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      Public Priorities and Concerns Regarding COVID-19 in an Online Discussion Forum: Longitudinal Topic Modeling


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          INTRODUCTION Given the rapidly changing nature of the coronavirus disease 2019 (COVID-19) pandemic, real-time monitoring of COVID-19 cases and deaths has been widely embraced. 1 The pandemic has also been accompanied by an “infodemic,” an overabundance of information and misinformation. 2 Public response to the pandemic and infodemic is important, but undermeasured. 3 Real-time analysis of public response could lead to earlier recognition of changing public priorities, fluctuations in wellness, and uptake of public health measures, all of which carry implications for individual- and population-level health. 3 To test this hypothesis, we measured daily changes in the frequency of topics of discussion across 94,467 COVID-19-related comments on an online public forum in March, 2020. METHODS Reddit is the 19th most popular website in the world with 420 million monthly active users. 4 Between March 3 and March 31, 2020, we obtained all comments from the “Daily Discussion Post” on “r/Coronavirus,” the most popular COVID-19 subreddit with 1.9 million members. We defined 50 discussion topics, groups of commonly co-occurring words, using a machine learning based approach to natural language processing, latent Dirichlet allocation (LDA). 5 For each of the 50 topics, we reviewed the ten words and comments most associated with each topic. 6 We identified topics that fell into three categories of interest: response to public health measures, impact on daily life, and sense of pandemic severity. We tracked daily variations in the average prevalence of topics across all comments. In order to improve visualization of patterns of topic change, we used locally estimated scatterplot smoothing (LOESS) lines. To quantify the degree of change in prevalence, we compared 4-day periods using the two-proportion z-test. We used R version 3.6.1 for all analyses. All data was publicly available, and the study was considered exempt under University of Pennsylvania Institutional Review Board guidelines. RESULTS In the 29 days between March 3 and March 31, we collected 94,467 posts from r/Coronavirus daily discussion threads, with peak activity between March 15 and 17 (16% of comments). Of the 50 LDA topics (available by request), ten pertained to the three categories of interest. Other topics included those related to news sharing, political discussions, and discussions about the science of COVID-19. Table 1 shows key topic words and representative comments, and Figure 1 displays the change in topic frequency over time by category. In the “public health measures” category, for instance, “hand washing” became less prevalent throughout March (2.7% from March 3 to March 6 vs 1.9% from March 28 to March 31, p < .001; two-proportion z-test). “Impact on daily life” topics showed “travel” peaking early and dropping throughout the month (3.2% March 3–March 6 vs 1.0% March 28–March 31, p < .001) and concern regarding “personal finances” increasing (1.5% March 3–March 6 vs 2.1% March 28–March 31, p = .003). “Sense of pandemic severity” evolved over the month, with fewer comments comparing COVID-19 with the flu (2.3% March 3–March 6 vs 1.8% March 28–March 31, p = .04) and mid- to late-month growth in comments reporting numbers of cases and deaths (2.1% March 12–March 15 vs 2.7% March 28–March 31, p = .001). Table 1 Latent Dirichlet Allocation Topics from a Coronavirus Subreddit Throughout March, 2020, with a Collection of Top Words Used to Define the Topic and a Redacted Representative Reddit Comment Topic Category Topic Top words Redacted representative Reddit comment (to preserve user anonymity) Public health measures Hand washing hands, wash, touch, use, water, soap “At least get them to wash hands as soon as they get back and wash clothes” Outdoor safety stay, people, away, home, outside, safe “It’s okay to go for a walk, just try to stay at least 6 feet from others.” Masks masks, wear, face, n95, use, make “What type of filter to insert in a cotton mask? Ordering some cotton masks with an insert to add a filter. Would an air conditioner filter work?” Daily life impact Food and supplies food, grocery, people, store, toilet, buy “Just went to my local grocery store this morning. The place was packed with folks… saw a ton of people buying paper towels, toilet paper etc.that aisle was almost empty.” Travel changes travel, back, trip, US, flight, cancel “Going to a wedding in Canada next month. What are the odds travel is banned between the last weeks of April?” School closing school, closed, still, public, kids, university “Gov has closed all K-12 schools in [state] starting Monday until early April.” Personal finances work, get, pay, money, need, help “My work just closed until further notice. I work in food service industry. What are my options for govenrment financial assistance? I do not have paid sick leave or paid time off.” Sense of pandemic severity Number of cases and deaths cases, number, deaths, new, confirmed “So if these numbers are correct, US is now third in total cases behind China and Italy, and FIRST in new cases, surpassing Italy. And we are supposed to be ~10 days behind Italy.” Comparison to flu flu, like, coronavirus, much, bad, worse “There is no way this virus is as bad as people are saying it is. Do not about 61,000 people die every year from flu?” Danger to elderly rate, death, mortality, age, higher, risk “The case fatality rate in Italy was 1.0%, but with a much more elderly population, in which coronavirus death rate is much higher” Fig. 1 The change in the prevalence over the month of March, 2020, in Reddit comment content related to a public health measures, b daily life impact, and c sense of pandemic severity. Lines show locally estimated scatterplot smoothing (LOESS) for the daily average prevalence of the topic across all comments; shaded grey area represents the standard error of the LOESS estimation. DISCUSSION This analysis indicates that longitudinal topic modeling of Reddit content is effective in identifying patterns of public dialogue and could be used to guide targeted interventions. For instance, comparisons to the flu were embraced by the public. Early recognition of this reality could have led to more specific information dissemination campaigns and earlier public acknowledgement of disease severity. Questions about safely spending time outdoors peaked in mid-March, representing a missed opportunity for public guidance. Tracking and responding proactively to common questions, such as what material is best used for a homemade mask, may minimize the spread of misinformation. Notably missing from these Reddit topics were discussions of contact tracing, a growing area of public concern. Limitations of this study include that Reddit users are not representative of all segments of the population, and that Reddit data is not associated with a geographic location. Real-time monitoring of online COVID-19 dialogue holds promise for more dynamically understanding and responding to needs in public health emergencies.

