Hidden resilience and adaptive dynamics of the global online hate ecology

Analysis of Guangzhou city image perception based on weibo text data (2019-2023)

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to "Guangzhou City" between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

Online harassment of Japanese celebrities and influencers

Famous people, such as celebrities and influencers, are harassed online on a daily basis. Online harassment mentally disturbs them and negatively affects society. However, limited studies have been conducted on the online harassment victimization of famous people, and its effects remain unclear. We surveyed Japanese famous people (N = 213), who were influential people who appeared on television and other traditional media and on social media, regarding online harassment victimization, emotional injury, and action against offenders and revealed that various forms of online harassment are prevalent. Some victims used the anti-harassment functions provided by weblogs and social media systems (e.g., blocking/muting/reporting offender accounts and closing comment forms), talked about their victimization to close people, and contacted relevant authorities to take legal action (talent agencies, legal consultants, and police). By contrast, some victims felt compelled to accept harassment and did not initiate action for offenses. We propose several approaches to support victims, inhibit online harassment, and educate people. Our findings help that platforms establish support systems against online harassment.

Nonlinear spreading behavior across multi-platform social media universe

Understanding how harmful content (mis/disinformation, hate, etc.) manages to spread among online communities within and across social media platforms represents an urgent societal challenge. We develop a non-linear dynamical model for such viral spreading, which accounts for the fact that online communities dynamically interconnect across multiple social media platforms. Our mean-field theory (Effective Medium Theory) compares well to detailed numerical simulations and provides a specific analytic condition for the onset of outbreaks (i.e., system-wide spreading). Even if the infection rate is significantly lower than the recovery rate, it predicts system-wide spreading if online communities create links between them at high rates and the loss of such links (e.g., due to moderator pressure) is low. Policymakers should, therefore, account for these multi-community dynamics when shaping policies against system-wide spreading.

Persistent interaction patterns across social media platforms and over time

Growing concern surrounds the impact of social media platforms on public discourse1-4 and their influence on social dynamics5-9, especially in the context of toxicity10-12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years-from Usenet to contemporary social media-our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.

Neonatal Male Circumcision: Clearly Beneficial for Public Health or an Ethical Dilemma? A Systematic Review

Contrasting ethical and legal arguments have been made concerning neonatal male circumcision (NMC) that merit the first systematic review on this topic. We performed PRISMA-compliant keyword searches of PubMed, EMBASE, SCOPUS, LexisNexis, and other databases and identified 61 articles that met the inclusion criteria. In the bibliographies of these articles, we identified 58 more relevant articles and 28 internet items. We found high-quality evidence that NMC is a low-risk procedure that provides immediate and lifetime medical and health benefits and only rarely leads to later adverse effects on sexual function or pleasure. Given this evidence, we conclude that discouraging or denying NMC is unethical from the perspective of the United Nations Convention on the Rights of the Child, which emphasizes the right to health. Further, case law supports the legality of NMC. We found, conversely, that the ethical arguments against NMC rely on distortions of the medical evidence. Thus, NMC, by experienced operators using available safety precautions, appears to be both legal and ethical. Consistent with this conclusion, all of the evidence-based pediatric policies that we reviewed describe NMC as low-risk and beneficial to public health. We calculated that a reduction in NMC in the United States from 80% to 10% would substantially increase the cases of adverse medical conditions. The present findings thus support the evidence-based NMC policy statements and are inconsistent with the non-evidence-based policies that discourage NMC. On balance, the arguments and evidence reviewed here indicate that NMC is a medically beneficial and ethical public health intervention early in life because it reduces suffering, deaths, cases, and costs of treating adverse medical conditions throughout the lifetimes of circumcised individuals.

Comments by opponents on the British Medical Association's guidance on non-therapeutic male circumcision of children seem one-sided and may undermine public health

The British Medical Association (BMA) guidance on non-therapeutic circumcision (NTMC) of male children is limited to ethical, legal and religious issues. Here we evaluate criticisms of the BMA's guidance by Lempert et al. While their arguments promoting autonomy and consent might be superficially appealing, their claim of high procedural risks and negligible benefits seem one-sided and contrast with high quality evidence of low risk and lifelong benefits. Extensive literature reviews by the American Academy of Pediatrics and the United States Centers for Disease Control and Prevention in developing evidence-based policies, as well as risk-benefit analyses, have found that the medical benefits of infant NTMC greatly exceed the risks, and there is no reduction in sexual function and pleasure. The BMA's failure to consider the medical benefits of early childhood NTMC may partly explain why this prophylactic intervention is discouraged in the United Kingdom. The consequence is higher prevalence of preventable infections, adverse medical conditions, suffering and net costs to the UK's National Health Service for treatment of these. Many of the issues and contradictions in the BMA guidance identified by Lempert et al stem from the BMA's guidance not being sufficiently evidence-based. Indeed, that document called for a review by others of the medical issues surrounding NTMC. While societal factors apply, ultimately, NTMC can only be justified rationally on scientific, evidence-based grounds. Parents are entitled to an accurate presentation of the medical evidence so that they can make an informed decision. Their decision either for or against NTMC should then be respected.

