Trends and fluctuations in the severity of interstate wars

Online speech and communal conflict: Evidence from India

How does online speech affect offline attacks? While a growing literature has examined this link in right-wing violence in the West, much less is known about its importance in the religiously divided societies of the Global South. Furthermore, existing research has overwhelmingly focused on negative externalities of social media, while paying comparatively little attention to their conciliatory effects. We advance the scholarship in both of these areas by analyzing 22.4 million posts from Koo, an Indian social media network popular among India's Hindu nationalists. We combine these data with information on attacks on religious minorities in India from 2020 through 2022. We find that the frequency of hashtags with a Hindu-chauvinist connotation are associated with increased attacks on Muslims and Christians. We also find that the frequency of hashtags alluding to the overcoming of religious divisions is associated with fewer attacks. These results survive a battery of robustness checks and supplemental tests. Additionally, the observed relationships disappear during exogenous Internet outages, consistent with the effect being driven by online speech. Importantly, since the content we study is not overtly aggressive and conveys values rather than factual claims, it does not classify as hate speech, misinformation, or disinformation. This suggests that the scholarly debate on what kinds of online speech influence offline harm has to be broadened and that censorship and fact-checking can fall short of addressing online speech's negative consequences in religiously divided societies.

Impacts of armed conflicts on tuberculosis burden and treatment outcomes: a systematic review

OBJECTIVES

This systematic review aimed to summarise existing literature on the impacts of armed conflicts on tuberculosis burden and treatment outcomes.

DESIGN

A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

DATA SOURCES

PubMed, Web of Science, Cumulative Index to Nursing and Allied Health Literature Plus, Scopus, ScienceDirect, Embase and medRxiv.

DATA EXTRACTION AND SYNTHESIS

Three reviewers independently screened, selected eligible studies and extracted data. A narrative review was undertaken to summarise the findings qualitatively.

RESULTS

Eleven studies were included in this review, reporting on tuberculosis incidence rates, prevalence and treatment outcomes, including mortality. Overall, the impact of armed conflicts on case notifications was variable. Six studies reported overall increases in tuberculosis case notifications following the onset of conflicts, while three studies reported overall decreases in tuberculosis case notifications. Factors, including limited access to healthcare services, challenges in surveillance and laboratory confirmation, the destruction of health systems and incapacitating the healthcare workforce, contributed to a decrease in the number of notified cases. The higher tuberculosis notification in some of the studies could be attributed to the disruption of tuberculosis prevention and control programmes as well as increased socioeconomic deprivation, including malnutrition, mass migration, poor living conditions and overcrowding that are worsened during conflicts. Armed conflicts without effective interventions were associated with worse tuberculosis treatment outcomes, including lower proportions of people with treatment success and higher proportions of people with loss to follow-up, mortality and treatment failure. However, implementing various interventions in conflict settings (such as establishing a National Tuberculosis Control Programme) led to higher tuberculosis notification rates and treatment success.

CONCLUSION

The impact of armed conflicts on tuberculosis notification is complex and is influenced by multiple factors. The findings of this review underscore the importance of concerted efforts to control tuberculosis in conflict settings using available resources.

Testing the power-law hypothesis of the interconflict interval

War is an extreme form of collective human behaviour characterized by coordinated violence. We show that this nature of war is substantiated in the temporal patterns of conflict occurrence that obey power law. The focal metric is the interconflict interval (ICI), the interval between the end of a conflict in a dyad (i.e. a pair of states) and the start of the subsequent conflict in the same dyad. Using elaborate statistical tests, we confirmed that ICI samples compiled from the history of interstate conflicts from 1816 to 2014 followed a power-law distribution. We then demonstrate that the power-law properties of ICIs can be explained by a hypothetical model assuming an information-theoretic formulation of the Clausewitz thesis on war: the use of force is a means of interstate communication. Our findings help us to understand the nature of wars between regular states, the significance of which has increased since the Russian invasion of Ukraine in 2022.

Modeling analysis of armed conflict risk in sub-Saharan Africa, 2000-2019

Sub-Saharan Africa has suffered frequent outbreaks of armed conflict since the end of the Cold War. Although several efforts have been made to understand the underlying causes of armed conflict and establish an early warning mechanism, there is still a lack of a comprehensive assessment approach to model the incidence risk of armed conflict well. Based on a large database of armed conflict events and related spatial datasets covering the period 2000-2019, this study uses a boosted regression tree (BRT) approach to model the spatiotemporal distribution of armed conflict risk in sub-Saharan Africa. Evaluation of accuracy indicates that the simulated models obtain high performance with an area under the receiver operator characteristic curve (ROC-AUC) mean value of 0.937 and an area under the precision recall curves (PR-AUC) mean value of 0.891. The result of the relative contribution indicates that the background context factors (i.e., social welfare and the political system) are the main driving factors of armed conflict risk, with a mean relative contribution of 92.599%. By comparison, the climate change-related variables have relatively little effect on armed conflict risk, accounting for only 7.401% of the total. These results provide novel insight into modelling the incidence risk of armed conflict, which may help implement interventions to prevent and minimize the harm of armed conflict.

