Since the outbreak in China in late 2019, the novel coronavirus (COVID-19) has spread around the world and has come to dominate online conversa ons. By linking 2.3 million Twi er users to loca ons within the United States, we study in aggregate how poli cal characteris cs of the loca ons affect the evolu on of online discussions about COVID-19. We show that COVID-19 cha er in the US is largely shaped by poli cal polariza on. Par sanship correlates with sen ment toward government measures and the tendency to share health and preven on messaging. Crossideological interac ons are modulated by user segrega on and polarized network structure. We also observe a correlaon between user engagement with topics related to public health and the varying impact of the disease outbreak in different US states. These findings may help inform policies both online and offline. Decision-makers may calibrate their use of online pla orms to measure the effec veness of public health campaigns, and to monitor the recep on of na onal and state-level policies, by tracking in real-me discussions in a highly polarized social media ecosystem.

Background Social media chatter in 2020 has been largely dominated by the COVID-19 pandemic. Existing research shows that COVID-19 discourse is highly politicized, with political preferences linked to beliefs and disbeliefs about the virus. As it happens with topics that become politicized, people may fall into echo chambers, which is the idea that one is only presented with information they already agree with, thereby reinforcing one’s confirmation bias. Understanding the relationship between information dissemination and political preference is crucial for effective public health communication. Objective We aimed to study the extent of polarization and examine the structure of echo chambers related to COVID-19 discourse on Twitter in the United States. Methods First, we presented Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provided new insights into the characterization of partisan users. Results We observed that right-leaning users were noticeably more vocal and active in the production and consumption of COVID-19 information. We also found that most of the highly influential users were partisan, which may contribute to further polarization. Importantly, while echo chambers exist in both the right- and left-leaning communities, the right-leaning community was by far more densely connected within their echo chamber and isolated from the rest. Conclusions We provided empirical evidence that political echo chambers are prevalent, especially in the right-leaning community, which can exacerbate the exposure to information in line with pre-existing users’ views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online.

Background The novel coronavirus, also known as SARS-CoV-2, has come to define much of our lives since the beginning of 2020. During this time, countries around the world imposed lockdowns and social distancing measures. The physical movements of people ground to a halt, while their online interactions increased as they turned to engaging with each other virtually. As the means of communication shifted online, information consumption also shifted online. Governing authorities and health agencies have intentionally shifted their focus to use social media and online platforms to spread factual and timely information. However, this has also opened the gate for misinformation, contributing to and accelerating the phenomenon of misinfodemics. Objective We carried out an analysis of Twitter discourse on over 1 billion tweets related to COVID-19 over a year to identify and investigate prevalent misinformation narratives and trends. We also aimed to describe the Twitter audience that is more susceptible to health-related misinformation and the network mechanisms driving misinfodemics. Methods We leveraged a data set that we collected and made public, which contained over 1 billion tweets related to COVID-19 between January 2020 and April 2021. We created a subset of this larger data set by isolating tweets that included URLs with domains that had been identified by Media Bias/Fact Check as being prone to questionable and misinformation content. By leveraging clustering and topic modeling techniques, we identified major narratives, including health misinformation and conspiracies, which were present within this subset of tweets. Results Our focus was on a subset of 12,689,165 tweets that we determined were representative of COVID-19 misinformation narratives in our full data set. When analyzing tweets that shared content from domains known to be questionable or that promoted misinformation, we found that a few key misinformation narratives emerged about hydroxychloroquine and alternative medicines, US officials and governing agencies, and COVID-19 prevention measures. We further analyzed the misinformation retweet network and found that users who shared both questionable and conspiracy-related content were clustered more closely in the network than others, supporting the hypothesis that echo chambers can contribute to the spread of health misinfodemics. Conclusions We presented a summary and analysis of the major misinformation discourse surrounding COVID-19 and those who promoted and engaged with it. While misinformation is not limited to social media platforms, we hope that our insights, particularly pertaining to health-related emergencies, will help pave the way for computational infodemiology to inform health surveillance and interventions.

Motivation The complete characterization of enzymatic activities between molecules remains incomplete, hindering biological engineering and limiting biological discovery. We develop in this work a technique, Enzymatic Link Prediction (ELP), for predicting the likelihood of an enzymatic transformation between two molecules. ELP models enzymatic reactions catalogued in the KEGG database as a graph. ELP is innovative over prior works in using graph embedding to learn molecular representations that capture not only molecular and enzymatic attributes but also graph connectivity. Results We explore transductive (test nodes included in the training graph) and inductive (test nodes not part of the training graph) learning models. We show that ELP achieves high AUC when learning node embeddings using both graph connectivity and node attributes. Further, we show that graph embedding improves link prediction by 30% in AUC over fingerprint-based similarity approaches and by 8% over Support Vector Machines. We compare ELP against rule-based methods. We also evaluate ELP for predicting links in pathway maps and for reconstruction of edges in reaction networks of four common gut microbiota phyla: actinobacteria, bacteroidetes, firmicutes and proteobacteria. To emphasize the importance of graph embedding in the context of biochemical networks, we illustrate how graph embedding can guide visualization. Availability The code and datasets are available through https://github.com/HassounLab/ELP

UNSTRUCTURED During 2020, social media chatter has been largely dominated by the COVID-19 pandemic. In this paper, we study the extent of polarization of COVID-19 discourse on Twitter in the U.S. First, we propose Retweet-BERT, a scalable and highly accurate model for estimating user polarity by leveraging language features and network structures. Then, by analyzing the user polarity predicted by Retweet-BERT, we provide new insights into the characterization of partisan users. Right-leaning users, we find, are noticeably more vocal and active in the production and consumption of COVID-19 information. Our analysis also shows that most of the highly influential users are partisan, which may contribute to further polarization. Crucially, we provide empirical evidence that political echo chambers are prevalent, exacerbating the exposure to information in line with pre-existing users' views. Our findings have broader implications in developing effective public health campaigns and promoting the circulation of factual information online.