Online conspiracy communities are more resilient to deplatforming

Online social media foster the creation of active communities around shared narratives. Such communities may turn into incubators for conspiracy theories-some spreading violent messages that could sharpen the debate and potentially harm society. To face these phenomena, most social media platforms implemented moderation policies, ranging from posting warning labels up to deplatforming, i.e. permanently banning users. Assessing the effectiveness of content moderation is crucial for balancing societal safety while preserving the right to free speech. In this article, we compare the shift in behavior of users affected by the ban of two large communities on Reddit, GreatAwakening and FatPeopleHate, which were dedicated to spreading the QAnon conspiracy and body-shaming individuals, respectively. Following the ban, both communities partially migrated to Voat, an unmoderated Reddit clone. We estimate how many users migrate, finding that users in the conspiracy community are much more likely to leave Reddit altogether and join Voat. Then, we quantify the behavioral shift within Reddit and across Reddit and Voat by matching common users. While in general the activity of users is lower on the new platform, GreatAwakening users who decided to completely leave Reddit maintain a similar level of activity on Voat. Toxicity strongly increases on Voat in both communities. Finally, conspiracy users migrating from Reddit tend to recreate their previous social network on Voat. Our findings suggest that banning conspiracy communities hosting violent content should be carefully designed, as these communities may be more resilient to deplatforming.

Rise of post-pandemic resilience across the distrust ecosystem

Why does online distrust (e.g., of medical expertise) continue to grow despite numerous mitigation efforts? We analyzed changing discourse within a Facebook ecosystem of approximately 100 million users who were focused pre-pandemic on vaccine (dis)trust. Post-pandemic, their discourse interconnected multiple non-vaccine topics and geographic scales within and across communities. This interconnection confers a unique, system-level (i.e., at the scale of the full network) resistance to mitigations targeting isolated topics or geographic scales-an approach many schemes take due to constrained funding. For example, focusing on local health issues but not national elections. Backed by numerical simulations, we propose counterintuitive solutions for more effective, scalable mitigation: utilize "glocal" messaging by blending (1) strategic topic combinations (e.g., messaging about specific diseases with climate change) and (2) geographic scales (e.g., combining local and national focuses).

The efficacy of Facebook's vaccine misinformation policies and architecture during the COVID-19 pandemic

Online misinformation promotes distrust in science, undermines public health, and may drive civil unrest. During the coronavirus disease 2019 pandemic, Facebook-the world's largest social media company-began to remove vaccine misinformation as a matter of policy. We evaluated the efficacy of these policies using a comparative interrupted time-series design. We found that Facebook removed some antivaccine content, but we did not observe decreases in overall engagement with antivaccine content. Provaccine content was also removed, and antivaccine content became more misinformative, more politically polarized, and more likely to be seen in users' newsfeeds. We explain these findings as a consequence of Facebook's system architecture, which provides substantial flexibility to motivated users who wish to disseminate misinformation through multiple channels. Facebook's architecture may therefore afford antivaccine content producers several means to circumvent the intent of misinformation removal policies.

Shockwavelike Behavior across Social Media

Online communities featuring "anti-X" hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwavelike solutions explain how, why, and when such activity rises from "out-of-nowhere," and show how it can be delayed, reshaped, and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.

Hate speech in the Internet context: Unpacking the roles of Internet penetration, online legal regulation, and online opinion polarization from a transnational perspective

Hate speech has plagued human societies for decades and severely influenced the whole world. By analyzing a balanced panel dataset covering 167 countries and 19 years derived from the V-Dem project, the relationships between critical Internet context indicators and online hate speech were disentangled, casting light on how to tackle the hate speech problem on a global scale. We found that Internet penetration was positively associated with online hate speech in general, confirming Internet's technological affordances have the potential to facilitate hate speech expression. Online legal regulation was negatively related to online hate speech but had no significant moderating effect between Internet penetration and online hate speech, reflecting an underlying tension among legal approaches’ comprehensive effects. Online opinion polarization was positively related to online hate speech and significantly moderated the relationship between Internet penetration and online hate speech, which indicated that a polarized opinion atmosphere might exacerbate hate speech and intensify intergroup conflicts. Our findings’ theoretical contributions and policy implications are further discussed.

Offline events and online hate

Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.

From Facebook to YouTube: The Potential Exposure to COVID-19 Anti-Vaccine Videos on Social Media

This article examines the role of Facebook and YouTube in potentially exposing people to COVID-19 vaccine-related misinformation. Specifically, to study the potential level of exposure, the article models a uni-directional information-sharing pathway beginning when a Facebook user encounters a vaccine-related post with a YouTube video, follows this video to YouTube, and then sees a list of related videos automatically recommended by YouTube. The results demonstrate that despite the efforts by Facebook and YouTube, COVID-19 vaccine-related misinformation in the form of anti-vaccine videos propagates on both platforms. Because of these apparent gaps in platform-led initiatives to combat misinformation, public health agencies must be proactive in creating vaccine promotion campaigns that are highly visible on social media to overtake anti-vaccine videos' prominence in the network. By examining related videos that a user potentially encounters, the article also contributes practical insights to identify influential YouTube channels for public health agencies to collaborate with on their public service announcements about the importance of vaccination programs and vaccine safety.

