In 1949, the computer pioneer John von Neumann published Theory and Organization of Complicated Automata in which he identified the virality of a computer program as its ability to self-replicate.1 Two decades later, Frederick Cohen proved this theory with a mathematic equation, subsequently coining the term ‘computer virus’ as ‘a program that can infect other programs by modifying them to include a, possibly evolved, version of itself.’2 Today, computer viruses have evolved into advanced persistent threats (APTs) expertly designed to elude detection, as has the tendency to describe cybersecurity phenomena using metaphors from infectious disease.
Language such as “antivirus pattern matching,” “code sanitization,” “index cases,” “infected nodes,” and “vulnerable networks” is now endemic. The epidemiological metaphors of everyday software parlance are thus well established. What is less recognized, however, is the flip side of this analogy: that innovations within network theory, cybersecurity, and data science are revolutionizing the study of infectious disease. In the throes of a global pandemic, the moment is ripe to deploy methods and theories from network theory to combat disease spread. One cannot evade the irony of this timely inversion wherein such innovations might provide solutions to issues that are, at times, their namesake.
What follows is an initial attempt to demonstrate the reflexivity of paradigms in network theory and epidemiology. This exercise is anchored in the titular suffix -demics, which stems from the Greek word for populations demos. This shared etymon makes for a fruitful comparison as it signals the intrinsically social import of both fields: from the transmission of disease in pandemics to the dissemination of knowledge in academia, and more recently, the ostensibly viral edema of disinformation online, one’s approach to each of these subjects is determined by the nature of spreading phenomena.
By presenting possible avenues for their convergence, this reading list seeks to spawn new perspectives for tackling pressing issues across a range of adverse environments from disease spread, cybersecurity in public health data, the virality of online disinformation, and more. In other words, only by leveling these disciplines do we discover actionable revelations that can be funneled into an integrative panacea for these post-normal times. This reading list consists of four cross-analyses that can be categorized under the following headings: 1) Epidemiology for Network Theory; 2) Public Health + Big Data; 3) Infodemics: the Structural Virality of Disinformation; 4) Viral Knowledge in the Post-Digital Age.
This section introduces the implications of epidemiological models and big data analytics for enhancing cybersecurity. Owing to the fact that biomedical research routinely simulates models from big data analytics, extending the analogy between cybersecurity and fields such as epidemiology is a useful exercise. By modeling methodologies that identify statistical associations between mutations and diseases, computer scientists are able to more precisely identify patterns of threat activity for individual hosts in real-time. Moreover, adopting transmission models used in epidemiology allows for researchers to explore vulnerabilities of networks and to define predictive algorithms for the analysis of societal events based on open source data. This produces sophisticated metrics for cybersecurity in determining the probabilities of malware infection and/or information contagion. Lastly, mapping networks of influence in such a way offers a new lens to analyze the emergence of collective phenomena.
While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. Through collecting threat log data to observe the patterns of threat activity for individual hosts through network-wide scans, this methodology associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine the probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cybersecurity.
The emergence of big data analytics—predictive and highly autonomous—is a game changer for in cyber security. This is a current trend, still immature but already noticeable and influential. There is an approach inspired by genetic epidemiology where features of a computer host—such as the network services active on a computer—are statistically linked to the kinds of threats to which that host is likely to be susceptible. This is similar to the tools used in genetics to identify statistical associations between mutations and diseases, and like in the case of genetic epidemiology relies on the availability of large volumes of data, especially when rare conditions are of interest.
This research focuses on web-based traffic with client-server architecture and adopts simple probability-based transmission models used in epidemiology to explore the vulnerability of the URI web-network to anticipated threats. Relying on a set of intuitive assumptions, we simulate the spread of infection on the dynamic bipartite graph inferred from observed external and modeled unobserved internal web-browsing traffic and evaluate the susceptibility of URI nodes to threats initiated by random clients and clients from specific countries.
