2.1. Fatalities data

We rely on the UCDP Candidate Events Dataset that makes available monthly releases of violence data with not more than a month’s lag globally (Sundberg and Melander, Reference Sundberg and Melander2013; Hegre et al., Reference Hegre, Croicu, Eck and Högbladh2020; Davies et al., Reference Davies, Pettersson and Öberg2023). We are interested in deaths at the country/month level or grid cell/month level resulting from armed force used by an organized actor against another organized actor or against civilians. We include state-based conflict, non-state conflict, and one-sided violence. Since the dataset does not code zeros, we allocate a zero to any unit in which GED data is available, and the country is independent. Importantly, this means we are predicting political violence and escalations into internal armed conflict. We do not predict external wars like invasions of one country by another.

With respect to the grid cell forecasts, uncertainties arise with respect to the exact location of events. These are directly coded in the UCDP data as “where_prec.” The lowest precision for “where_prec” is “only the country where the event took place is known.” But at the grid cell level, we only include events that have been coded with geographical precision up to the ADM2 level (i.e., an individual grid cell). We also retain events that have been coded as taking place in international waters and air space.Footnote ³

2.2. Text data

Our text data are comprised of over 6 million documents from 1989 to present. These are downloaded from Factiva and are sourced from two newspapers (347,874 articles from the New York Times and 142,813 from the Economist) and three news aggregators (968,898 articles from the Associated Press, 3,588,489 from the BBC Monitor, and 39,232 from LatinNews).Footnote ⁴ Text is downloaded according to rules set in an extensive query. As a generalization, a document is downloaded if a country or capital name appears in the title or lead paragraph. One limitation relates to the inherent bias of news data, particularly in political regimes where the media is censored or restricted. However, Mueller and Rauh (Reference Mueller and Rauh2022b) show that this bias results in no obvious failure of the model when predicting hard onsets. The inclusion of LatinNews as a source is specifically intended to improve the text signal for Latin America since BBC Monitor generally focuses on Asia and Africa.

Standard natural language preprocessing (NLP) techniques are used, including the removal of punctuation, stop words, and lemmatization. In addition to single words (unigrams), we also consider common combinations of two or three words (bigrams and trigrams). Any token (unigram, bigram, or trigram) that appears in at least half of the documents (too frequent) or in fewer than 200 documents (too infrequent) is also removed.

This results in a corpus whereby documents are assigned to the country/month level. Hence, we have a set of documents that represent the news landscape for every country for every month between January 1989 and present. For the grid-cell level predictions, we additionally use prepositions to detect locations mentioned in news articles. The challenge is to condense this text data into a set of features that improve the forecast.

LDA, developed by Blei et al. (Reference Blei, Ng and Jordan2003), is a probabilistic model used for topic modeling and document clustering in natural language processing. LDA assumes that documents are mixtures of topics, and topics are mixtures of words. It aims to uncover the latent topics within a corpus by iteratively assigning words in documents to topics and estimating the distribution of topics in documents and words in topics. This modeling technique allows LDA to extract meaningful topics from a collection of text data, making it a valuable tool for tasks such as document categorization, content recommendation, and understanding the thematic structure of large text datasets.

For the implementation, we rely on the Python package from Řehřek and Sojka (Reference Řehřek and Sojka2010) of the dynamic Latent Dirichlet allocation (LDA) topic model (Hoffman et al., Reference Hoffman, Bach and Blei2010) estimated with 15 topics. This enables the reduction of the dimensionality of the text data without using priors on which elements of the text will be most useful for forecasting conflict. The dynamic LDA can be summarized as follows:

1. 1. Topics: Topics are distributions over words. Each topic assigns different probabilities to different words in the full set of tokens. For example, a topic on “economics” might assign high probabilities to words such as “economy,” “inflation,” “investment,” and so forth We estimate topics by first training the model on all text data up until 2010m1. We allow the a priori weight variational hyperparameters for each document to be inferred by the algorithm, and $ \alpha $ , the a priori belief for each topics’ probability is set to the default of (1/N), where $ N $ is the number of topics. The estimated topics and top keywords are discussed in more detail in Section 4.3 and Table 6.

2. 2. Document-topic distribution: This is a matrix of size $ D\times N $ , where $ D $ is the number of documents and $ N $ is the number of topics. Each row sums to 1, that is, a document is represented by the relative proportion of various topics in that document.

3. 3. Country-topic shares: Hence, for each country at each time step, we can compute the proportion of the news landscape that is assigned to a given topic by averaging over the document-topic distribution assigned to that country/month. This process is dynamic because we reinterpret the topic distribution of previous months as new documents become available each month.

Note that the purpose of the text analysis here is to generate a broad, all-encompassing dimensionality reduction of the entire news text. This is because the project does not impose what kind of news content or events will be indicative of conflict risk. This is a significant difference from the approach taken in the Political Instability Task Force (PITF), for example, which relies on event coders to produce large databases like the ICEWS database from Boschee et al. (Reference Boschee, Lautenschlager, O’Brien, Shellman, Starz and Ward2015) or, more recently, the Polecat global event dataset (Halterman et al., Reference Halterman, Schrodt, Beger, Bagozzi and Scarborough2023). We expect the NLP approach here to work better if the factors that predict conflict, especially those that are negatively associated with conflict risk, are hard to foresee.