Detecting trends and shocks in terrorist activities

Although there are some techniques for dealing with sparse and concentrated discrete data, standard time-series analyses appear ill-suited to understanding the temporal patterns of terrorist attacks due to the sparsity of the events. This article addresses these issues by proposing a novel technique for analysing low-frequency temporal events, such as terrorism, based on their cumulative curve and corresponding gradients. Using an iterative algorithm based on a piecewise linear function, our technique detects trends and shocks observed in the events associated with terrorist groups that would not necessarily be visible using other methods. The analysis leverages disaggregated data on political violence from the Armed Conflict Location & Event Data Project (ACLED) to analyse the intensity of the two most violent terrorist organisations in Africa: Boko Haram (including its splinter group, the Islamic State West Africa Province), and Al-Shabaab. Our method detects moments when terrorist groups change their capabilities to conduct daily attacks and, by taking into account the directionality of attacks, highlights major changes in the government's strategies. Results suggest that security policies have largely failed to reduce both groups' forces and restore stability.

Discovering the mesoscale for chains of conflict

Conflicts, like many social processes, are related events that span multiple scales in time, from the instantaneous to multi-year development, and in space, from one neighborhood to continents. Yet, there is little systematic work on connecting the multiple scales, formal treatment of causality between events, and measures of uncertainty for how events are related to one another. We develop a method for extracting causally related chains of events that addresses these limitations with armed conflict. Our method explicitly accounts for an adjustable spatial and temporal scale of interaction for clustering individual events from a detailed data set, the Armed Conflict Event & Location Data Project. With it, we discover a mesoscale ranging from a week to a few months and tens to hundreds of kilometers, where long-range correlations and nontrivial dynamics relating conflict events emerge. Importantly, clusters in the mesoscale, while extracted from conflict statistics, are identifiable with mechanism cited in field studies. We leverage our technique to identify zones of causal interaction around conflict hotspots that naturally incorporate uncertainties. Thus, we show how a systematic, data-driven, and scalable procedure extracts social objects for study, providing a scope for scrutinizing and predicting conflict and other processes.

A theoretical analysis of complex armed conflicts

The introduction and analysis of a simple idealized model enables basic insights into how military characteristics and recruitment strategies affect the dynamics of armed conflicts, even in the complex case of three or more fighting groups. In particular, the model shows when never ending wars (stalemates) are possible and how initial conditions and interventions influence a conflict’s fate. The analysis points out that defensive recruitment policies aimed at compensating for suffered losses lead to conflicts with simple dynamics, while attack groups sensitive to the damages they inflict onto their enemies can give rise to conflicts with turbulent behaviours. Since non-governmental groups often follow attack strategies, the conclusion is that the evolution of conflicts involving groups of that kind can be expected to be difficult to forecast.

Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena

The global rise of COVID-19 health risk has triggered the related misinformation infodemic. We present the first analysis of COVID-19 misinformation networks and determine few of its implications. Firstly, we analyze the spread trends of COVID-19 misinformation and discover that the COVID-19 misinformation statistics are well fitted by a log-normal distribution. Secondly, we form misinformation networks by taking individual misinformation as a node and similarity between misinformation nodes as links, and we decipher the laws of COVID-19 misinformation network evolution: (1) We discover that misinformation evolves to optimize the network information transfer over time with the sacrifice of robustness. (2) We demonstrate the co-existence of fit get richer and rich get richer phenomena in misinformation networks. (3) We show that a misinformation network evolution with node deletion mechanism captures well the public attention shift on social media. Lastly, we present a network science inspired deep learning framework to accurately predict which Twitter posts are likely to become central nodes (i.e., high centrality) in a misinformation network from only one sentence without the need to know the whole network topology. With the network analysis and the central node prediction, we propose that if we correctly suppress certain central nodes in the misinformation network, the information transfer of network would be severely impacted.

Unifying casualty distributions within and across conflicts

The distribution of whole war sizes and the distribution of event sizes within individual wars, can both be well approximated by power laws where size is measured by the number of fatalities. However the power-law exponent value for whole wars has a substantially smaller magnitude – and hence a flatter distribution – than for individual wars. We provide detailed numerical evidence that confirms that these numerically different power-law exponent values are interrelated in a simple way by the effect of aggregating fatalities from individual events within wars to whole wars. We offer intuition for this finding and hence strengthen the case for a unified description and understanding of human conflict across scales.

