Understanding recurrent crime as system-immanent collective behavior

Containing the spreading of crime is a major challenge for society. Yet, since thousands of years, no effective strategy has been found to overcome crime. To the contrary, empirical evidence shows that crime is recurrent, a fact that is not captured well by rational choice theories of crime. According to these, strong enough punishment should prevent crime from happening. To gain a better understanding of the relationship between crime and punishment, we consider that the latter requires prior discovery of illicit behavior and study a spatial version of the inspection game. Simulations reveal the spontaneous emergence of cyclic dominance between ''criminals'', ''inspectors'', and ''ordinary people'' as a consequence of spatial interactions. Such cycles dominate the evolutionary process, in particular when the temptation to commit crime or the cost of inspection are low or moderate. Yet, there are also critical parameter values beyond which cycles cease to exist and the population is dominated either by a stable mixture of criminals and inspectors or one of these two strategies alone. Both continuous and discontinuous phase transitions to different final states are possible, indicating that successful strategies to contain crime can be very much counter-intuitive and complex. Our results demonstrate that spatial interactions are crucial for the evolutionary outcome of the inspection game, and they also reveal why criminal behavior is likely to be recurrent rather than evolving towards an equilibrium with monotonous parameter dependencies.Comment: 9 two-column pages, 5 figures; accepted for publication in PLoS ON

Drivers of COVID-19 protest across localities in Israel: a machine-learning approach

Anti-government protests emerged globally in response to COVID-19 countermeasures. What are the key drivers of these pandemic-related protests, and to what extent do they differ from the drivers of non-COVID protests? We examine these questions in the context of Israel, which faced a growing political crisis at the start of the pandemic, effectively blurring the distinction between different causes of protest. Our data features 1,922 protests across 189 Israeli localities for the period between March and July 2022. Using a machine learning approach, we find that all protests, regardless of whether they were directly related to the pandemic or not, were motivated by the same set of key indicators – albeit with the ranking of drivers for COVID-related protests inverted for non-COVID protests. Local infection rates and government responses were more pronounced for the former, whereas differences in residential and commercial property taxes, access to affordable housing, quality of education and demography were among the most important drivers for the latter. Our analysis underscores the role that local governments played in managing the pandemic, and demonstrates that variation in socioeconomic conditions had an important effect on the incidence of protests across Israel

Trajectories of resilience to acute malnutrition in the Kenyan drylands

IntroductionInsight into the resilience of local food systems—variability driven by climate, conflict, and food price shocks—is critical for the treatment and prevention of child acute malnutrition.MethodsWe use a combination of latent class mixed modeling and time-to-event analysis to develop and test a measure of resilience that is outcome-based, sensitive to specific shocks and stressors, and captures the enduring effects of how frequently and severely children face the risk of acute malnutrition.ResultsHarnessing a high-resolution longitudinal dataset with anthropometric information on 5,597 Kenyan households for the 2016–20 period, we identify resilience trajectories for 141 wards across Kenya. These trajectories—characterized by variation in the duration and severity of episodes of acute malnutrition—are associated with differential risk: (1) some 57% of wards exhibit an increasing trajectory—high household risk despite growing resilience; (2) 39% exhibit chronic characteristics—showing no real signs of recovery after an episode of crisis; (3) 3% exhibit robust characteristics—low variability with low-levels of individual household risk; whereas (4) 1% show a steady decrease in resilience—associated with high levels household risk.DiscussionOur findings highlight the importance of measuring resilience at the ward-level in order to better understand variation in the nutritional status of rural households

How does the geography of surveillance affect collective action?

How does residing in the proximity of surveillance infrastructure—i.e., checkpoints, the separation barrier, and military installations—affect support for cooperative and confrontational forms of collective action? Cooperative actions involve engagement with outgroups to advance the ingroup cause (e.g., negotiations, joint actions, and peace movements), whereas confrontational actions involve unilateral tactics to weaken the outgroup (e.g., boycott, armed resistance). In the context of West Bank and Jerusalem, we combine geo‐coded data on the surveillance infrastructure with a representative survey of the adult population from 49 communities (N = 1,000). Our multilevel analyses show that surveillance does not affect support for confrontational actions but instead decreases support for cooperative actions. Moreover, we identify a new, community‐level mechanism whereby surveillance undermines cooperative actions through weakening inclusive norms that challenge dominant us‐versus‐them perspectives. These effects are empirically robust to various individual‐ and community‐level controls, as well as to the potential of reverse causality and residential self‐selection. Our findings illustrate how cooperative voices and the fabric of social communities become the first casualties of exposure to surveillance. They also speak to the importance of considering structural factors, with broader implications for the socio‐psychological study of collective action

