Riot Events Research Articles

Predicting Protests and Riots in Urban Environments With Satellite Imagery and Deep Learning

ABSTRACTConflict, manifesting as riots and protests, is a common occurrence in urban environments worldwide. Understanding their likely locations is crucial to policymakers, who may (for example) seek to provide overseas travelers with guidance on safe areas, or local policymakers with the ability to pre‐position medical aid or police presences to mediate negative impacts associated with riot events. Past efforts to forecast these events have focused on the use of news and social media, restricting applicability to areas with available data. This study utilizes a ResNet convolutional neural network and high‐resolution satellite imagery to estimate the spatial distribution of riots or protests within urban environments. At a global scale (N = 18,631 conflict events), by training our model to understand relationships between urban form and riot events, we are able to predict the likelihood that a given urban area will experience a riot or protest with accuracy as high as 97%. This research has the potential to improve our ability to forecast and understand the relationship between urban form and conflict events, even in data‐sparse regions.

Objective Method for Determining the Importance of Unprecedented Restlessness as a Rice Crisis Indicator at the National Level

In this study, we use restlessness as an input for a rice crisis indicator, since restlessness rather than rice price provides a comparable year-to year context. We outline the significant increase in the use of unprecedented restlessness (UR) as an indicator for rice crises. The UR approach involves a precedence analysis, in contrast with the existing approach, the price shock analysis. We test UR as a new indicator for rice crises at the national level, which can be applied in Asia and other countries around the world where rice is the staple food. Strong indicators point out the effectiveness of strategic government programs and are able to assess solutions and detect rice crises, while weak indicators are only reliable in detecting whether or not there has been a crisis. UR is tested across 43 countries using two new statistics: success probability (SP) and constraint probability (CP). As a consequence of SP and CP calculations, a large number of IMR control charts for UR analysis are constructed to provide evidence that UR is a strong indicator. The optimum validity measurement result is achieved with SP = 8/26 = 0.31 and CP = 8/14 = 0.57. This means that the UR detects and is followed by only 31% of riot events. Since the value of SP is less than 0.6, we can conclude that the UR indicator is not considered valid as an indicator of rice crises at the national level. The values of CP and SP are determined subjectively as equal to 0.6. This is the main cause of the emergence of new problems in the calibration of UR as an indicator of rice crises. The subjective success criteria trigger a question regarding why the value is 0.6, for which there is no scientific justification. Based on this background, we continue to objectively establish success criteria for UR validity. After conducting a risk analysis involving a crisis recovery cost (CRC) to crisis anticipation cost (CAC) ratio, it is found that the probability of the CRC-to-CAC ratio having values greater than 7 is 0.76, which means the CRC-to-CAC ratio tends to be higher than 7. Objectively, it is concluded that UR, which has been defined as rice crisis indicator at the national level, is an important indicator.

You talkin’ to me? Exploring Human/Bot Communication Patterns during Riot Events

We analyze a data-set including more than 4.5 million tweets related to four highly emotional riot events. In particular, we examine statistically significant structural patterns that emerge as humans directly engage in an exchange of emotional messages with other humans on Twitter. Furthermore, we compare typical human-to-human communication patterns with those that emerge as bots engage in an emotional message-exchange with human users. To this end, we apply the novel concept of emotion-exchange motifs. We found that a) human-to-human conversations results in a variety of motifs that contain reciprocal edges and self-loops, b) bots predominantly contribute to the emergence of message broadcasting, single-way message sending behavior, c) in contrast to previous findings we found that in certain events bots frequently engage in direct message exchanges with humans, d) during riot events bots tend to direct fear-conveying messages to human users.

Pick your language: how riot reporting differs between English and Hindi newspapers in India

ABSTRACTRiot reporting is one aspect of newspaper coverage that can drive people into the streets in acts of collective protest or violence. Media observers and scholars have proposed that the language of Indian newspapers, be it English or vernacular, partially dictates the kinds of riot events reported and the quality of those reports. I tested whether this conventional wisdom holds by investigating the content of Indian riot coverage in the English Times of India and Hindu Hindustan. While Hindustan emphasized official statements and interviews with political parties, neither newspaper accurately represented the actual number of riots in their reporting. In fact, coverage in both papers followed predictable patterns likely driven by a new focus on selling newspapers at any cost in order to increase advertising revenue. This study contributes to a growing literature highlighting the similarities between media outlets and the degree to which their reporting is removed from actual events.

The non-financial costs of violent public disturbances: Emotional responses to the 2011 riots in England

The August of 2011 saw the largest riots in the United Kingdom in decades. Half of London's boroughs, as well as neighborhoods in several other cities, were impacted through the more than 200 individual riot events that caused £200 ($300) million in property damage. Despite widespread media coverage at the time, we know little about what citizens experienced during the riots. This paper bridges that gap using daily response panel data (from the Mappiness smartphone application) to estimate the beyond-monetary costs of the riots. Based on the difference-in-differences estimation, the disturbances substantially increased unhappiness and stress in areas they affected. This negative effect was even more pronounced in areas with the biggest proportion of Black residents, and it also reached a national scale, as even neighborhoods without riots experienced a pronounced wellbeing loss. The negative effects persisted beyond the end of the disturbances, at least until the end of the summer. Citizens changed their behavior in response to the events, respondents in neighborhoods with riots started seeking information and communicating more, which manifested in higher levels of TV watching, texting, email, and social media use. The English riots form part of a larger trend in current social tensions—with a marked wellbeing loss for the majority of Brits.

