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Online Hate Network Spreads Malicious COVID-19 Content Outside the Control of Individual Social Media Platforms

Authors:
Nicolas Velasquez at University of Atacama
Nicolas Velasquez
Rhys Leahy at George Washington University
Rhys Leahy
Nicholas Johnson Restrepo
Nicholas Johnson Restrepo
Nicholas Johnson Restrepo
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Yonatan Lupu at George Washington University
Yonatan Lupu
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Abstract

We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.
Available via license: CC BY 4.0
Content may be subject to copyright.
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... However, a discrepancy can be observed between the literature supported by scientific findings and the knowledge fed by conspiracy theories or hate speech (Velásquez et al., 2020). As Velásquez et al. (2020) pointed out, "hate multiverse spreads malicious COVID-19 content" (Velásquez et al., 2020, p. 1), while Ferrara (2020) drew attention to the role of robot-generated content in the spread of fake news and conspiracy theories (Ferrara, 2020). ...
... However, a discrepancy can be observed between the literature supported by scientific findings and the knowledge fed by conspiracy theories or hate speech (Velásquez et al., 2020). As Velásquez et al. (2020) pointed out, "hate multiverse spreads malicious COVID-19 content" (Velásquez et al., 2020, p. 1), while Ferrara (2020) drew attention to the role of robot-generated content in the spread of fake news and conspiracy theories (Ferrara, 2020). Based on the data from Pew Research Center (2020), more than half of social media users came across information about the pandemiceven during the initial periodwhich they judged to be entirely fictional (Jurkowitz & Mitchell, 2020). ...
The Relationship between Self-Representation on Social Media and Affective or Anxiety Disorders in the Perspective of the COVID-19 Pandemic
Book
Full-text available
  • May 2023
The purpose of the current dissertation is to shed light on the relationship between self-representation and affective or anxiety disorders from the perspective of the COVID-19 pandemic by presenting the author’s research results after a thorough literature review. After this short introduction – which includes the justification of the choice of topic, its social relevance, the methodology of the research, and the personal motivation of the author – an extensive literature review (Chapter 2) discusses the relationship between social media, mental health, and the COVID-19 pandemic. Since so far, very few research results have been published that examined this triple connection, the sources available on the double connections are introduced as well: first, on the connection between social media and mental health (Chapter 2.1), then on the connection between mental health and the COVID-19 pandemic (Chapter 2.2), and finally on the about social media and the pandemic (Chapter 2.3), before turning to examine the results of the triple connection so far (Chapter 2.4). The literature review is followed by the author’s research results (Chapter 3) in three separate yet connected parts, which can be interpreted separately but give a more comprehensive picture together. The first research is about the possible psychosocial impact of modifying face and body photographs in social media (Chapter 3.1); this mixed-method pilot study helps explore the correlations of self-representation with questionnaire data collection and interviews with experts and users.The second research is a real-time cross-sectional analysis of self-representation on social media and depression risk during lockdowns and restrictions of the first five COVID-19 pandemic waves (Chapter 3.2). The unique feature is that the data was not collected retrospectively but took place at the peaks of the waves of the pandemic. The third research, which took place in parallel with the second, is a longitudinal analysis that focuses on the self-representation of users diagnosed with an affective disorder or anxiety disorder (Chapter 3.3). Here, self-representative photos and videos were analyzed on Facebook or Instagram over three years. The analysis covers three groups: the members of the first had an official diagnosis of one of the specified common anxiety or affective disorders; the members of the second group did not have such a diagnosis, but based on their symptoms, they suspected that they might have such mental illnesses; and the members of the third group had neither an official nor a self-suspected diagnosis. In addition to the content analysis, questionnaire data were collected twice to examine the relationship between self-representation on social media and affective or anxiety disorders in the perspective of the COVID-19 pandemic.
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How I learned to hate you. Parasocial interactions in echo chambers and their spillover effects
Article
Full-text available
  • Mar 2023
This article analyzes the most influential posts on Facebook related to COVID-19, for the first two years of the pandemic, to explain how parasocial opinion leaders created echo chambers, in the Romanian public sphere, and to discuss the cumulative spillover effects these echo chambers had on society at large. A database of the 233,242 most influential posts in Romanian about COVID-19, from the first two years of the pandemic, is investigated using a mixed methods approach, to 1) verify statistically if issue-related echo chambers existed and 2) to describe, qualitatively, how they functioned. A special focus is devoted to trolling in the form of reactions to posts, such as haha reactions for messages about COVID-related deaths. Using the literature on parasocial interaction, inoculation theory, online disinhibition effect and echo chambers, the article shows how echo chambers supported trolling behavior, for radicalized Facebook users, how they polluted the public discussion and how they made dialog impossible for social groups that ended up identifying each other as the enemy. Based on these research results, the author proposes two policy recommendations for social platforms.