Addressing religious hate online: from taxonomy creation to automated detection

Abusive language in online social media is a pervasive and harmful phenomenon which calls for automatic computational approaches to be successfully contained. Previous studies have introduced corpora and natural language processing approaches for specific kinds of online abuse, mainly focusing on misogyny and racism. A current underexplored area in this context is religious hate, for which efforts in data and methods to date have been rather scattered. This is exacerbated by different annotation schemes that available datasets use, which inevitably lead to poor repurposing of data in wider contexts. Furthermore, religious hate is very much dependent on country-specific factors, including the presence and visibility of religious minorities, societal issues, historical background, and current political decisions. Motivated by the lack of annotated data specifically tailoring religion and the poor interoperability of current datasets, in this article we propose a fine-grained labeling scheme for religious hate speech detection. Such scheme lies on a wider and highly-interoperable taxonomy of abusive language, and covers the three main monotheistic religions: Judaism, Christianity and Islam. Moreover, we introduce a Twitter dataset in two languages-English and Italian-that has been annotated following the proposed annotation scheme. We experiment with several classification algorithms on the annotated dataset, from traditional machine learning classifiers to recent transformer-based language models, assessing the difficulty of two tasks: abusive language detection and religious hate speech detection. Finally, we investigate the cross-lingual transferability of multilingual models on the tasks, shedding light on the viability of repurposing our dataset for religious hate speech detection on low-resource languages. We release the annotated data and publicly distribute the code for our classification experiments at https://github.com/dhfbk/religious-hate-speech.

A cultural evolutionary theory that explains both gradual and punctuated change

Cumulative cultural evolution (CCE) occurs among humans who may be presented with many similar options from which to choose, as well as many social influences and diverse environments. It is unknown what general principles underlie the wide range of CCE dynamics and whether they can all be explained by the same unified paradigm. Here, we present a scalable evolutionary model of discrete choice with social learning, based on a few behavioural science assumptions. This paradigm connects the degree of transparency in social learning to the human tendency to imitate others. Computer simulations and quantitative analysis show the interaction of three primary factors-information transparency, popularity bias and population size-drives the pace of CCE. The model predicts a stable rate of evolutionary change for modest degrees of popularity bias. As popularity bias grows, the transition from gradual to punctuated change occurs, with maladaptive subpopulations arising on their own. When the popularity bias gets too severe, CCE stops. This provides a consistent framework for explaining the rich and complex adaptive dynamics taking place in the real world, such as modern digital media.

Losing the battle over best-science guidance early in a crisis: COVID-19 and beyond

Ensuring widespread public exposure to best-science guidance is crucial in any crisis, e.g., coronavirus disease 2019 (COVID-19), monkeypox, abortion misinformation, climate change, and beyond. We show how this battle got lost on Facebook very early during the COVID-19 pandemic and why the mainstream majority, including many parenting communities, had already moved closer to more extreme communities by the time vaccines arrived. Hidden heterogeneities in terms of who was talking and listening to whom explain why Facebook's own promotion of best-science guidance also appears to have missed key audience segments. A simple mathematical model reproduces the exposure dynamics at the system level. Our findings could be used to tailor guidance at scale while accounting for individual diversity and to help predict tipping point behavior and system-level responses to interventions in future crises.

Online and Offline Coordination in Australia’s Far-Right: A Study of True Blue Crew

Far-right extremism transpires in virtual and physical space. In this study, we examine how the Australian far-right extremist group ‘True Blue Crew’ attempted to coordinate their offline activities with their social media activism. To this end, we conducted a thematic content analysis of administrator posts and user comments present on the group’s Facebook page prior to and following an organised street rally in June 2017. This online analysis was partnered with ethnographic field work to gauge the perceptions of group members and supporters during the rally in Melbourne, Victoria. The results highlight the multi-dimensional and intimate manner in which online and offline contexts are coordinated to support far-right activism and mobilisation. This study offers an empirical account of how far-right attitudes, activism, and mobilisation transpired in Australia in the years prior to an Australian committing the Christchurch terror attack. It reveals a growing frustration within the broader far-right movement, leading to later strategic adaptation that can be interpreted as an early warning sign of an environment increasingly conducive to violence. This provides a more nuanced understanding of the context from which far-right terrorism emerges, and speaks to the importance of maintaining a level of analysis that transverses the social and the individual, as well as the online and the offline spaces. Implications for security and government agencies responses are discussed.

