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Register a new userUnderstanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic
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Shuo Zhang
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two new communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, evidenced by both language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly loyal. To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence.
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The COVID-19 pandemic has affected peoples lives around the world on an unprecedented scale. We intend to investigate hoarding behaviors in response to the pandemic using large-scale social media data. First, we collect hoarding-related tweets shortly after the outbreak of the coronavirus. Next, we analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from March 1 to April 30, 2020, and dissect the hoarding-related tweets by age, gender, and geographic location. We find the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population. Furthermore, using topic modeling, we investigate the opinions expressed towards the hoarding behavior by categorizing these topics according to demographic and geographic groups. We also calculate the anxiety scores for the hoarding and anti-hoarding related tweets using a lexical approach. By comparing their anxiety scores with the baseline Twitter anxiety score, we reveal further insights. The LIWC anxiety mean for the hoarding-related tweets is significantly higher than the baseline Twitter anxiety mean. Interestingly, beer has the highest calculated anxiety score compared to other hoarded items mentioned in the tweets.
Successful navigation of the Covid-19 pandemic is predicated on public cooperation with safety measures and appropriate perception of risk, in which emotion and attention play important roles. Signatures of public emotion and attention are present in social media data, thus natural language analysis of this text enables near-to-real-time monitoring of indicators of public risk perception. We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from ~20 million unique Covid-19-related tweets from 12 countries posted between 10th March -- 14th June 2020. We find evidence of psychophysical numbing: Twitter users increasingly fixate on mortality, but in a decreasingly emotional and increasingly analytic tone. Semantic network analysis based on word co-occurrences reveals changes in the emotional framing of Covid-19 casualties that are consistent with this hypothesis. We also find that the average attention afforded to national Covid-19 mortality rates is modelled accurately with the Weber-Fechner and power law functions of sensory perception. Our parameter estimates for these models are consistent with estimates from psychological experiments, and indicate that users in this dataset exhibit differential sensitivity by country to the national Covid-19 death rates. Our work illustrates the potential utility of social media for monitoring public risk perception and guiding public communication during crisis scenarios.
Identifying superspreaders of disease is a pressing concern for society during pandemics such as COVID-19. Superspreaders represent a group of people who have much more social contacts than others. The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via peoples ubiquitous mobile devices. This technology offers promise for detecting superspreaders. In this paper, we propose a general framework for WLAN-log-based superspreader detection. In our framework, we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions. Next, we adopt three vertex centrality measurements over the contact graphs to generate three groups of superspreader candidates. Finally, we leverage SEIR simulation to determine groups of superspreaders among these candidates, who are the most critical individuals for the spread of disease based on the simulation results. We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university. Our evaluation shows superspreaders exist on university campuses. They change over the first few weeks of a semester, but stabilize throughout the rest of the term. The data also demonstrate that both symmetric and asymmetric contact tracing can discover superspreaders, but the latter performs better with daily contact graphs. Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i.e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework. We believe our proposed framework and these results may provide timely guidance for public health administrators regarding effective testing, intervention, and vaccination policies.
We conduct a large-scale social media-based study of oral health during the COVID-19 pandemic based on tweets from 9,104 Twitter users across 26 states (with sufficient samples) in the United States for the period between November 12, 2020 and June 14, 2021. To better understand how discussions on different topics/oral diseases vary across the users, we acquire or infer demographic information of users and other characteristics based on retrieved information from user profiles. Women and younger adults (19-29) are more likely to talk about oral health problems. We use the LDA topic model to extract the major topics/oral diseases in tweets. Overall, 26.70% of the Twitter users talk about wisdom tooth pain/jaw hurt, 23.86% tweet about dental service/cavity, 18.97% discuss chipped tooth/tooth break, 16.23% talk about dental pain, and the rest are about tooth decay/gum bleeding. By conducting logistic regression, we find that discussions vary across user characteristics. More importantly, we find social disparities in oral health during the pandemic. Specifically, we find that health insurance coverage rate is the most significant predictor in logistic regression for topic prediction. People from counties with higher insurance coverage tend to tweet less about all topics of oral diseases. People from counties at a higher risk of COVID-19 talk more about tooth decay/gum bleeding and chipped tooth/tooth break. Older adults (50+), who are vulnerable to COVID-19, are more likely to discuss dental pain. To our best knowledge, this is the first large-scale social media-based study to analyze and understand oral health in America amid the COVID-19 pandemic. We hope the findings of our study through the lens of social media can provide insights for oral health practitioners and policy makers.
COVID-19 has affected the world economy and the daily life routine of almost everyone. It has been a hot topic on social media platforms such as Twitter, Facebook, etc. These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread. Twitters retweet functionality allows users to share the existing content with other users without altering the original content. Analysis of social media platforms can help in detecting emergencies during pandemics that lead to taking preventive measures. One such type of analysis is predicting the number of retweets for a given COVID-19 related tweet. Recently, CIKM organized a retweet prediction challenge for COVID-19 tweets focusing on using numeric features only. However, our hypothesis is, tweet text may play a vital role in an accurate retweet prediction. In this paper, we combine numeric and text features for COVID-19 related retweet predictions. For this purpose, we propose two CNN and RNN based models and evaluate the performance of these models on a publicly available TweetsCOV19 dataset using seven different evaluation metrics. Our evaluation results show that combining tweet text with numeric features improves the performance of retweet prediction significantly.
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