Newsalyze: Effective Communication of Person-Targeting Biases in News Articles

Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines state-of-the-art methods from natural language understanding. Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers. Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption, e.g., we present respondents with a news overview and individual articles. We not only measure the visualizations' effect on respondents' bias-awareness, but we can also pinpoint the effects on individual components of the visualizations by employing a conjoint design. Our bias-sensitive overviews strongly and significantly increase bias-awareness in respondents. Our study further suggests that our content-driven identification method detects groups of similarly slanted news articles due to substantial biases present in individual news articles. In contrast, the reviewed prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets

What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter

News stories circulating online, especially on social media platforms, are nowadays a primary source of information. Given the nature of social media, news no longer are just news, but they are embedded in the conversations of users interacting with them. This is particularly relevant for inaccurate information or even outright misinformation because user interaction has a crucial impact on whether information is uncritically disseminated or not. Biased coverage has been shown to affect personal decision-making. Still, it remains an open question whether users are aware of the biased reporting they encounter and how they react to it. The latter is particularly relevant given that user reactions help contextualize reporting for other users and can thus help mitigate but may also exacerbate the impact of biased media coverage. This paper approaches the question from a measurement point of view, examining whether reactions to news articles on Twitter can serve as bias indicators, i.e., whether how users comment on a given article relates to its actual level of bias. We first give an overview of research on media bias before discussing key concepts related to how individuals engage with online content, focusing on the sentiment (or valance) of comments and on outright hate speech. We then present the first dataset connecting reliable human-made media bias classifications of news articles with the reactions these articles received on Twitter. We call our dataset BAT - Bias And Twitter. BAT covers 2,800 (bias-rated) news articles from 255 English-speaking news outlets. Additionally, BAT includes 175,807 comments and retweets referring to the articles. Based on BAT, we conduct a multi-feature analysis to identify comment characteristics and analyze whether Twitter reactions correlate with an article’s bias. First, we fine-tune and apply two XLNet-based classifiers for hate speech detection and sentiment analysis. Second, we relate the results of the classifiers to the article bias annotations within a multi-level regression. The results show that Twitter reactions to an article indicate its bias, and vice-versa. With a regression coefficient of 0.703 ( ), we specifically present evidence that Twitter reactions to biased articles are significantly more hateful. Our analysis shows that the news outlet’s individual stance reinforces the hate-bias relationship. In future work, we will extend the dataset and analysis, including additional concepts related to media bias

Human-in-the-Loop Hate Speech Classification in a Multilingual Context

The shift of public debate to the digital sphere has been accompanied by a rise in online hate speech. While many promising approaches for hate speech classification have been pro- posed, studies often focus only on a single language, usually English, and do not address three key concerns: post-deployment perfor- mance, classifier maintenance and infrastruc- tural limitations. In this paper, we introduce a new human-in-the-loop BERT-based hate speech classification pipeline and trace its de- velopment from initial data collection and an- notation all the way to post-deployment. Our classifier, trained using data from our original corpus of over 422k examples, is specifically developed for the inherently multilingual set- ting of Switzerland and outperforms with its F1 score of 80.5 the currently best-performing BERT-based multilingual classifier by 5.8 F1 points in German and 3.6 F1 points in French. Our systematic evaluations over a 12-month period further highlight the vital importance of continuous, human-in-the-loop classifier main- tenance to ensure robust hate speech classifica- tion post-deployment

Household behavior and vulnerability to acute malnutrition in Kenya

Anticipating those most at-risk of being acutely malnourished significantly shapes decisions that pertain to resource allocation and intervention in times of food crises. Yet, the assumption that household behavior in times of crisis is homogeneous—that households share the same capacity to adapt to external shocks—ostensibly prevails. This assumption fails to explain why, in a given geographical context, some households remain more vulnerable to acute malnutrition relative to others, and why a given risk factor may have a differential effect across households? In an effort to explore how variation in household behavior influences vulnerability to malnutrition, we use a unique household dataset that spans 23 Kenyan counties from 2016 to 2020 to seed, calibrate, and validate an evidence-driven computational model. We use the model to conduct a series of counterfactual experiments on the relationship between household adaptive capacity and vulnerability to acute malnutrition. Our findings suggest that households are differently impacted by given risk factors, with the most vulnerable households typically being the least adaptive. These findings further underscore the salience of household adaptive capacity, in particular, that adaption is less effective for economic vis-à-vis climate shocks. By making explicit the link between patterns of household behavior and vulnerability in the short- to medium-term, we underscore the need for famine early warning to better account for variation in household-level behavior

Saving Human Lives: What Complexity Science and Information Systems can Contribute

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.Comment: 67 pages, 25 figures; accepted for publication in Journal of Statistical Physics [for related work see http://www.futurict.eu/