Enhanced cooperation in multiplayer snowdrift games with random and dynamic groupings

An analytically tractable generalization of the N-person snowdrift (NSG) game that illustrates how cooperation can be enhanced is proposed and studied. The number of players competing within a NSG varies from one time step to another. Exact equations governing the frequency of cooperation f_{c}(r) as a function of the cost-to-benefit ratio r within an imitation strategy updating scheme are presented. For group sizes g uniformly distributed within the range g∈[1,g_{m}], an analytic formula for the critical value r_{c}(g_{m}), below which the system evolves into a totally cooperative (AllC) state, is derived. In contrast, a fixed group size NSG does not support an AllC state. The result r_{c}(g_{m}) requires the presence of sole-player groups and involves the inverse of the harmonic numbers and, more generally, the inverse first moment of the group size distribution. For r>r_{c}(g_{m}), the equation that determines the dynamical mixed states f_{c}(r) is given, with exact solutions existing for g_{m}≤5. The exact treatment allows the study of the phase boundary between the AllC state and the mixed states. The analytic results are checked against simulation results and exact agreements are demonstrated. The analytic form of the critical r_{c}(g_{m}) illustrates the necessity of having groups of a sole player in the evolutionary process. This result is supported by simulations with group sizes excluding the sole groups for which no AllC state emerges. A physically transparent picture of the importance of the sole players in inducing an AllC state is further presented based on the last surviving pattern before the AllC state is attained. The exact expression r_{c}(g_{m}) turns out to remain valid for nonuniform group-size distributions. Our analytical tractable generalization, therefore, sheds light on how a competing environment with variable group sizes could enhance cooperation and induce an AllC state.

Monitoring event-driven dynamics on Twitter: a case study in Belarus

Analysts of social media differ in their emphasis on the effects of message content versus social network structure. The balance of these factors may change substantially across time. When a major event occurs, initial independent reactions may give way to more social diffusion of interpretations of the event among different communities, including those committed to disinformation. Here, we explore these dynamics through a case study analysis of the Russian-language Twitter content emerging from Belarus before and after its presidential election of August 9, 2020. From these Russian-language tweets, we extracted a set of topics that characterize the social media data and construct networks to represent the sharing of these topics before and after the election. The case study in Belarus reveals how misinformation can be re-invigorated in discourse through the novelty of a major event. More generally, it suggests how audience networks can shift from influentials dispensing information before an event to a de-centralized sharing of information after it.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43545-022-00330-x.

Characterizing polarization in online vaccine discourse-A large-scale study

Vaccine hesitancy is currently recognized by the WHO as a major threat to global health. Recently, especially during the COVID-19 pandemic, there has been a growing interest in the role of social media in the propagation of false information and fringe narratives regarding vaccination. Using a sample of approximately 60 billion tweets, we conduct a large-scale analysis of the vaccine discourse on Twitter. We use methods from deep learning and transfer learning to estimate the vaccine sentiments expressed in tweets, then categorize individual-level user attitude towards vaccines. Drawing on an interaction graph representing mutual interactions between users, we analyze the interplay between vaccine stances, interaction network, and the information sources shared by users in vaccine-related contexts. We find that strongly anti-vaccine users frequently share content from sources of a commercial nature; typically sources which sell alternative health products for profit. An interesting aspect of this finding is that concerns regarding commercial conflicts of interests are often cited as one of the major factors in vaccine hesitancy. Further, we show that the debate is highly polarized, in the sense that users with similar stances on vaccination interact preferentially with one another. Extending this insight, we provide evidence of an epistemic echo chamber effect, where users are exposed to highly dissimilar sources of vaccine information, depending the vaccination stance of their contacts. Our findings highlight the importance of understanding and addressing vaccine mis- and dis-information in the context in which they are disseminated in social networks.

The Moderation of Human Characteristics in the Control Mechanisms of Rumours in Social Media: The Case of Food Rumours in China

Social networks are widely used as a fast and ubiquitous information-sharing medium. The mass spread of food rumours has seriously invaded public's healthy life and impacted food production. It can be argued that the government, companies, and the media have the responsibility to send true anti-rumour messages to reduce panic, and the risks involved in different forms of communication to the public have not been properly assessed. The manuscript develops an empirical analysis model from 683 food anti-rumour cases and 7,967 data of the users with top comments to test the influence of the strength of rumour/anti-rumour on rumour control. Furthermore, dividing the users into three categories, Leaders, Chatters, and General Public, and study the influence of human characteristics on the relationship between the strength of rumour/anti-rumour and rumour control by considering the different human characteristics as moderator variables. The results showed that anti-rumours have a significant positive impact on the control of rumours; the ambiguity of rumours has a significant negative impact on the Positive Comment Index (PCI) in rumour control. Further, the Leaders increased the overall level of PCI, but negatively adjusted the relationship between evidence and PCI; the Chatters and the General Public reduced the overall level of PCI, and Chatters weakened the relationship between the specific type of anti-rumour form and PCI while the General Public enhanced the relationship between the specific type of anti-rumour form and PCI. In the long run, the role of Leaders needs to be further improved, and the importance of the General Public is growing in the food rumour control process.

