Research

We provide original insight into how governments solicit foreign support during wartime by examining how Ukrainian officials have used social media during its ongoing war with Russia. We combine the Twitter histories of more than 100 top Ukrainian officials with tens of millions of tweets about the war to identify key networks of foreign consumers that the Ukrainian government targets with messages. Our analysis provides three conclusions: First, Twitter is a strategic tool for communicating with international audiences, as both the volume of government tweets increases dramatically and shifts toward English-language posting as soon as the invasion begins. Second, most of those tweets focus on several different international themes and circulate in 9 distinct international networks, which range from governments to human rights organizations to mass media. Third, The content and sentiment of government messaging impact the extent to which they disseminate across different foreign constituencies. For instance, US and Canadian government twitter networks are much more responsive to tweets about human rights violations than they are to those about security considerations. All told, we provide the first large-scale, systematic test of the efficacy of social media outreach efforts to solicit foreign support during war.

Policy Report

Over the past 10 years, corruption scandals have rattled civic space across many countries. Much programming has focused on disseminating information to citizens about government malfeasance as a means of promoting accountability, but the results have varied considerably. Instead of relying on a complex chain of accountability running from voters to politicians, we engage directly with CSOs and bureaucrats themselves. We test whether delivering information on corruption directly to either the CSOs mobilizing civil society or the bureaucrats involved in procurement provides a more direct means of reducing corruption. Our preliminary findings suggest that information about corruption might lead local civil society leaders to plan more organizing to fight corruption, but only when that information shows that corruption is worse than expected. Similarly, information on corruption leads contracting bureaucrats to reduce the extent to which they prioritize legal documents, firm attributes, and a firm’s past experience when assessing bids for government contracts. All told, our preliminary results suggest an interesting, more direct, path for impacting corruption.

Policy Report

This report analyzes the impact of over 200 arrests and murders of journalists on press willingness to report on politically sensitive topics in the following weeks and months. We combine dated arrest and murder data from the Committee to Protect Journalists (CPJ) with data from the Machine Learning for Peace (MLP) project to examine if the ‘shock’ of a violent event changes reporting on 7 different civic space event types in the weeks and months following the event. We report 3 key findings. First, that coverage of several types of civic space events does not change after violence against journalists. Nevertheless, we find that the arrest of journalists represses reporting on several sensitive topics, including police raids, corruption and defamation cases. In contrast, we find that the murder of journalists increases reporting on corruption, legal action and raids.

Policy Report

Many aspiring autocrats have passed legal restrictions on citizens’ online behavior. Unfortunately, there is little rigorous evidence on the impact of that legal repression. We provide such evidence from Tanzania, where we analyze the impact of the 2015 Cybercrimes Law on posts on political threads in Jamii Forums — a widely used, citizen-driven online platform in the country. Our analysis of more than 11 million individual posts reveals several insightful findings on the ways in which online media repression succeeds and fails to impact citizen postings.

Policy Report

Despite the centrality of corruption scandals to civic space dynamics in many countries, our capacity to understand when, why and where corruption elicits civic responses is sharply limited by constraints inherent in standard annual corruption data. In this report we introduce a new big data approach to measuring corruption that allows researchers and analysts to ask new questions. By measuring the share of monthly news reporting on corruption in 53 countries over 11 years, we provide data on its salience. This measure does a good job of identifying corruption scandals and provides a tool for monitoring corruption in near real-time.

Policy Brief

Policy Report

In March 2020, the World Health Organization officially declared COVID-19 a pandemic, sparking a global wave of emergency measures designed to combat the virus’s spread. The onset of the pandemic and the perceived need for temporary but dramatic restrictions on fundamental liberties — such as the freedoms of movement and assembly — to preserve public health, shaped political conflict in the ensuing months in important ways. We investigate the impact of COVID-19 on political conflict using high-frequency data on government declarations of emergency, levels of civic activism, and government coercion in the months before and after the onset of the pandemic in March 2020. We discuss several specific cases (Serbia, Albania, Belarus, Ukraine, Ethiopia, and Sri Lanka) to tease out the sequencing of civic activism and government coercion after the onset of the pandemic. We find that most countries saw a sustained decline in government coercion, likely due to voluntary compliance with emergency measures, although levels generally returned to normal within one year. Most countries also saw a brief reduction in civic activity, but levels returned to normal within six months in most countries and within one year in nearly all. The return of civic activity was usually related to elections or instances of government repression, while mobilization motivated by dissatisfaction with COVID response itself was relatively rare. These findings have implications for our understanding of how crises affect political mobilization and conflict.

Policy Brief

Policy Report

One crucial feature of the ongoing global wave of democratic backsliding is that aspiring autocrats seek to influence the media, oftentimes through legal restrictions on the press and social media. Yet little research has examined how formal and social media respond to those legal restrictions targeting the free flow of information. We develop an original argument linking key characteristics of media sources to the regulatory environment and examine how the content and sentiment of their coverage responds to restrictive media laws. We test our claims using an enormous corpus of electronic media in Tanzania and employ two state-of-the-art neural network models to classify the topics and sentiment of news stories. We then estimate diff-in-diff models exploiting a significant legal change that targeted media houses. We find that critical news sources censor the tone of their coverage, even as they continue to cover the same issues; we also find that international news sources are unable to fill the hole left by a critical domestic press. The paper sheds light on the conditions under which the press can be resilient in the face of legal threats.

