one would hope to see more representation by hand surgeons on social media. More active participation and provision of educational content by specialists is warranted. Clinical relevance: There is an opportunity for hand surgeons to educate those searching upper extremity conditions and seeking out expertise in a domain where information is largely driven by nonsurgeons and the quality of information is not vetted. This study identifies the need for more hand surgeon involvement to expand knowledge and communication efforts within the specialty and with the public through the evolving world of social media.

Analysis of Worldwide Greenhouse and Carbon Monoxide Gas Emissions: Which Countries Exhibit a Special Pattern? A Closer Look via Twitter.

There is significant global concern about the harmful effects of greenhouse gas and carbon monoxide emissions (deforestation, air pollution, global warming, etc.). The 2015 Paris Agreement on climate change aspires to reduce global warming by achieving a climate-neutral world. Research has been carried out to calculate and diminish the aforementioned emissions in waste, power industry, transport, building, in addition to other areas. The aim of this paper is to analyse the carbon and greenhouse gas emissions across countries around the globe in order to find patterns and correlate them to socio-economic indicators [gross national income (GNI), industrial production (IPI) and human development indexes (HDI)] as well as Twitter interactions regarding climate change. For this purpose, time series and socio-economic data have been downloaded from different repositories including EDGAR (Emissions Database for Global Atmospheric Research), World Bank and UNDP (United Nations Development Programme). Although classical clustering algorithms have already been used in the examination of some environmental issues, we use a non-parametric time series clustering method, which has been suggested in certain scientific literature as a more flexible approach, since any ad hoc parametric assumptions are required. The chosen socio-economic indicators have also demonstrated their relevance in pieces of research related to various fields. With respect to Twitter, which is one of the most popular social networks nowadays, significant analysis has also been performed on the basis of capturing citizens' perceptions on a multitude of matters. We found that several countries such as Brazil, India, China, Nigeria, Russia, United States, Spain, Andorra, Greece, and Qatar show differences in carbon and greenhouse gas emissions patterns. Besides, there does not seem to be a correlation between GNI, IPI and HDI as well as the above mentioned emissions ( correlation < 0.16 ) . Regarding Twitter interactions, a dissimilarity in the distribution of hashtags was detected between the aforementioned countries and the rest of the world. This research can help to identify countries in which more governmental measures are needed to reduce the type of emissions analysed in certain industrial sectors. In addition, it points out the topics related to climate change that seem to generate the most debate on Twitter for countries with an unusual pattern. Supplementary Information: The online version contains supplementary material available at 10.1007/s41742-023-00510-4.

A Novel Hybrid Multi-Modal Deep Learning for Detecting Hashtag Incongruity on Social Media.

Hashtags have been an integral element of social media platforms over the years and are widely used by users to promote, organize and connect users. Despite the intensive use of hashtags, there is no basis for using congruous tags, which causes the creation of many unrelated contents in hashtag searches. The presence of mismatched content in the hashtag creates many problems for individuals and brands. Although several methods have been presented to solve the problem by recommending hashtags based on the users' interest, the detection and analysis of the characteristics of these repetitive contents with irrelevant hashtags have rarely been addressed. To this end, we propose a novel hybrid deep learning hashtag incongruity detection by fusing visual and textual modality. We fine-tune BERT and ResNet50 pre-trained models to encode textual and visual information to encode textual and visual data simultaneously. We further attempt to show the capability of logo detection and face recognition in discriminating images. To extract faces, we introduce a pipeline that ranks faces based on the number of times they appear on Instagram accounts using face clustering. Moreover, we conduct our analysis and experiments on a dataset of Instagram posts that we collect from hashtags related to brands and celebrities. Unlike the existing works, we analyze these contents from both content and user perspectives and show a significant difference between data. In light of our results, we show that our multimodal model outperforms other models and the effectiveness of object detection in detecting mismatched information.

Femitags for feminist connected crowds in Latin America and Spain.

In recent years, women are acting all over the world against gender violence and femicide. This new wave of feminist claims is characterized by the intensive use of social media to spread consciousness and amplify influence. For this research, we analyse three femitags (i.e., feminist hashtags) from Twitter that have been relevant in different crucial mobilizations in Argentina, Spain, and Mexico. These are three hashtags with different functions for activism that have shown special relevance due to their continuity or their intensity in the Spanish-speaking area between 2015 and 2020 (before the confinement due to the COVID-19 pandemic). #NiUnaMenos (#NotASingleWomanLess) started in Argentina in 2015 and called to massive mobilizations on the streets. #Cuéntalo (#TellIt) was initiated in Spain in 2018 for sexual abuse disclosure. #NiUnaMas (#NotASingleWomanMore) trended in México around 2020 to denounce every new victim of rape or femicide. We analyse how those hashtags have spread in the Spanish-speaking region, what kind of social actors have been involved and what has been the role of opinion leaders. All data were collected with academic access to the Twitter API during December 2021. We have found that the most influential actors in the conversation are contingent and circumstantial, the leadership structure tends towards horizontality, and opinion leaders with large numbers of followers are only important in very specific moments. In all cases, femitags serve as a toolbox for action and build up an archive of grievances with a transnational dimension. Furthermore, all of them point out that structural violence against women leads to feminicide.1.