Morally Homogeneous Networks and Radicalism

Online radicalization is among the most vexing challenges the world faces today. Here, we demonstrate that homogeneity in moral concerns results in increased levels of radical intentions. In Study 1, we find that in Gab—a right-wing extremist network—the degree of moral convergence within a cluster predicts the number of hate-speech messages members post. In Study 2, we replicate this observation in another extremist network, Incels. In Studies 3 to 5 ( N = 1,431), we demonstrate that experimentally leading people to believe that others in their hypothetical or real group share their moral views increases their radical intentions as well as willingness to fight and die for the group. Our findings highlight the role of moral convergence in radicalization, emphasizing the need for diversity of moral worldviews within social networks.

Dynamics of online hate and misinformation

Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model, trained and fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of "pure haters", meant as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents' community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of the number of comments and time. Our results show that, coherently with Godwin's law, online debates tend to degenerate towards increasingly toxic exchanges of views.

Machine learning dismantling and early-warning signals of disintegration in complex systems

From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features - e.g., heterogeneous connectivity, mesoscale organization, hierarchy - affect their robustness to external perturbations, such as targeted attacks to their units. Identifying the minimal set of units to attack to disintegrate a complex network, i.e. network dismantling, is a computationally challenging (NP-hard) problem which is usually attacked with heuristics. Here, we show that a machine trained to dismantle relatively small systems is able to identify higher-order topological patterns, allowing to disintegrate large-scale social, infrastructural and technological networks more efficiently than human-based heuristics. Remarkably, the machine assesses the probability that next attacks will disintegrate the system, providing a quantitative method to quantify systemic risk and detect early-warning signals of system's collapse. This demonstrates that machine-assisted analysis can be effectively used for policy and decision-making to better quantify the fragility of complex systems and their response to shocks.

Three clusters of content-audience associations in expression of racial prejudice while consuming online television news

It is well investigated that the expression of racial prejudice is often induced by news coverage on the internet, and the exposure to media contributes to the cultivation of long-term prejudice. However, there is a lack of information regarding the immediate effects of news delivered through television or television-like media on the expression of racial prejudice. This study provides a framework for understanding such effects by focusing on content-audience associations using the logs of an "online television" service, which provides television-like content and user experiences. With these logs, we found an association between the news-watching and comment-posting behaviors. Consequently, logs relevant to two distinct forms of racism, modern and old-fashioned racism, were extracted. Using mathematical modeling, which considers the different levels of program inducements to racist expression, personal inclinations of audiences to racism, and certainty of prediction of audience behaviors, we found three associative patterns between the news programs and audiences. The relevance of the topics covered to the basic beliefs of each form of racism was characterized into three clusters: expression as a reaction to news that is directly relevant to the basic beliefs of racism with weak inducements by non-bigots, minority abuse by distorting the meanings of news content indirectly relevant to the beliefs but with strong inducements by audiences with a strong bias, and racial toxic opinions independent of the news content by clear bigots. Our findings provide implications for inhibiting the expression of online prejudice based on the characteristics of these patterns.

Preventing Violent Extremism and Social Work: Recent US History and Prospects

Anti-immigrant, anti-Semitic, and anti-Muslim violent extremism is on the rise in Western nations while jihadist terrorism continues throughout the Middle East and Africa. Despite significant efforts by governmental, non-governmental, and civic society organizations to address violent extremism, little progress has been made to prevent it. White supremacist organizations are now organizing globally through a variety of next generation communication networks using techniques developed by ISIS and Al-Qaeda. Throughout, relatively few social work academics have engaged in preventing violent extremism (PVE) scholarship. Though the profession is referenced frequently in the PVE literature, it is dominated by those in psychology and political science. Few articles in major social work journals have discussed social work's role or advanced PVE research. What has been published has mainly been critical of social work in this arena for legitimate fears of securitization, lack of resources/training, and the potential to discriminate against particular groups. The profession has a long history of impactful work in violence prevention in a myriad of practice areas. Given this wealth of experience and focus on social justice, social work should be a leader in this field. Areas of potential engagement in practice and research are discussed.

Online hate network spreads malicious COVID-19 content outside the control of individual social media platforms

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.

Hidden order across online extremist movements can be disrupted by nudging collective chemistry

Disrupting the emergence and evolution of potentially violent online extremist movements is a crucial challenge. Extremism research has analyzed such movements in detail, focusing on individual- and movement-level characteristics. But are there system-level commonalities in the ways these movements emerge and grow? Here we compare the growth of the Boogaloos, a new and increasingly prominent U.S. extremist movement, to the growth of online support for ISIS, a militant, terrorist organization based in the Middle East that follows a radical version of Islam. We show that the early dynamics of these two online movements follow the same mathematical order despite their stark ideological, geographical, and cultural differences. The evolution of both movements, across scales, follows a single shockwave equation that accounts for heterogeneity in online interactions. These scientific properties suggest specific policies to address online extremism and radicalization. We show how actions by social media platforms could disrupt the onset and 'flatten the curve' of such online extremism by nudging its collective chemistry. Our results provide a system-level understanding of the emergence of extremist movements that yields fresh insight into their evolution and possible interventions to limit their growth.