Policy Brief

Policy Report

Civil society is a powerful force for political change and democratic accountability. Understanding this, a growing number of governments have cultivated a diverse repertoire of repressive tactics, ranging from legal sanctions to outright physical coercion. Advances in big data analytics are endowing governments with new tools, including the ability to anticipate citizen action and engage in preemptive repression. Civil society needs new tools to navigate increasingly sophisticated repression. In this research note, we report on our ability to forecast to civic space using the MLP dataset. Analyzing 9 different civic space event types and 39 countries, we find that ror most country-event pairs, we cannot reliably predict shocks. However, we are able to predict certain shocks in certain places with considerable precision. We accomplish this using interpretable models that reveal the model’s decision-making process. Interpretable models provide a way for practitioners with contextual knowledge to judge how reliable models are in the real world. Thus, we provide the basis for an `early warning system’ that could help civil society strategize around repressive government action. 

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How donors respond to closing civic space has important implications for the incentives facing aspiring autocrats intent on democratic backsliding. If NGO laws are effective in cutting-off support for advocacy work and are not met with resistance or repercussions from donors, legal restrictions on civil society are likely to continue to proliferate. We examine how donors respond to such restrictions using data on 2.3 million aid projects, original global data tracking NGOs laws, and a variety of research designs. We find evidence that in response to restrictive NGO laws, advocacy-oriented donors decrease spending on advocacy and maintain spending on development. In short, restrictive NGO laws `work’ from the point of view of repressive governments: donors reduce their support for activities that aspiring autocrats find threatening, such as political advocacy, while funding for service-oriented development projects continues unabated.

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Getting an accurate picture of any country’s civic space is difficult. While many analysts rely on the international news, the vast majority of news coverage on any given country is the news media in that country. The INSPIRES Machine Learning for Peace team has spent enormous time ensuring it is extracting as much news as possible from national sources. But what are the returns to all that effort?

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A key feature of the global wave of democratic backsliding is that aspiring autocrats seek to influence the media through legal restrictions. We develop an original argument linking media characteristics to the regulatory environment and test it using a huge corpus of electronic media in Tanzania. We employ two state-of-the-art machine learning models to classify the topics and sentiment of news stories and exploit a significant legal change that targeted media houses. We find that critical news sources censor the tone of their articles but continue to cover the same topics; we also find that international news sources do not fill the hole left by a critical domestic press. The paper sheds light on the conditions under which the press can be resilient in the face of legal threats.

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To limit oversight by civil society, governments often repress NGOs. However, quantitative research has yet to investigate how restricted civic space impacts the behavior of NGOs operating in diverse sectors. Surveying employees from 106 NGOs in Cambodia, we employ a conjoint experiment to identify how the prevalence of repression affects NGOs’ pursuit of funding via grant applications. We find that although increases in the perceived prevalence of harassment has a stronger deterrent effect on advocacy work, harassment also deters NGOs focused on service delivery. Our results suggest that local officials target both advocacy and service delivery NGOs, but for different reasons.

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This report uses the INSPIRES data to investigate the association between protests and the passage of laws bearing on civic space. The relationship between protest and legal changes has important policy and academic implications. Practitioners often face decisions about whether to support protest movements in support of legal openings, as well as whether to support movements protesting against legal closures. Previous research provides competing findings on the relationship between protests and legal restrictions on free assembly and civic space. That work, however, has been hamstrung by poor data on both protests and the timing and characteristics of laws bearing on civic space.

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Given the increased foreign policy efforts of Russia and China, combined with growing concerns about the decline of democracy worldwide, this report examines their influence around elections. However, quantitative analysis of resurgent authoritarian influence (RAI) faces data limitations. Using high-frequency RAI data from the Machine Learning for Peace (MLP) project, drawn from millions of news articles covering the period 2012-2023, the study explores correlations between RAI themes and electoral timing. The results indicate active domestic interference tactics by authoritarian regimes before and during elections, with the importance of these tactics declining after elections. Diplomatic activity increases significantly after elections, and economic influence remains constant, unaffected by electoral cycles. The case studies provide insight into the distinct involvement of Russia and China (respectively in Armenia and Cameroon) in specific elections, highlighting covert influence strategies during election periods and suggesting a need for vigilant policymaking.

Policy Brief

Policy Report

This research memo reports assess the relationship between Resurgent Authoritarianism Influence (RAI) and changes in civic space. We present evidence that for some countries, increases in RAI activity are associated with near-term changes in civic space. In doing so, we provide one of the first tests of a claim driving high-level decision-making in foreign policy and international advocacy. We find that increases in RAI are more often associated with increasing restrictions on civic space, although increases in RAI are also predictive of decreasing restrictions in some cases. Conversely, civic space events are not predictive of RAI events. Together, these findings provide evidence that influence from Russia and China are not so much responding to civic space dynamics in target countries as they are trying to shape it.

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This research memo reviews the academic and policy literatures on Resurgent Authoritarian Influence (RAI), discusses existing data, and describes the MLP RAI data. We group our 22 RAI events into 5 conceptual categories and summarize the prevalence of reporting on these categories across countries and over time using visualizations, descriptive statistics, and dimensionality reduction. Thhis analysis suggest that RAI activity has been surprisingly consistent over the last ten years, that Russia and China utilize regionally-specific approaches to exerting influence, and that Russia and China deployed similar strategies when dealing with strategically important countries with which they have a strained or hostile relationship.

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