One False Step: Detroit, Step and Movies of Rising and Falling

Detroit and Step are two recent movies in the context of urban riots in protest of police brutality. They refer to time periods separated by half a century, but there are common themes in the two that seem appropriate to both times. The movies are not primarily concerned with the riot events, but the riot is a major motivating force in both; Detroit is pessimistic, while Step is hopeful. Together, they define alternative possibilities of African American community life. One is eternally sought, and the other is always a dreadful possibility.

Riot Rewards? Religious Conflict and Electoral Outcomes

Do incidents of ethno-religious polarization influence voter behavior, and hence benefit nationalist right-wing parties? We address this question through the case study of India, the world’s largest functional democracy. Specifically, we examine whether prior events of Hindu-Muslim riots electorally benefit the Bharatiya Janta Party (BJP), a prominent Hindu nationalist party. The paper contributes to the literature by proposing a novel instrument to demonstrate a causal relationship between Hindu-Muslim riots and BJP’s electoral performance. Exploiting the association between temperature and conflict, i.e. moderately high temperature increases conflict by provoking aggressive behavior while very high temperature dampens it by curtailing mobility, we use temperature variation as an instrument for Hindu-Muslim riots. Results show that riots significantly increase BJP’s voteshare in subsequent elections. We explain this causal association through the ethnic voting framework i.e. riot events consolidate the Hindu vote in favour of BJP and improve its electoral performance.

Spatial Patterns in the 2011 London Riots

Riots broke out in London on 6th August 2011. Many narratives of the riots describe them as having subsequently intensified within and spread across various parts of the city during the following 4 days and nights before finally petering out. This article addresses two important empirical puzzles stemming from the riots. First, we ask whether the riot events did indeed display distinct spatial patterns and, if so, whether these events spread between locations. Second, we discuss how knowledge of the spatial distribution of these events could influence future analytical research on the topic and how, in turn, this work could inform crime reduction strategies for events of this type. Language: en

The Search for Order: Understanding Hindu-Muslim Violence in Post-Partition India

One distinguishing feature of mainstream social science is its growing regard for model building and formal hypothesis testing. In South Asian studies this is most evident in accounts of ethnic riots or communal violence. This paper examines a model of votes and violence proposed by Steven Wilkinson. We first examine how well the model performs against a data set that we have assembled on the twenty worst incidents of communal violence in India since 1950. The Wilkinson model is consistent with some important key facts in our data set, most notably in terms of levels of urbanization and percentage Muslims in riotaffected towns and cities. However, proximity to national or state elections is not found to be a strong driver of prolonged ethnic rioting. Nor is it the case that India's worst instances of communal violence occurred mainly where there was direct electoral competition between less than 3.5 effective political parties, the other main predictive variable in the Wilkinson model. We then discuss the limitations more broadly of attempts to explain and even predict ethnic violence within the framework of a quantitative model. We pay attention to time inconsistencies, principal-agent problems, religiosity and the homogenization of riot events, and omitted variables (notably, memory work and ideological fervour). We conclude with some general remarks on the search for order in social science.

The dynamics of riot growth: An epidemiological approach

A new approach to the study of large‐scale urban riots has resulted in the discovery of remarkably coherent patterns in the distribution of riot events over time. Patterns within three major riots suggest that the dynamics of the spread of riot behavior during a riot can be fruitfully compared to those operative in classical epidemics. We therefore conceptualize riots as behavioral epidemics, and apply the mathematical theory of epidemics to data from the Los Angeles (1965), Detroit (1967), and Washington, D.C., (1968) riots.

Black Ghetto Violence: A Case Study Inquiry into the Spatial Pattern of Four Los Angeles Riot Event-Types

A riot is not “all hell breaking loose,” but a complex sociopolitical process. Patterns not apparent on the surface characterize different forms of riotous behavior, can be uncovered through systematic analysis, and do shed new light on the rioting phenomenon. Nearly 2,000 Los Angeles riot events of four discrete classes have been coded by census tract location. Stepwise multiple regression analysis employing sociodemographic and retail-establishment-location variables has revealed remarkably ordered and structured patterns. Looting emerges as a qualitatively different phenomenon from the other riot event-types considered, calling into question the simplistic characterization of “rioting mainly for fun and profit” drawn by Banfield, and suggesting that what is needed are studies which address rioting in some measure of its true complexity. Two sociodemographic variables consistently emerge as highly predictive of riot event spatial patterns, allowing speculation concerning the sociodemographic fertile ground for violent political protest.

Young children and the Watts revolt

Immediately following the August, 1965 Los Angeles (Watts) race rioting, semistructured, interviews were obtained with 107 Negro, 23 Mexican-American and 52 white preschool children. Response data were used to assess the children's awareness of riot events, their fears or other affective reactions and their attitudes toward the riot participants. Over 70% in each group were aware of the rioting and approximately 50% gave evidence of rio-trelated fear. Attitudes of Negro and white children toward the rioters varied in (positive-negative) direction and intensity, while the small Mexican-American group was more uniformly hostile-fearful.