COVID-19, a tale of two pandemics: novel coronavirus and fake news messaging

The emergence of COVID-19, caused by novel Coronavirus SARS-CoV-2, became a pandemic in just 10 weeks. Without effective medications or vaccines available, authorities turned toward mitigation measures such as use of face masks, school's closings, shelter-in-place, telework and social distancing. People found refuge on the internet and social media apps; however, there was a proliferation of instant messaging containing hoaxed, deliberate misleading information: fake news messaging (FNM). The aim of this study was to assess FNM through content analysis and to discriminate them in a proposed taxonomy structure. A sample of convenience of messages, memes, tweets or cartoons in several languages was selected from the most popular social media outlets, i.e. Facebook, WhatsApp, Twitter etc. More than 300 FNM were identified. Descriptive statistics were used for highlighting potential relationships between variables. Content analysis determined that FNM could be divided into Health- and non-health-related types. There are several sub-types considering, but not limited to, religious beliefs, politics, economy, nutrition, behaviors, prevention of the infection, the origin of the disease and conspiracy theories. The parallel FNM pandemic affected the response from an already debilitated public health system through the confusion created in the community and the erosion in the credibility of genuine media. Public health practitioners had to face people's unpredictable behaviors, panic, tensions with the communities and, in some cases, a hostile climate toward frontline workers. Public health practitioners must adjust ongoing and future health promotion and education interventions including plans to neutralize fake news messages.

Characterizing network dynamics of online hate communities around the COVID-19 pandemic

Hate speech has long posed a serious problem for the integrity of digital platforms. Although significant progress has been made in identifying hate speech in its various forms, prevailing computational approaches have tended to consider it in isolation from the community-based contexts in which it spreads. In this paper, we propose a dynamic network framework to characterize hate communities, focusing on Twitter conversations related to COVID-19 in the United States and the Philippines. While average hate scores remain fairly consistent over time, hate communities grow increasingly organized in March, then slowly disperse in the succeeding months. This pattern is robust to fluctuations in the number of network clusters and average cluster size. Infodemiological analysis demonstrates that in both countries, the spread of hate speech around COVID-19 features similar reproduction rates as other COVID-19 information on Twitter, with spikes in hate speech generation at time points with highest community-level organization of hate speech. Identity analysis further reveals that hate in the US initially targets political figures, then grows predominantly racially charged; in the Philippines, targets of hate consistently remain political over time. Finally, we demonstrate that higher levels of community hate are consistently associated with smaller, more isolated, and highly hierarchical network clusters across both contexts. This suggests potentially shared structural conditions for the effective spread of hate speech in online communities even when functionally targeting distinct identity groups. Our findings bear theoretical and methodological implications for the scientific study of hate speech and understanding the pandemic's broader societal impacts both online and offline.

Facebook Pages, the "Disneyland" Measles Outbreak, and Promotion of Vaccine Refusal as a Civil Right, 2009-2019

Objectives. To understand changes in how Facebook pages frame vaccine opposition.Methods. We categorized 204 Facebook pages expressing vaccine opposition, extracting public posts through November 20, 2019. We analyzed posts from October 2009 through October 2019 to examine if pages' content was coalescing.Results. Activity in pages promoting vaccine choice as a civil liberty increased in January 2015, April 2016, and January 2019 (t[76] = 11.33 [P < .001]; t[46] = 7.88 [P < .001]; and t[41] = 17.27 [P < .001], respectively). The 2019 increase was strongest in pages mentioning US states (t[41] = 19.06; P < .001). Discussion about vaccine safety decreased (r_s[119] = -0.61; P < .001) while discussion about civil liberties increased (r_s[119] = 0.33; Py < .001]). Page categories increasingly resembled one another (civil liberties: r_s[119] = -0.50 [P < .001]; alternative medicine: r_s[84] = -0.77 [P < .001]; conspiracy theories: r_s[119] = -0.46 [P < .001]; morality: r_s[106] = -0.65 [P < .001]; safety and efficacy: r_s[119] = -0.46 [P < .001]).Conclusions. The "Disneyland" measles outbreak drew vaccine opposition into the political mainstream, followed by promotional campaigns conducted in pages framing vaccine refusal as a civil right. Political mobilization in state-focused pages followed in 2019.Public Health Implications. Policymakers should expect increasing attempts to alter state legislation associated with vaccine exemptions, potentially accompanied by fiercer lobbying from specific celebrities.

Anthropocene-related disease: The inevitable outcome of progressive niche modification?

While the Anthropocene is often discussed in terms of the health of the planet, there has been less attention paid to its impact on the health of humans. We argue that there is now sufficient evidence of broad and growing adverse effects on human health to consider Anthropocene-related diseases and their impact on public health as a category of conditions needing specific recognition and preventative action. Using the examples of climate change-related health challenges, non-communicable disease, antimicrobial resistance and the unique challenges of the digital environment, we discuss how the profound and pervasive environmental changes of the Anthropocene can affect our health, with broad effects on societal health. We frame this concept in terms of human evolutionary history and cultural evolution’s runaway characteristics, reflecting our drive for continual and cumulative innovation for reasons beyond simply survival and Darwinian fitness. As the causative agents are often remote from those populations most adversely affected, prevention and mitigation require collective societal and policy actions.

