Our project stands out in three ways. First, we dramatically accelerate the provision of high-quality data to practitioners and policymakers who need to take timely, evidence-based action to counter attacks on human rights and democracy. Second, we collect data from local sources of news and information in more than 20 languages. Past efforts collected data only from highly structured, international sources. We use machine learning translation and a data science team to monitor small, local news sources that require highly adaptive scraping methods but provide unprecedented access to local knowledge. We also conduct a quarterly survey of activists in 19 countries to balance our results with local voices. Third, we go beyond merely tracking important historical events by using big data to produce accessible forecasts about future events that can be used by organizations focused on human rights and democracy-promotion to anticipate and plan for changing conditions.

MLP is currently producing quarterly updates to our data and forecasts for 40 countries and making this information publicly available on interactive data dashboards hosted on our project website. These dashboards allow consumers to interact with historical data and compare trends in the occurrence of 42 events across countries and over time. The dashboards also allow consumers to see our forecasts for 15 unique events and inspect the country-specific factors that increase or decrease the probability of changes in event activity. We are actively promoting the use of this information by hosting seminars, tutorials, and public events targeting policymakers and civil society groups. In addition to promoting the existing data products, we are constantly developing new tools and research products and improving the accuracy of our forecasting models. We plan to expand these efforts to include 12 new countries by over the next 12 months.

The MLP project was developed within the INSPIRES Consortium, a partnership between USAID, academic researchers, and international NGOs with deep ties to local civil society around the world. This creates a feedback loop, allowing policymakers and democracy practitioners to inform the development of research products, research products to inform the strategic decisions around policies and programs, and partner NGOs to disseminate research products to organizations on the ground.

Since 2021, our data and forecasts have been one piece of information used by USAID and INSPIRES Consortium partners to make decisions about the deployment of resources and design of programs. Since January 2022, this information has been publicly available on our website. In our last quarterly survey of activists in 19 countries, more than 80% of respondents indicated that they are likely to this information to inform programming and communicate with stakeholders.

Over the past two years, MLP has rapidly scaled our efforts across 40 countries, with funding secured to expand to another 12 countries over the next 12 months. We have also provided advanced warning of several major political events, including major protests in Serbia, mass arrests in El Salvador, and political use of defamation cases in Tunisia. While the impact of these tools on decision-making is hard to quantify, in our last quarterly survey of 70 activists living in 19 countries, more than 80% of respondents indicated that they were likely use MLP forecasts to inform programming decisions and communicate with stakeholders about the future. Our algorithms are able to classify news articles according to the events being reported on with average accuracies above 80% across our 42 categories. Using a measure that summarizes overall civic space activity for each country, our forecasts are currently able to explain an average of more than 70% of monthly variation across countries.

Building the data collection and processing infrastructure necessary to scrape, store, and process vast quantities of text from dozens of online news sources in more than 20 languages presented numerous challenges and was interrupted by number failures. Overcoming these obstacles has required the procurement and maintenance of advanced computation systems, the recruitment of full-time data scientists, and the close involvement of university data security and network specialists. Furthermore, communicating the insights of quantitative forecasts of future conditions and events to non-technical audiences took years of iteration. Producing accessible visualizations required multiple workshops and planning sessions testing different means of summarizing large amounts of information into easily digestible, concise visual representations. Finally, some events that we track have proven too unpredictable to forecast accurately.

For MLP’s efforts to be successful, countries must have a sufficiently developed media environment with multiple sources of online news with several years of accessible archives. While most countries in the world meet these criteria, extremely small countries or countries with protracted conflict may not be viable places to implement MLP activities. Furthermore, this project requires close collaboration between researchers with advanced technical skills and policymakers and practitioners with a desire to translate quantitative data into actionable insights.

Our data production pipeline presents innumerable opportunities for adaptation to new problems. We produce monthly data and forecast 6-months into the future. However, the underlying data can track events at a weekly or even daily level, and new forecasting models could make predictions at different frequencies (ex. weekly) or predict specific changes in events or combinations of events (ex. sudden increases in violence or increased use of legal attacks on opponents). More importantly, our corpus of 70+ million news articles and data processing infrastructure is already being repurposed to track new events of phenomenon, such as polarization or misinformation, within our existing system. Furthermore, these articles contain additional information, such as the specific location where events occur (ex. cities) that can be used to produce even more fine-grained data. We will partner with new organizations and agencies to provide data that helps address their specific missions.

Incorporating local voices and knowledge into big data has been much more challenging than we anticipated. While scraping data and information from highly structured, international, English-language sources of online news is relatively simple, expanding these efforts to include domestic news sources requires constant adaptation and advanced technical skills. However, our research demonstrates that these local sources provide vastly more information about their respective countries than is reported-on by international sources. Furthermore, without the novel information provided by local sources, forecasting models are unable to make accurate predictions about the future.