The report represents a breakthrough case study in the capacity to identify cyber swarms and viral insurgencies in nearly real-time as they are developing in plain sight. It is an analysis of over 100 million social media comments through which the authors demonstrate how “a joke for some, acts as a violent meme that circulates instructions for a violent, viral insurgency for others.” This phenomenon should be of particular concern, the authors note, for the military for whom “the meme’s emphasis on military language and culture poses a special risk.” Because most of law enforcement and the military remain ignorant of “memetic warfare,” extremists who employ it “possess a distinct advantage over government officials and law enforcement.”3
Since the beginning of the twenty-first century, two new threats have received increased attention: biological warfare (BW) and cyber warfare. While it may appear that these two threats have little in common, they share several characteristics that have significant implications for international security. This article examines the two modalities side-by-side to review these common characteristics. In light of these commonalities and due to the extensive experience and rich history of dealing with BW threats, strategies for enhancing cybersecurity could advance more quickly by drawing meaningful insights from the biological warfare experience, such as the prospect of developing constraining international norms.
While the use of and reliance on the Internet continues to grow, it is clear that governments, industries, and individuals will need to find innovative models and means for cooperation to reduce cyber threats that impact citizens. An Internet Health Model is a conceptual framework to help guide a global, coordinated approach to improving security on the Internet. Improving the health of the Internet requires a global, collaborative approach to protecting people from potential dangers online.
During the COVID-19 pandemic, we have heard daily the importance of good hygiene – acts meant to keep us safe as the virus continues to take its somewhat unpredictable course. We must also strengthen our cyber hygiene to ensure computer users are not unnecessarily exposing themselves to computer viruses. Malware, ransomware, and other nefarious cyber viruses can infect computers, causing them to become “ill,” failing to operate as they were designed and hurting us in the process. This article explains how cybercriminals are leveraging the concerns and fears over COVID-19 to steal passwords, data, and money.
This section illustrates how certain aspects of information sharing and analysis for cyber defense are worth emulating to help detect and manage public health threats; the ethical challenges around public health data; the implications of social media data in digital disease detection; the increasing complexity of cybersecurity threats to health care systems; an overview of AI and web-based participatory surveillance of infectious diseases; characteristics of mixing networks as they inform cybernetic determination and subsumption; and the benefits of integrating the fields of epidemiology, biostatistics, and data science, particularly during a public health crisis. These novel processes invoke new considerations for public disease control and prevention, including social determination as a renewed challenge for critical epidemiology.
This is a timely paper for a number of reasons, all of which are concisely embodied within the title. Ethical challenges are not something we consider often enough in this or most biomedical research journals. Yet, such challenges increasingly confront us as private citizens providing health data and as researchers using these data for public health and research purposes. Big data is an ill-defined term, yet there can be no denying that social media data have the potential for important public health uses, but also have risks. Use of these data far outpaces the governance and due diligence of the ethical considerations that need to be addressed, such as stigmatization of particular communities and the infringement of individual freedoms. The advent of social media and big data is likely to exacerbate problems associated with current disease detection and public health approaches.
Malicious software and infectious diseases are similar is several respects, as are the functional requirements for surveillance and intelligence to defend against these threats. Given these similarities, this article compares and contrasts the actors, relationships, and norms at work in cyber intelligence and disease surveillance. Historical analysis reveals that civilian cyber defense is more decentralized, private, and voluntary than public health in the United States. Information sharing about malware is also limited, despite information technology being integral to cyberspace. Such limits suggest that automation through electronic health records will not automatically improve public health surveillance. Still, certain aspects of information sharing and analysis for cyber defense are worth emulating or, at the very least, learning from to help detect and manage health threats.
Epidemiology, biostatistics, and data science are broad disciplines that incorporate a variety of substantive areas. Common among them is a focus on quantitative approaches for solving intricate problems. When the substantive area is health and health care, the overlap is further cemented. Researchers in these disciplines are fluent in statistics, data management and analysis, and health and medicine, to name but a few competencies. Yet there are important and perhaps mutually exclusive attributes of these fields that warrant tighter integration. Collaboration and cross-training offer the opportunity to share and learn of the constructs, frameworks, theories, and methods of these fields with the goal of offering fresh and innovate perspectives for tackling challenging problems in health and health care.