Lay summary: There is increasing evidence that our uniquely evolved ability to modify our environments rapidly and at an accelerating pace is having impacts on our health, particularly non-communicable diseases and poor mental wellbeing. Reframing these public health challenges as Anthropocene-related diseases emphasizes the need for collective responsibility and systems approaches to prevention.

Psychology of Transnational Terrorism and Extreme Political Conflict

Fear of transnational terrorism, along with a revitalization of sectarian nationalism, is sundering social and political consensus across the world. Can psychology help? The focus of this review is on the psychological and related social factors that instigate and sustain violent extremism and polarizing group conflict. I first describe the changing global landscape of transnational terrorism, encompassing mainly violent Islamist revivalism and resurgent racial and ethnic supremacism. Next, I explore the psychosocial nature of the devoted actor and rational actor frameworks, focusing on how sacred values, identity fusion, and social network dynamics motivate and maintain extreme violence. The psychology of the will to fight and die is illustrated in behavioral and brain studies with frontline combatants in Iraq, militant supporters in Morocco, and radicalizing populations in Spain. This is followed by a consideration of how to deal with value-driven conflicts and a discussion of how the Internet and social media encourage the propagation of polarized conflict.

Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to banning, or dismantling, strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs. unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms.

Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups

Cooperation in human groups is challenging, and various mechanisms are required to sustain it, although it nevertheless usually decays over time. Here, we perform theoretically informed experiments involving networks of humans (1,024 subjects in 64 networks) playing a public-goods game to which we sometimes added autonomous agents (bots) programmed to use only local knowledge. We show that cooperation can not only be stabilized, but even promoted, when the bots intervene in the partner selections made by the humans, re-shaping social connections locally within a larger group. Cooperation rates increased from 60.4% at baseline to 79.4% at the end. This network-intervention strategy outperformed other strategies, such as adding bots playing tit-for-tat. We also confirm that even a single bot can foster cooperation in human groups by using a mixed strategy designed to support the development of cooperative clusters. Simple artificial intelligence can increase the cooperation of groups.

Co-adaptation enhances the resilience of mutualistic networks

Mutualistic networks, which describe the ecological interactions between multiple types of species such as plants and pollinators, play a paramount role in the generation of Earth's biodiversity. The resilience of a mutualistic network denotes its ability to retain basic functionality when errors and failures threaten the persistence of the community. Under the disturbances of mass extinctions and human-induced disasters, it is crucial to understand how mutualistic networks respond to changes, which enables the system to increase resilience and tolerate further damages. Despite recent advances in the modelling of the structure-based adaptation, we lack mathematical and computational models to describe and capture the co-adaptation between the structure and dynamics of mutualistic networks. In this paper, we incorporate dynamic features into the adaptation of structure and propose a co-adaptation model that drastically enhances the resilience of non-adaptive and structure-based adaptation models. Surprisingly, the reason for the enhancement is that the co-adaptation mechanism simultaneously increases the heterogeneity of the mutualistic network significantly without changing its connectance. Owing to the broad applications of mutualistic networks, our findings offer new ways to design mechanisms that enhance the resilience of many other systems, such as smart infrastructures and social-economical systems.

Skepticism and rumor spreading: The role of spatial correlations

Critical thinking and skepticism are fundamental mechanisms that one may use to prevent the spreading of rumors, fake news, and misinformation. We consider a simple model in which agents without previous contact with the rumor, being skeptically oriented, may convince spreaders to stop their activity or, once exposed to the rumor, decide not to propagate it as a consequence, for example, of fact checking. We extend a previous, mean-field analysis of the combined effect of these two mechanisms, active and passive skepticism, to include spatial correlations. This can be done either analytically, through the pair approximation, or simulating an agent-based version on diverse networks. Our results show that while in mean field there is no coexistence between spreaders and susceptibles (although, depending on the parameters, there may be bistability depending on the initial conditions), when spatial correlations are included, because of the protective effect of the isolation provided by removed agents, coexistence is possible.

The online competition between pro- and anti-vaccination views

Distrust in scientific expertise^1-14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks^2-4, as happened for measles in 2019^5,6. Homemade remedies^7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice^9-11. There is a lack of understanding about how this distrust evolves at the system level^13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change¹¹, and highlight the key role of network cluster dynamics in multi-species ecologies¹⁵.