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          How to fight an infodemic

          WHO's newly launched platform aims to combat misinformation around COVID-19. John Zarocostas reports from Geneva. WHO is leading the effort to slow the spread of the 2019 coronavirus disease (COVID-19) outbreak. But a global epidemic of misinformation—spreading rapidly through social media platforms and other outlets—poses a serious problem for public health. “We’re not just fighting an epidemic; we’re fighting an infodemic”, said WHO Director-General Tedros Adhanom Ghebreyesus at the Munich Security Conference on Feb 15. Immediately after COVID-19 was declared a Public Health Emergency of International Concern, WHO's risk communication team launched a new information platform called WHO Information Network for Epidemics (EPI-WIN), with the aim of using a series of amplifiers to share tailored information with specific target groups. Sylvie Briand, director of Infectious Hazards Management at WHO's Health Emergencies Programme and architect of WHO's strategy to counter the infodemic risk, told The Lancet, “We know that every outbreak will be accompanied by a kind of tsunami of information, but also within this information you always have misinformation, rumours, etc. We know that even in the Middle Ages there was this phenomenon”. “But the difference now with social media is that this phenomenon is amplified, it goes faster and further, like the viruses that travel with people and go faster and further. So it is a new challenge, and the challenge is the [timing] because you need to be faster if you want to fill the void…What is at stake during an outbreak is making sure people will do the right thing to control the disease or to mitigate its impact. So it is not only information to make sure people are informed; it is also making sure people are informed to act appropriately.” About 20 staff and some consultants are involved in WHO's communications teams globally, at any given time. This includes social media personnel at each of WHO's six regional offices, risk communications consultants, and WHO communications officers. Aleksandra Kuzmanovic, social media manager with WHO's department of communications, told The Lancet that “fighting infodemics and misinformation is a joint effort between our technical risk communications [team] and colleagues who are working on the EPI-WIN platform, where they communicate with different…professionals providing them with advice and guidelines and also receiving information”. Kuzmanovic said, “In my role, I am in touch with Facebook, Twitter, Tencent, Pinterest, TikTok, and also my colleagues in the China office who are working closely with Chinese social media platforms…So when we see some questions or rumours spreading, we write it down, we go back to our risk communications colleagues and then they help us find evidence-based answers”. “Another thing we are doing with social media platforms, and that is something we are putting our strongest efforts in, is to ensure no matter where people live….when they’re on Facebook, Twitter, or Google, when they search for ‘coronavirus’ or ‘COVID-19’ or [a] related term, they have a box that…directs them to a reliable source: either to [the] WHO website to their ministry of health or public health institute or centre for disease control”, she said. Google, Kuzmanovic noted, has created an SOS Alert on COVID-19 for the six official UN languages, and is also expanding in some other languages. The idea is to make the first information that the public receive be from the WHO website and the social media accounts of WHO and Dr Tedros. WHO also uses social media for real-time updates. WHO is also working closely with UNICEF and other international agencies that have extensive experience in risk communications, such as the International Federation of Red Cross and Red Crescent Societies. Carlos Navarro, head of Public Health Emergencies at UNICEF, the children's agency, told The Lancet that while a lot of incorrect information is spreading through social media, a lot is also coming from traditional mass media. “Often, they pick the most extreme pictures they can find…There is overkill on the use of [personal protective equipment] and that tends to be the photos that are published everywhere, in all major newspapers and TV…that is, in fact, sending the wrong message”, Navarro said. David Heymann, professor of infectious disease epidemiology at the London School of Hygiene & Tropical Medicine, told The Lancet that the traditional media has a key role in providing evidence-based information to the general public, which will then hopefully be picked up on social media. He also observed that for both social and conventional media, it is important that the public health community help the media to “better understand what they should be looking for, because the media sometimes gets ahead of the evidence”.
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              Is Open Access