Computer viruses are designed to be pests, proliferating in uncontrolled ways and causing severe damage to electronic data. These malignant programs, which amplify between files and computers, are strikingly similar in virulence, modes of spread, and evolutionary pathways over time to the microbes that cause infectious diseases. Both biological viruses and these virtual viruses are transmitted from host to host. Computer viruses are a human invention; however, their development follows a well-recognized biological route. Relatively harmless ancestors gradually or step-by-step evolve into “pathogens;” the host develops adaptive defense mechanisms, which in turn select for new virus “variants;” eventually, equilibrium is reached between infection and host defenses. Comparing “virtual microbes” with their biological counterparts can help us control both.
Networks and the epidemiology of directly transmitted infectious diseases are fundamentally linked. The foundations of epidemiology and early epidemiological models were based on population-wide random-mixing, but in practice, each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a mixing network. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behavior of infections. Therefore, characteristics of mixing networks—and how these deviate from the random-mixing norm—have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures.
The study of epidemiological processes as a form of socially determined movement requires a renewed understanding of the social order, and thus, an updated understanding of the social relations that move society. The new digital technological revolution implies radical effects on health which we call cybernetic determination and subsumption. This novel process raises new questions on public health and prevention; but also requires a new reading of reality, a rethinking of human life and health, of its social determination, which implies the need for new categories and analysis and renewed challenges for critical epidemiology.
Ever-increasing threats to cybersecurity present serious challenges for population health. However, the direct intersections between cybersecurity and public health can benefit from examination through the lenses of public health system operational frameworks. In this paper, we thus provide an overview of how cybersecurity issues may systemically impact public health emergency preparedness and imperil the delivery of essential public health services. We discuss future broad-based policy and research considerations accordingly for this critical public health security dimension.
Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. One way to improve early detection is to monitor health-seeking behavior in the form of queries to online search engines, which are submitted by millions of users around the world each day. This study presents a method of analyzing large numbers of Google search queries to track influenza-like illness in a population, an approach would enable search queries to detect influenza epidemics in areas with a large population of web search users.
Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.
On February 15th, WHO Director-General Tedros Adhanom Ghebreyesus declared the online response to COVID-19 a widespread infodemic: “an overabundance of information – some accurate and some not – that makes it hard for people to find trustworthy sources and reliable guidance when they need it.”4 Following this, the MIT Technology Review observed that the virus has the makings of “the first true social-media ‘infodemic’ [as] social media has zipped information and misinformation around the world at unprecedented speeds, fueling panic, racism ... and hope.”5 This section will present readings that engage with the nature of this term infodemic, which whilst capturing the scale of the problem, risks obscuring the nuances of various information disorders that are simultaneously occurring. In other words, not all forms of ‘disinformation’ are equal.
To illustrate the staggering complexities of this infodemic, the readings will span the more technical spectrum of measuring and visualizing online ‘virality’: graph-based approaches to mapping disinfo-spread; the structural virality and branching dynamics of information spreading on online social networks; a neural model of valuation and information virality; design, dissemination, and disinformation in viral maps; and the role of local topological information in viral information spreading. Notwithstanding the real-world consequences of these processes, the readings will also address the quantifiable effects that infodemics have on the spatial spread of the pandemic. Attention to the international legislation that underpins the public’s right to free, accurate health information and a free press will be emphasized. And finally, as we witness an unprecedented coalition of tech companies to combat viral disinformation, texts that question the role of big tech in their efforts to censor misinformation as it endangers public health are equally listed.