Examining Hashtag Use of #blackboyjoy and #theblackmancan and Related Content on Instagram: Descriptive Content Analysis.

BACKGROUND: Social media is widely accessible and increasingly utilized. Social media users develop hashtags and visual, text-based imagery to challenge misrepresentations, garner social support, and discuss a variety of mental health issues. Understanding how Black men are represented on social media and are using social media may be an avenue for promoting their engagement with and uptake of digital mental health interventions. OBJECTIVE: The aim of this study was to conduct a content analysis of posts containing visual and text-based components related to representations of Black men's race, gender, and behaviors. METHODS: An exploratory, descriptive content analysis was conducted for 500 Instagram posts to examine characteristics, content, and public engagement of posts containing the hashtags #theblackmancan and #blackboyjoy. Posts were selected randomly and extracted from Instagram using a social network mining tool during Fall 2018 and Spring 2019. A codebook was developed, and all posts were analyzed by 2 independent coders. Analyses included frequency counts and descriptive analysis to determine content and characteristics of posts. Mann-Whitney U tests and Kruskal-Wallis H tests were conducted to assess engagement associated with posts via likes, comments, and video views. RESULTS: Of the 500 posts extracted, most were image based (368/500, 73.6%), 272/500 (54.4%) were posted by an individual and 135/500 (27.0%) by a community organization, 269/500 (53.8%) were posted by individuals from Black populations, and 177/500 (35.4%) posts contained images of only males. Posts depicted images of Black men as fathers (100/500, 20.0%), Black men being celebrated (101/500, 20.2%), and Black men expressing joy (217/500, 43.4%). Posts (127/500, 25.4%) also depicted Black men in relation to gender atypical behavior, such as caring for children or styling their children's hair. Variables related to education and restrictive affection did not show up often in posts. Engagement via likes (median 1671, P<.001), comments (P<.001), and views (P<.001) for posts containing #theblackmancan was significantly higher compared with posts containing #blackboyjoy (median 140). Posts containing elements of celebrating Black men (P=.02) and gender atypical behavior (P<.001) also had significantly higher engagement. CONCLUSIONS: This is one of the first studies to look at hashtag use of #blackboyjoy and #theblackmancan. Posts containing #blackboyjoy and #theblackmancan promoted positive user-generated visual and text-based content on Instagram and promoted positive interactions among Black and diverse communities. With the popularity of social media and hashtag use increasing, researchers and future interventional research should investigate the potential for such imagery to serve as culturally relevant design components for digital mental health prevention efforts geared towards Black men and the communities they exist and engage with.

#Laryngology: A Standardized Hashtag Ontology.

BACKGROUND: In the past decade, there has been a rise in social media applications and usage among individuals in the otolaryngology- head and neck surgery (OHNS) community. Hashtags (#), used to identify posts relating to similar topics, are utilized to search medical information, build a network, find providers, and discuss research. Previous OHNS literature in this arena includes a standard ontology, or list of hashtags, developed for the otology subspecialty. To date, the ontology of laryngology hashtags has not been created. The objective of this study is to propose a standardized ontology to use when discussing topics in laryngology on social media to maximize reach and effect. METHODS: Using a combination of previously published techniques, along with laryngology specific adjustments, the authors developed a list of suggested hashtags. An initial list was systematically culled from laryngology Instagram accounts including academic programs, laryngology influencers (fellowship-trained laryngologists with publicly available professional accounts with greater than 500 followers), and professional societies/conferences. The list was abbreviated using current rate of use, specificity, and expert opinion. These were then categorized to include general terms, diseases and diagnoses, and treatment strategies RESULTS: Across all culled Instagram posts, there were 240 unique laryngology hashtags used and 1152 total hashtags were applied. The authors derived unique terms to be included in the ontology for laryngology by expert opinion of fellowship-trained laryngologists. CONCLUSION: Laryngology is in the early stages of utilization of social media. Developing a specific ontology of hashtags to be used will optimize the reach and connections of term specific searches.