Big Data in der Radikalisierungsforschung

Zusammenfassung. Die Erforschung extremistischer Radikalisierung hat durch digitale Verhaltensspurdaten, wie z. B. Social-Media-Posts oder öffentlich zugänglichen Medien, einen neuen Auftrieb erfahren. Vor dem Hintergrund, dass Big Data als „epistemologische Revolution“ angesehen wird, liefert die vorliegende systematische Literaturübersicht einen Überblick darüber, (i) welche Ziele, Datenquellen und Methoden im Rahmen von Spurdatenstudien in der Radikalisierungsforschung gewählt werden, illustriert exemplarisch einige Ergebnisse dieser Studien und (ii) analysiert welche Gemeinsamkeiten und Unterschiede zu traditionellen Studien wie Fragebogen- oder Experimentalstudien bestehen. Grundlage für den Überblick liefern 63 Studien, von denen allerdings nur eine geringe Anzahl (k = 18) digitale Verhaltensspurdaten nutzten, während der Großteil aus traditionellen Zugängen (k = 52) besteht. Die Ergebnisse zeigen, dass Spurdatenstudien größtenteils darauf abzielten, Personen mit radikalen Einstellungen zu identifizieren und die Entwicklung radikaler Ansichten vorherzusagen. Insgesamt eröffnen sich durch Verhaltensspurdaten bisher ungenutzte Potentiale für die Analyse von Persönlichkeitsprofilen und die Untersuchung dynamischer sozialer Interaktionen derjenigen, die anfällig für extremistische Rekrutierung sind.Eine englische Übersetzung als Rohfassung dieses Artikels finden Sie als Elektronisches Supplement 1.

Multibody interactions and nonlinear consensus dynamics on networked systems

Multibody interactions can reveal higher-order dynamical effects that are not captured by traditional two-body network models. In this work, we derive and analyze models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs. Our work reveals that multibody dynamical effects that go beyond rescaled pairwise interactions can appear only if the interaction function is nonlinear, regardless of the underlying multibody structure. As a practical application, we introduce a specific nonlinear function to model three-body consensus, which incorporates reinforcing group effects such as peer pressure. Unlike consensus processes on networks, we find that the resulting dynamics can cause shifts away from the average system state. The nature of these shifts depends on a complex interplay between the distribution of the initial states, the underlying structure, and the form of the interaction function. By considering modular hypergraphs, we discover state-dependent, asymmetric dynamics between polarized clusters where multibody interactions make one cluster dominate the other.

Are Online Haters Psychopaths? Psychological Predictors of Online Hating Behavior

Despite growing prevalence of derogatory online behaviors, still little is known about psychological factors underlying this negative phenomenon. In the present study, we aimed to compare characteristics of persons who post hating and non-hating comments about Polish sports players during Winter Olympic Games in Pyeongchang (2018) on the Internet. Ninety-four Internet users (41% women) participated in the study, among which 46 posted hating comments. After 1 month, participants were invited to take part in a psychological survey, and filled the Dark Triad questionnaire, the Satisfaction with Life Scale, the Scale of Frustration, and the Scale of Envy. Results showed that high scores in Psychopathy subscale were significant predictors of posting hating comments online; high scores on the Envy Scale were marginally significant. Our findings provide initial evidence that persons who engage in derogatory online behavior have a high level of Psychopathy, but, contrary to previous studies, do not have elevated levels of other traits, commonly associated with disruptive behavior. Our research is one of the first to establish a psychological background of online haters, while setting a clear line between online hating and other derogatory online behaviors (e.g., trolling, cyber-bullying, or hatred speech).

Promoting immunization resiliency in the digital information age

The avalanche of online information on immunization is having a major impact on the percentage of the population who choose to get vaccinated. Vaccine misinformation spreads widely with the interactive Web 2.0 and social media; this can bury science-based information. A plethora of immunization misinformation online is affecting trust in health care professionals and in public immunization programs. There are no simple solutions to this, but seven evidence-based strategies can help. First, listen to patients' and parents' concerns, and demonstrate responsiveness by adopting best immunization practices, such as pain mitigation. Second, recognize and alert others to anti-immunization tactics, namely, conspiracy theories, fake experts, selectivity, demands that vaccines be 100% safe and effective, misrepresentation and false logic. Third, avoid unproductive debates with those who have strongly held views, both in person and when using social media. Be respectful, stick to your key message, identify where to find useful information and exit. Fourth, consider establishing an attractive, easily searchable online presence that reflects the complex art of persuasion. Emphasize the benefits of vaccine, use reader-friendly graphics and highlight facts with stories to strengthen your case. Fifth, work with social media platform providers, not to stifle freedom of expression, but to help ensure that misinformation is not favoured in searches. Sixth, promote curriculum development in the schools to improve students' understanding of the benefits and safety of immunization and to foster critical thinking skills. To do this, optimize the use of age-appropriate comics and interactive learning tools such as electronic games. Seventh, to shift the narrative in specific communities with low vaccination rates, work with community leaders to build tailored programs that foster trust and reflect local values.

Editorial: On Collective Ownership of the Prevention of Mass Casualties Perpetrated by Youth

A great deal has been learned in the aftermath of the Columbine High School massacre, and the science of prevention of mass casualties has matured over the past two decades. This article provides commentary on a new synthesis of the knowledge base, and incorporates very recent work from disparate disciplines that have further bearing on the prevention of such catastrophic events. Collective ownership of evidence-informed facets of primary, secondary, and tertiary prevention by all practicing child and adolescent psychiatrists-in concert with efforts of primary care physicians, educators, policy-makers, law enforcement, and the general public-stands to avert progression of at-risk youth through stages of violent radicalization and the acquisition of means to perpetrate these incidents.

Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines

Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.

Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning

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.