This report represents a preliminary analysis of data analyzed by Graphika on the global online conversation surrounding the coronavirus pandemic via four maps, three of which form the beginning of a “time series” of maps. The series is a set of large-scale and granular network maps seeded on the same mainstream signals associated with general conversation around the coronavirus, with data collected at monthly intervals between December 19, 2019 and March 17, 2020. These maps form the basis of a chronological series that allows rigorous analysis of structural changes to this online conversation.
This fact sheet uses a sample of fact-checks to identify some of the main types, sources, and claims of COVID-19 misinformation seen so far. Building on other analyses, the paper combines a systematic content analysis of fact-checked claims about the virus and the pandemic with social media data indicating the scale and scope of engagement. Ultimately, the findings describe the makeup and circulation of misinformation about COVID-19 based on content analysis finalized by 31 March.
This paper sets out ARTICLE 19’s position on freedom of expression issues impacted by the COVID-19 crisis. It describes international standards on the right to freedom of expression and information especially in relation to the right to health. It highlights the key role played by these rights in the development and implementation of effective public health strategies. The briefing details several challenges to freedom of expression and information during the current COVID-19 crisis and makes recommendations to state and other actors, notably the media and social media platforms.
Twitter has been used to track trends and disseminate health information during viral epidemics. This study aimed to quantify and understand early changes in Twitter activity, content, and sentiment about the COVID-19 epidemic. Researchers found that tweets with negative sentiment and emotion parallel the incidence of cases for the COVID-19 outbreak. Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and target public health messages based on user interest and emotion.
Relying on diffusion of innovation theory, this study examines the impacts of perceived message features and network characteristics on size and structural virality of information diffusion on Twitter. Findings indicated that, with respect to message features, perceived efficacy after reading a tweet positively predicted diffusion size of the tweet, whereas perceived susceptibility to a health condition after reading a tweet positively predicted structural virality of the tweet. Theoretical and practical implications were discussed on disseminating health information via broadcasting and viral diffusion on social media.
Information sharing is an integral part of human interaction that serves to build social relationships and affects attitudes and behaviors in individuals and large groups. The paper presents a unifying neurocognitive framework of mechanisms underlying information sharing at scale (virality). It argues that expectations regarding self-related and social consequences of sharing (e.g., in the form of potential for self-enhancement or social approval) are integrated into a domain-general value signal that encodes the value of sharing a piece of information. This parsimonious framework may help advance theory, improve predictive models, and inform new approaches to effective intervention. More broadly, these data shed light on the core functions of sharing—to express ourselves in positive ways and to strengthen our social bonds.
Social factors, rather than individual psychology, are essential to understanding the spread and persistence of false beliefs. It might seem that there’s an obvious reason that true beliefs matter: false beliefs will hurt you. But if that’s right, then why is it (apparently) irrelevant to many people whether they believe true things or not? The Misinformation Age, written for a political era riven by “fake news,” “alternative facts,” and disputes over the validity of everything from climate change to the size of inauguration crowds, shows convincingly that what you believe depends on who you know. If social forces explain the persistence of false belief, we must understand how those forces work in order to fight misinformation effectively.
In this paper, we aim to study infection dynamics related to the spatial spread of an epidemic in interconnected regions in the presence of random perturbations caused by the three above-mentioned reasons. Therefore, we devise a stochastic multi-region epidemic model in which contacts between susceptible and infected populations, vaccination-based and movement restriction optimal control approaches are all assumed to be unpredictable, and then, we discuss the effectiveness of such policies. In order to reach our goal, we employ a stochastic maximum principle version for noised systems, state and prove the sufficient and necessary conditions of optimality, and finally provide the numerical results obtained using a stochastic progressive-regressive schemes method.
The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. The research demonstrates the dependency of virality on critical social network factors and determine the pattern virality that an information item takes over time.
The intricate structure of many large-scale networked systems has attracted the attention of the scientific community, leading to many results attempting to explain the relationship between a network's structural features and the performance of spreading processes taking place in the network. In this study, the researchers propose an alternative approach to overcome these limitations using algebraic graph theory and convex optimization to study how structural properties constrain the behavior of spreading processes in the network.