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The Social Lives of Infectious Diseases: Why Culture Matters to COVID-19
Article
Full-text available
  • Sep 2021
Over the course of the year 2020, the global scientific community dedicated considerable effort to understanding COVID-19. In this review, we discuss some of the findings accumulated between the onset of the pandemic and the end of 2020, and argue that although COVID-19 is clearly a biological disease tied to a specific virus, the culture–mind relation at the heart of cultural psychology is nonetheless essential to understanding the pandemic. Striking differences have been observed in terms of relative mortality, transmission rates, behavioral responses, official policies, compliance with authorities, and even the extent to which beliefs about COVID-19 have been politicized across different societies and groups. Moreover, many minority groups have very different experiences of the pandemic relative to dominant groups, notably through existing health inequities as well as discrimination and marginalization, which we believe calls for a better integration of political and socioeconomic factors into cultural psychology and into the narrative of health and illness in psychological science more broadly. Finally, individual differences in, for example, intolerance of uncertainty, optimism, conspiratorial thinking, or collectivist orientation are influenced by cultural context, with implications for behaviors that are relevant to the spread and impact of COVID-19, such as mask-wearing and social distancing. The interplay between cultural context and the experience and expression of mental disorders continues to be documented by cultural-clinical psychology; the current work extends this thinking to infectious disease, with special attention to diseases spread by social contact and fought at least in part through social interventions. We will discuss cultural influences on the transmission, course, and outcome of COVID-19 at three levels: (1) cross-society differences; (2) within-society communities and intergroup relations; and (3) individual differences shaped by cultural context. We conclude by considering potential theoretical implications of this perspective on infectious disease for cultural psychology and related disciplines, as well as practical implications of this perspective on science communication and public health interventions.
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Stewardship of global collective behavior
Article
Full-text available
  • Jul 2021
Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a “crisis discipline” just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.
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Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention
Article
Full-text available
  • Jun 2020
Across two studies with more than 1,700 U.S. adults recruited online, we present evidence that people share false claims about COVID-19 partly because they simply fail to think sufficiently about whether or not the content is accurate when deciding what to share. In Study 1, participants were far worse at discerning between true and false content when deciding what they would share on social media relative to when they were asked directly about accuracy. Furthermore, greater cognitive reflection and science knowledge were associated with stronger discernment. In Study 2, we found that a simple accuracy reminder at the beginning of the study (i.e., judging the accuracy of a non-COVID-19-related headline) nearly tripled the level of truth discernment in participants’ subsequent sharing intentions. Our results, which mirror those found previously for political fake news, suggest that nudging people to think about accuracy is a simple way to improve choices about what to share on social media.
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Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning
Article
Full-text available
  • May 2020
A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations ("anti-vax"). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination (“pro-vax”) community. However, the anti-vax community exhibits a broader range of “flavors” of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation.
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Using social and behavioural science to support COVID-19 pandemic response
Article
Full-text available
  • Apr 2020
  • Nat. Hum. Behav.
The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.