Mapping global variation in human mobility

The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth's populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes.

Extraordinary curtailment of massive typhus epidemic in the Warsaw Ghetto

The highly dependent interplay of disease, famine, war, and society is examined based on an extreme period during World War II. Using mathematical modeling, we reassess events during the Holocaust that led to the liquidation of the Warsaw Ghetto (1941-1942), with the eventual goal of deliberately killing ~450,000, mostly Jewish residents, many through widespread starvation and a large-scale typhus epidemic. The Nazis justified genocide supposedly to control the spread of disease. This exemplifies humanity's ability to turn upon itself, based on racially guided epidemiological principles, merely because of the appearance of a bacterium. Deadly disease and starvation dynamics are explored using modeling and the maths of food ration cards. Strangely, the epidemic was curtailed and was brought to a sudden halt before winter, when typhus normally accelerates. A far more massive epidemic outbreak was prevented through the antiepidemic efforts by the often considered incompetent and corrupt ghetto leadership and the Herculean efforts of ghetto doctors.

Common statistical patterns in urban terrorism

The underlying reasons behind modern terrorism are seemingly complex and intangible. Despite diverse causal mechanisms, research has shown that there exists general statistical patterns at the global scale that can shed light on human confrontation behaviour. While many policing and counter-terrorism operations are conducted at a city level, there has been a lack of research in building city-level resolution prediction engines based on statistical patterns. For the first time, the paper shows that there exist general commonalities between global cities under frequent terrorist attacks. By examining over 30 000 geo-tagged terrorism acts over 7000 cities worldwide from 2002 to today, the results show the following. All cities experience attacks A that are uncorrelated to the population and separated by a time interval t that is negative exponentially distributed with a death-toll per attack that follows a power-law distribution. The prediction parameters yield a high confidence of explaining up to 87% of the variations in frequency and 89% in the death-toll data. These findings show that the aggregate statistical behaviour of terror attacks are seemingly random and memoryless for all global cities. They enabled the author to develop a data-driven city-specific prediction system, and we quantify its information-theoretic uncertainty and information loss. Further analysis shows that there appears to be an increase in the uncertainty over the predictability of attacks, challenging our ability to develop effective counter-terrorism capabilities.

What motivated the Industrial Revolution: England's libertarian culture or affluence per se?

What impelled the Industrial Revolution's spectacular economic growth? Life History Theory, Baumard argues, explains how England's world-supreme affluence psychologically fostered innovation; moreover, wherever similar affluence abounds, a “civilizing process” bringing enlightenment and democracy is apt to evolve. Baumard insightfully analyzes a “constellation of affluence” but proffers somewhat whiggish history given England's prior and unique proto-capitalist culture of economic liberty and individualism.

Quantifying the Holocaust: Hyperintense kill rates during the Nazi genocide

Operation Reinhard (1942-1943) was the largest single murder campaign of the Holocaust, during which some 1.7 million Jews from German-occupied Poland were murdered by the Nazis. Most perished in gas chambers at the death camps Belzec, Sobibor, and Treblinka. However, the tempo, kill rates, and spatial dynamics of these events were poorly documented. Using an unusual dataset originating from railway transportation records, this study identifies an extreme phase of hyperintense killing when >1.47 million Jews-more than 25% of the Jews killed in all 6 years of World War II-were murdered by the Nazis in an intense,100-day (~3-month) surge. Operation Reinhard is shown to be an extreme event, based on kill rate, number, and proportion (>99.9%) of the population murdered in camps, highlighting its singularly violent character, even compared to other more recent genocides. The Holocaust kill rate is some 10 times higher than estimates suggested by authorities on comparative genocide.

Fundamental patterns and predictions of event size distributions in modern wars and terrorist campaigns

It is still unknown whether there is some deep structure to modern wars and terrorist campaigns that could, for example, enable reliable prediction of future patterns of violent events. Recent war research focuses on size distributions of violent events, with size defined by the number of people killed in each event. Event size distributions within previously available datasets, for both armed conflicts and for global terrorism as a whole, exhibit extraordinary regularities that transcend specifics of time and place. These distributions have been well modelled by a narrow range of power laws that are, in turn, supported by some theories of violent group dynamics. We show that the predicted event-size patterns emerge broadly in a mass of new event data covering all conflicts in the world from 1989 to 2016. Moreover, there are similar regularities in the events generated by individual terrorist organizations, 1998—2016. The existence of such robust empirical patterns hints at the predictability of size distributions of violent events in future wars. We pursue this prospect using split-sample techniques that help us to make useful out-of-sample predictions. Power-law-based prediction systems outperform lognormal-based systems. We conclude that there is indeed evidence from the existing data that fundamental patterns do exist, and that these can allow prediction of size distribution of events in modern wars and terrorist campaigns.