Contagion in online social networks (OSN) occurs when users are exposed to information disseminated by other users. This study aims to investigate the differences between local and global contagion and the different contagion patterns of viral vs. non-viral information. Based on their analysis, researchers successfully predict whether a user will be infected by either a local or a global contagion and propose a novel method for early detection of the viral potential of an information nugget and investigate the spreading of viral and non-viral information. Differentiating between local versus global contagion, as well as between viral versus non-viral information, provides a novel perspective and better understanding of information diffusion in OSNs.
Despite its importance for rumors or innovations propagation, peer-to-peer collaboration, social networking, or marketing, the dynamics of information spreading is not well understood. Since the diffusion depends on the heterogeneous patterns of human behavior and is driven by the participants' decisions, its propagation dynamics shows surprising properties not explained by traditional epidemic or contagion models. The study finds that information spreading displays a non-Markovian branching dynamics that can be modeled by a two-step Bellman-Harris branching process that generalizes the static models known in the literature and incorporates the high variability of human behavior. It explains accurately all the features of information propagation under the "tipping point" and can be used for prediction and management of viral information spreading processes.
Finally, as a nod to the digital humanities (and my own beginnings in media aesthetics) the final section lists texts that inform the still nascent trajectory of post-digital theory as it relates to the virality of media on the occasion of a global pandemic. The term “post-digital” signals a critical inquiry into the digital world that examines its constitution, its theoretical orientation, and its implications. Central to this study is the notion of “virality,” which is predicated on technology’s almost auto-poietic ability to self-produce ad infinitum. This distinction has led some scholars to consider technology as indeed a ‘living’ organism and to develop a ‘bioinformational’ paradigm that is particularly acute in its relation to COVID-19. Through this framework, contagious diseases are understood as simultaneously biological, sociocultural, and digital phenomena — a trifold complexity that when viewed in relation to the post-digital reveals the pervasively viral nature of neoliberalism and the crucial need for open knowledge exchange to ensure rapid, equitable sharing of scientific information.
Viral modernity is a concept based upon the nature of viruses, the role they play in evolution and culture, and the basic application to understanding the role of information and forms of bioinformation in the social world. The concept draws a close association between viral biology on the one hand, and information science on the other – it is an illustration and prime example of bioinformationalism that brings together two of the most powerful forces that now drive cultural evolution. The concept of viral modernity applies to viral technologies, codes and ecosystems in information, publishing, education and emerging knowledge (journal) systems. This paper traces the relationship between epidemics, quarantine, and public health management and outlines elements of viral-digital philosophy (VDP) based on the fusion of living and technological systems. We discuss COVID-19 as a ‘bioinformationalist’ response that represents historically unprecedented level of sharing information. Finally, we look at the US response to COVID-19 through the lens of infodemics and post-truth.
In this thought-provoking book, Tony D. Sampson presents a contagion theory fit for the age of networks. Unlike memes and microbial contagions, “virality” does not restrict itself to biological analogies and medical metaphors. Instead, it points toward a theory of contagious assemblages, events, and affects. Sampson interprets contagion theory through the social relationalities first established in Gabriel Tarde's microsociology and subsequently recognized in Gilles Deleuze's ontological worldview.
The infodemic around COVID-19 will be analyzed for long after the pandemic. At this point, we need to develop immediate measures to protect ourselves individually and collectively—weed out reliable information, self-isolate, reduce panic, develop educated guesses and emergency plans. However, these urgent measures cannot arrive from thin air, and it is just as important to step back and take a birds-eye, longue durée view at the pandemic. While doctors, nurses, politicians, food suppliers, and many other brave people self-sacrifice to support our daily survival, this editorial argues that academics have a unique opportunity, and a moral duty, to immediately start conducting in-depth studies of current events.