COVID-19 vaccine sentiment analysis using public opinions on Twitter.

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs.

Migrant Racialization on Twitter during a border and a pandemic crisis.

This work examines how the outbreak of the COVID-19 pandemic reshaped the migration debate on Twitter. Through co-hashtag network analysis, time-frequency and content analysis, it shows that the pandemic was related with positive (humanitarian) and negative (threat) stances about migration. The positive side focused on the need to protect refugees stranded at camps in Greece from COVID-19. The negative focused on the Greek-Turkish land-border crisis (Evros crisis), using COVID-19 to reinforce migrants as racialized others. These findings fit the problematization of positive and negative migrant representations in the Global north as Eurocentric. In the case of camps, refugees fit well within the victim/helpless frame, justifying humanitarianism, this time on health grounds. Regarding the border crisis, refugees also fit the Eurocentric frame of violent/male/inferior other who could spread a deadly virus. Overall, COVID-19 intertwined with migration in Twitter debates, reinforcing the racialized, Eurocentric representational field on migrants from the Global south.

The body image "problem" on social media: Novel directions for the field.

Social media is a highly popular and interactive environment which has many benefits, but research has shown that its use is also associated with body image concerns. Leaked internal survey findings in September 2021 from Instagram provide some support for negative impacts on body image of teenage girls especially. In this short communication, we discuss the complexities of moderating social media platforms which aim to protect users from potentially harmful body image focused content. Several strategies have been employed and we specifically examine the moderation of pro-eating disorder hashtags (e.g., #thinspiration) and hiding of "Like" counts on Instagram in particular. We find that both are quick technical "fixes" that do not effectively address these long-standing and complex issues on social media. We provide suggestions for novel avenues of research, including that body image researchers are excellently positioned to propose specific suggestions for content moderation strategies that may actually be effective. Social media companies know that they have a major problem on their hands, as the leaked findings demonstrate, and so they may be more receptive than they have ever been to novel research findings from our community.

The pragmatic functions of emojis in Arabic tweets.

Emojis have been used on different platforms and in different languages to express feelings and emotions in online communication, which has led to their widespread familiarity among social media users worldwide. The current study is concerned with the pragmatic functions (speaker and contextual meanings) of emojis in Arabic tweets. The study used mixed methods to analyze the use of emojis and emoji position to identify their functions and possible gender similarities and differences. The dataset of this study consists of 421 Arabic tweets by Arab users at the beginning of 2022 (149 by male users and 272 by females). This study addressed three questions: (1) What are the common emojis used in Arabic tweets and their position in the tweet? (2) What are the pragmatic functions of these emojis? (3) What are the possible differences and similarities between male and female users in the use of emojis? The findings revealed that Loudly Crying Face, Red Heart, Face with Tears of Joy, Broken Heart, Smiling Face with Heart-Eyes, Pleading Face, Slightly Smiling Face, Pensive Face, and Weary Face were preponderant in Arabic tweets. Also, this study found seven pragmatic functions ranging from the most to the least frequent: Multiple functions, Reaction, Action, Decoration, Physical action, Softening, and Tone modification. Regarding the role of gender, there were both similarities and differences in terms of the frequency of emoji use, emoji position, and function. Moreover, the findings showed the importance of context to interpreting the functions of emojis. Finally, the findings have implications for emoji designers and Natural Language Processing (NLP).

#workfromhome: how multi-level marketers enact and subvert federal language policy for profit.

This paper analyzes how multi-level marketing companies (MLMs), via direct selling through electronic commerce (e-commerce) and social media, enact and evade federal language policy to maximize profits. Here we describe the federal language policies that govern this type of e-commerce, and in particular, the language policies of the Federal Trade Commission, which dictate what can and cannot be communicated by MLM companies and their contractors. We then illustrate how these federal language policies are enacted, and at times subverted, for financial gain during the COVID-19 economic and health crisis which rendered many people vulnerable. We draw on the discourse analysis of public documents, MLM insider sources via the first author, and over 100,000 public Instagram posts published by MLM independent contractors collected with the third-party Instagram data extraction tool, Phantombuster. We find that MLM independent contractors, although varying widely with respect to their enactment of federal and corporate policy, frequently reference COVID-19 implicitly or explicitly, a practice prohibited by federal policy. We demonstrate that quantitative and qualitative discourse analysis of language policies and practices of MLM social media provides a productive lens for understanding both the communication challenges of and responses to the COVID-19 pandemic. This approach reveals the variable ways in which language policies are taken up and discourses recontextualized with new meanings and for new purposes across social media platforms.