Prior social contagion models consider the spread of either one contagion on interdependent networks or multiple contagions on single layer networks, usually under assumptions of competition. We propose a new threshold model for the diffusion of multiple contagions. Individuals are placed on a multiplex network with a periodic lattice layer and a random-regular-graph layer. On these population structures, we study the interface between two key aspects of the diffusion process: the level of synergy between two contagions, and the rate at which individuals become dormant after adoption. Dormancy is defined as a looser form of immunity that limits active spreading but without conferring resistance. Monte Carlo simulations reveal lower synergy makes contagions more susceptible to percolation, especially those that diffuse on lattices. Faster diffusion of one contagion with dormancy probabilistically blocks the diffusion of the other, in a way similar to ring vaccination. We show that within a band of synergy, bimodal or trimodal branchings occur on the slower contagion on the lattice. We also show complimentary contagions can provide a synergistic boost to help spread contagions that have almost gone dormant.

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.

Using more than 4 billion tweets and labels on more than 5 million users, this paper compares the behavior of humans and bots politically and semantically during the pandemic. Results reveal liberal bots are more central than humans in general, but less important than institutional humans as the elite circle grows smaller. Conservative bots are surprisingly absent when compared to prior work on political discourse, but are better than liberal bots at eliciting replies from humans, which suggest they may be perceived as human more frequently. In terms of topic and framing, conservative humans and bots disproportionately tweet about the Bill Gates and bio-weapons conspiracy, whereas the 5G conspiracy is bipartisan. Conservative humans selectively ignore mask-wearing and we observe prevalent out-group tweeting when discussing policy. We discuss and contrast how humans appear more centralized in health-related discourse as compared to political events, which suggests the importance of credibility and authenticity for public health in online information diffusion.

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From fact-checking chatbots to community-maintained misinformation databases, Taiwan has emerged as a critical case-study for citizen participation in politics online. Due to Taiwan’s geopolitical history with China, the recent 2020 Taiwanese Presidential Election brought fierce levels of online engagement led by citizens from both sides of the strait. In this article, we study misinformation and digital participation on three platforms, namely Line, Twitter, and Taiwan’s Professional Technology Temple (PTT, Taiwan’s equivalent of Reddit). Each of these platforms presents a different facet of the elections. Results reveal that the greatest level of disagreement occurs in discussion about incumbent president Tsai. Chinese users demonstrate emergent coordination and selective discussion around topics like China, Hong Kong, and President Tsai, whereas topics like Covid-19 are avoided. We discover an imbalance of the political presence of Tsai on Twitter, which suggests partisan practices in disinformation regulation. The cases of Taiwan and China point toward a growing trend where regular citizens, enabled by new media, can both exacerbate and hinder the flow of misinformation. The study highlights an overlooked aspect of misinformation studies, beyond the veracity of information itself, that is the clash of ideologies, practices, and cultural history that matter to democratic ideals.

This study proposes a strategy to make the lookback option cheaper and more practical, and suggests the use of its properties to reduce risk exposure in cryptocurrency markets through blockchain enforced smart contracts and correct for informational inefficiencies surrounding prices and volatility. This paper generalizes partial, discretely-monitored lookback options that dilute premiums by selecting a subset of specified periods to determine payoff, which we call amnesiac lookback options. Prior literature on discretely-monitored lookback options considers the number of periods and assumes equidistant lookback periods in pricing partial lookback options. This study by contrast considers random sampling of lookback periods and compares resulting payoff of the call, put and spread options under floating and fixed strikes. Amnesiac lookbacks are priced with Monte Carlo simulations of Gaussian random walks under equidistant and random periods. Results are compared to analytic and binomial pricing models for the same derivatives. Simulations show diminishing marginal increases to the fair price as the number of selected periods is increased. The returns correspond to a Hill curve whose parameters are set by interest rate and volatility. We demonstrate over-pricing under equidistant monitoring assumptions with error increasing as the lookback periods decrease. An example of a direct implication for event trading is when shock is forecasted but its timing uncertain, equidistant sampling produces a lower error on the true maximum than random choice. We conclude that the instrument provides an ideal space for investors to balance their risk, and as a prime candidate to hedge extreme volatility. We discuss the application of the amnesiac lookback option and path-dependent options to cryptocurrencies and blockchain commodities in the context of smart contracts.

The Fields Medal, often referred as the Nobel Prize of mathematics, is awarded to no more than four mathematicians under the age of 40, every 4 years. In recent years, its conferral has come under scrutiny of math historians, for rewarding the existing elite rather than its original goal of elevating under-represented mathematicians. Prior studies of elitism focus on citational practices while a characterization of the structural forces that prevent access remain unclear. Here we show the flow of elite mathematicians between countries and lingo-ethnic identity, using network analysis and natural language processing on 240,000 mathematicians and their advisor–advisee relationships. We present quantitative evidence of how the Fields Medal helped integrate Japan after WWII, through analysis of the elite circle formed around Fields Medalists. We show increases in pluralism among major countries, though Arabic, African, and East Asian identities remain under-represented at the elite level. Our results demonstrate concerted efforts by academic committees, such as prize giving, can either reinforce the existing elite or reshape its definition. We anticipate our methodology of academic genealogical analysis can serve as a useful diagnostic for equity and systemic bias within academic fields.

Stating your gender pronouns, along with your name, is becoming the new norm of self-introductions at school, at the workplace, and online. The increasing prevalence and awareness of nonconforming gender identities put discussions of developing gender-inclusive language at the forefront. This work presents the first empirical research on gender pronoun usage on large-scale social media. Leveraging a Twitter dataset of over 2 billion tweets collected continuously over two years, we find that the public declaration of gender pronouns is on the rise, with most people declaring as using she series pronouns, followed by he series pronouns, and a smaller but considerable amount of non-binary pronouns. From analyzing Twitter posts and sharing activities, we can discern users who use gender pronouns from those who do not and also distinguish users of various gender identities. We further illustrate the relationship between explicit forms of social network exposure to gender pronouns and their eventual gender pronoun adoption. This work carries crucial implications for gender-identity studies and initiates new research directions in gender-related fairness and inclusion, as well as support against online harassment and discrimination on social media. CCS Concepts: • Human-centered computing → Empirical studies in HCI; Empirical studies in collaborative and social computing.