In this essay, Michael Peters very thoroughly examines the merits of “openness” as a value in academic life as well as commercial enterprise. He posits that computer viruses bring about a viral modernity which challenges and disrupts the openness of a free distribution model as well as distributed knowledge, media, and learning systems. Just like in biological systems, the virus flourishes the alterability of information allows the virus to modify and change information, providing conditions for self-replicability. Peters cites Fred Cohen to advocate the benevolent virus and friendly contagion as a foundation of the viral ecosystem instead of the corporate response to securitize and privatize all open systems through sophisticated encryption. Peter demonstrates how this underlies the continuing struggle between free open culture and proprietary closed culture that pitches the merits of a more equitable open education system against the less liberatory closed control of information favored traditionally by many companies, the nation state, and major universities.6
This paper argues that viral technologies can hold info-space hostage to the uncertain undercurrents of information itself. As such, despite mercantile efforts to capture the spirit of openness, the info-space finds itself frequently in a state far-from-equilibrium. It is open to often-unmanageable viral fluctuations, which produce levels of spontaneity, uncertainty and emergent order. So while corporations look to capture the perpetual, flexible and friction-free income streams from centralized information flows, viral code acts as an anarchic, acentered Deleuzian rhizome. It thrives on the openness of info-space, producing a paradoxical counterpoint to a corporatized information society and its attempt to steer the info-machine.
Evolution has transformed life through key innovations in information storage and replication, including RNA, DNA, multicellularity, and culture and language. We argue that the carbon-based biosphere has generated a cognitive system (humans) capable of creating technology that will result in a comparable evolutionary transition. Digital information has reached a similar magnitude to information in the biosphere. It increases exponentially, exhibits high-fidelity replication, evolves through differential fitness, is expressed through AI, and has facility for virtually limitless recombination. Like previous evolutionary transitions, the potential symbiosis between biological and digital information will reach a critical point where these codes could compete via natural selection. Alternatively, this fusion could create a higher-level superorganism employing a low-conflict division of labor in performing informational tasks.
This article resituates the Panopticon in Foucault’s work, showing how it emerged from research on social medicine in the early to mid 1970s, and relating it to discussions of the plague and the police. What is of interest here is how Foucault’s concerns with surveillance interrelate with concerns about society as a whole — not in the total institution of the prison, but in the realm of public health. This is pursued through detailed readings of Foucault’s analyses of urban medicine and the hospital. The article closes by making some general remarks about situating Foucault’s books in the context of his lecture courses, and about how the analysis of medicine may be a more profitable model for surveillance than the Panopticon.
Computer viruses have been written about as a security problem, as a social problem, and as a possible means of performing useful tasks in a distributed computing environment. Scientists have recently begun to ask if computer viruses are not a form of artificial life—a self-replicating organism. This paper begins with a description of how computer viruses operate and their history, and of the various ways computer viruses are structured. It then examines how viruses meet properties associated with life as defined by some researchers in the area of artificial life and self-organizing systems. The paper concludes with some comments directed toward the definition of artificially “alive” systems and experimentation.
In this conversation, Michael A. Peters discusses his philosophy of education in and for the age of digital media. The first part of the conversation classifies Michael Peters’ work in three interlocked themes: philosophy, political knowledge economy, and academic publishing. It explores the power of dialogue for philosophical inquiry, positions dialogue in relation to human learning, and analyses the philosophical thesis of postdisciplinarity. It assesses the role of “big data” and “learning analytics” in (educational) research, and links various approaches to inquiry with creativity. The second part of the conversation introduces the notion of “philosophy as pedagogy,” and introduces Michael Peters’ philosophy of technology. It inquires the role of educational philosophy in the contemporary network society, and explores links between postmodernism / poststructuralism and (neo)Marxism. The third part of the conversation explores the relationships between universalism and the Internet, locates digital postcolonialism, and looks into legacy of the Frankfurt School for learning in the age of digital media. Finally, it discusses Michael Peters’ lifelong fascination with Ludwig Wittgenstein, and outlines the main trajectories of Wittgenstein’s work into present and future of educational philosophy.