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Hidden resilience and adaptive dynamics of the global online hate ecology
Article
Full-text available
  • Sep 2019
  • NATURE
Online hate and extremist narratives have been linked to abhorrent real-world events, including a current surge in hate crimes1–6 and an alarming increase in youth suicides that result from social media vitriol⁷; inciting mass shootings such as the 2019 attack in Christchurch, stabbings and bombings8–11; recruitment of extremists12–16, including entrapment and sex-trafficking of girls as fighter brides¹⁷; threats against public figures, including the 2019 verbal attack against an anti-Brexit politician, and hybrid (racist–anti-women–anti-immigrant) hate threats against a US member of the British royal family¹⁸; and renewed anti-western hate in the 2019 post-ISIS landscape associated with support for Osama Bin Laden’s son and Al Qaeda. Social media platforms seem to be losing the battle against online hate19,20 and urgently need new insights. Here we show that the key to understanding the resilience of online hate lies in its global network-of-network dynamics. Interconnected hate clusters form global ‘hate highways’ that—assisted by collective online adaptations—cross social media platforms, sometimes using ‘back doors’ even after being banned, as well as jumping between countries, continents and languages. Our mathematical model predicts that policing within a single platform (such as Facebook) can make matters worse, and will eventually generate global ‘dark pools’ in which online hate will flourish. We observe the current hate network rapidly rewiring and self-repairing at the micro level when attacked, in a way that mimics the formation of covalent bonds in chemistry. This understanding enables us to propose a policy matrix that can help to defeat online hate, classified by the preferred (or legally allowed) granularity of the intervention and top-down versus bottom-up nature. We provide quantitative assessments for the effects of each intervention. This policy matrix also offers a tool for tackling a broader class of illicit online behaviours21,22 such as financial fraud.
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Exposure to opposing views on social media can increase political polarization
Article
Full-text available
  • Aug 2018
Significance Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. We conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Republican participants expressed substantially more conservative views after following a liberal Twitter bot, whereas Democrats’ attitudes became slightly more liberal after following a conservative Twitter bot—although this effect was not statistically significant. Despite several limitations, this study has important implications for the emerging field of computational social science and ongoing efforts to reduce political polarization online.
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Generalized Gelation Theory Describes Onset of Online Extremist Support
Article
Full-text available
  • Jul 2018
We introduce a generalized form of gelation theory that incorporates individual heterogeneity and show that it can explain the asynchronous, sudden appearance and growth of online extremist groups supporting ISIS (so-called Islamic State) that emerged globally post-2014. The theory predicts how heterogeneity impacts their onset times and growth profiles and suggests that online extremist groups present a broad distribution of heterogeneity-dependent aggregation mechanisms centered around homophily. The good agreement between the theory and empirical data suggests that existing strategies aiming to defeat online extremism under the assumption that it is driven by a few “bad apples” are misguided. More generally, this generalized theory should apply to a range of real-world systems featuring aggregation among heterogeneous objects.
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Liberals and conservatives are similarly motivated to avoid exposure to one another's opinions
Article
Full-text available
  • Sep 2017
Ideologically committed people are similarly motivated to avoid ideologically crosscutting information. Although some previous research has found that political conservatives may be more prone to selective exposure than liberals are, we find similar selective exposure motives on the political left and right across a variety of issues. The majority of people on both sides of the same-sex marriage debate willingly gave up a chance to win money to avoid hearing from the other side (Study 1). When thinking back to the 2012 U.S. Presidential election (Study 2), ahead to upcoming elections in the U.S. and Canada (Study 3), and about a range of other Culture War issues (Study 4), liberals and conservatives reported similar aversion toward learning about the views of their ideological opponents. Their lack of interest was not due to already being informed about the other side or attributable election fatigue. Rather, people on both sides indicated that they anticipated that hearing from the other side would induce cognitive dissonance (e.g., require effort, cause frustration) and undermine a sense of shared reality with the person expressing disparate views (e.g., damage the relationship; Study 5). A high-powered meta-analysis of our data sets (N = 2417) did not detect a difference in the intensity of liberals' (d = 0.63) and conservatives' (d = 0.58) desires to remain in their respective ideological bubbles.
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Addressing Health-Related Misinformation on Social Media
Article
  • Nov 2018
The ubiquitous social media landscape has created an information ecosystem populated by a cacophony of opinion, true and false information, and an unprecedented quantity of data on many topics. Policy makers and the social media industry grapple with the challenge of curbing fake news, disinformation, and hate speech; and the field of medicine is similarly confronted with the spread of false, inaccurate, or incomplete health information.
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Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election
Article
  • Jun 2017
Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion. Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users. In this study, we describe one such operation occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts occurred between April 27 and May 7, 2017 (Election Day). We then set to study the MacronLeaks disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups taken independently, as well as their interplay. We provide a characterization of both the bots and the users who engaged with them and oppose it to those users who didn't. Prior interests of disinformation adopters pinpoint to the reasons of the scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with a preexisting interest in alt-right topics and alternative news media, rather than French users with diverse political views. Concluding, anomalous account usage patterns suggest the possible existence of a black-market for reusable political disinformation bots.
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