              Studying expressions of loneliness in individuals using twitter: an observational study

              Objectives Loneliness is a major public health problem and an estimated 17% of adults aged 18–70 in the USA reported being lonely. We sought to characterise the (online) lives of people who mention the words ‘lonely’ or ‘alone’ in their Twitter timeline and correlate their posts with predictors of mental health. Setting and design From approximately 400 million tweets collected from Twitter in Pennsylvania, USA, between 2012 and 2016, we identified users whose Twitter posts contained the words ‘lonely’ or ‘alone’ and compared them to a control group matched by age, gender and period of posting. Using natural-language processing, we characterised the topics and diurnal patterns of users’ posts, their association with linguistic markers of mental health and if language can predict manifestations of loneliness. The statistical analysis, data synthesis and model creation were conducted in 2018–2019. Primary outcome measures We evaluated counts of language features in the users with posts including the words lonely or alone compared with the control group. These language features were measured by (a) open-vocabulary topics, (b) Linguistic Inquiry Word Count (LIWC) lexicon, (c) linguistic markers of anger, depression and anxiety, and (d) temporal patterns and number of drug words. Using machine learning, we also evaluated if expressions of loneliness can be predicted in users’ timelines, measured by area under curve (AUC). Results Twitter timelines of users (n=6202) with posts including the words lonely or alone were found to include themes about difficult interpersonal relationships, psychosomatic symptoms, substance use, wanting change, unhealthy eating and having troubles with sleep. Their posts were also associated with linguistic markers of anger, depression and anxiety. A random forest model predicted expressions of loneliness online with an AUC of 0.86. Conclusions Users’ Twitter timelines with the words lonely or alone often include psychosocial features and can potentially have associations with how individuals express and experience loneliness. This can inform low-resource online assessment for high-risk individuals experiencing loneliness and interventions focused on addressing morbidities in this condition.

                Author and article information

                J Gen Intern Med
                J Gen Intern Med
                Journal of General Internal Medicine
                Springer International Publishing (Cham )
                12 May 2020
                : 1-4
                [1 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Penn Medicine Center for Digital Health, , University of Pennsylvania, ; Philadelphia, PA USA
                [2 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Center for Emergency Care Policy and Research, Department of Emergency Medicine, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                [3 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Computer and Information Science, , University of Pennsylvania, ; Philadelphia, PA USA
                Author information
                © Society of General Internal Medicine 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                : 20 April 2020
                : 28 April 2020
                Concise Research Report

                Internal medicine
                Internal medicine


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