2.3. Prediction method

For the conflict prediction task, we rely on a Random Forest (Breiman, Reference Breiman2001) and implement a rolling forecast methodology. Random Forest is a robust and widely used ensemble machine learning algorithm that excels in predictive modeling tasks. It operates by constructing multiple decision trees during training, each based on a random subset of the data and features. These individual trees are then combined to make predictions. Random Forest offers several advantages, such as handling high-dimensional data and nonlinear relationships, reducing overfitting, and providing feature importance rankings. This algorithm’s strength lies in its ability to capture complex interactions within the data, making it a valuable tool for both regression and classification problems. In a rolling forecast, the forecast horizon typically remains fixed (in our case, 3 and 12 months into the future), but the forecast is updated at monthly intervals. As each period passes, we add the most recent actual data and update the forecast for the next prediction horizon.

Our datasets underlying the predictions are set up at the geographic unit/month level. At the national level, we generate forecasts for three different target variables across two different time horizons. We then distinguish between models that rely only on text features (text model) or a combination of historical violence and text features (best model). The text model only relies on the topic shares, and therefore “knows” much less about the situation a country is in. As a consequence, it reacts much stronger to news as this is the only source of its information. On the contrary, the best model is well informed about the history of violence in a country, but therefore, it will put much less weight, and hence react much less, to changes in news topics. This trade-off will be discussed further in Section 3, in which we evaluate the predictive performance of the models.

Table 1 defines the target variables and interpretation of the forecasting outputs. We define armed conflict as 0.5 deaths per 1 million inhabitants in any given month. Tables 2 and 3 describe the features used in the respective models. Note that the text features across all models and target variables are the same. However, the historical conflict features are the same for any violence and armed conflict predictions, but differ in the violence intensity case. Finally, population data is sourced from the World Bank. Since our unit of analysis is the country/month level, but the data is available annually, we assume that the population of a country is the same for any month in a given year.Footnote ⁵ In the case of missing data, we forward fill using the latest available data.

Table 1. National forecast target variables

^a We first compute $ z={\sum}_{i=1}^w\frac{x_i\times 1000}{y_i} $ , where w is the forecast horizon, while $ {x}_i $ and $ {y}_i $ represent the number of fatalities and population in month 𝑖, respectively. Hence, z represents the sum of fatalities per 1000 inhabitants over the next w months. We then have that $ a=\frac{z}{w} $ , that is, the average fatalities per 1000 inhabitants over the next w months. The log transformation is then conducted as ln(𝑎 + 1).

^b Strictly speaking, the model outputs the log-transformed average number of fatalities per capita. To convert back to number of fatalities, the prediction is transformed as best $ =\left({e}^x-1\right)\times \frac{y}{1000} $ , where x is the predicted log transformed average number of fatalities per capita and y is population.

Table 2. National violence and armed conflict forecast features

^a The number of tokens represents the flow of unigrams, bigrams, and trigrams mentioned in the documents for each country-month. To smooth out changes over time, instead of using the flow, we use a token stock ( $ {W}_t $ ), which consists of the present value of the flow of tokens. Let us define $ {w}_t $ as the number of tokens (unigrams, bigrams, trigrams) in all documents of a specific country at month t. For a decay rate of $ \delta =0.8 $ , the token stock for a specific month T is

$$ {W}_{t=T}={\sum}_{t=1}^T{\delta}^{T-t}{w}_t. $$

^b Similarly, the share of topics in the news of each month can be seen as a flow, which we transform into a stock ( $ {X}_{k,t} $ ) to reduce its variability. In this case, to account for the fact that months with a higher volume of news should carry more weight when updating the stock, we weight the flow by the number of tokens in each month. For a specific country and month t, let us define $ {x}_{k,t} $ as the share of topic k, $ {w}_t $ as the token count, and $ {W}_t $ as the stock of total token count. For a decay rate of $ \delta =0.8 $ , the stock of the share of topic k for a specific month T is:

$$ {X}_{k,t=T}=\frac{\sum_{t=1}^T{\delta}^{T-t}{w}_t{x}_{k,t}}{W_T} $$

Table 3. National violence intensity forecast features

^a See note a of Table 2.

^b See note b of Table 2.

^c Log transformation is conducted as ln( $ z+1 $ ), where $ z=\frac{x\ast 1000}{y} $ , such that x is the number of fatalities and y is population. In other words, z represents fatalities per 1000 inhabitants.

The purpose of the rolling forecast is to replicate the information set that would be available to a decision maker. In other words, they would observe features until period $ T $ and make a forecast for the aggregate window $ T<t\le T+W $ , where $ W $ is equal to 3 or 12. In other words, when the window is 3 months, then we are predicting, for instance, the likelihood of any battle death over the entirety of the 3 months. We do not predict outcomes for each of the 3 months separately. Similarly, when predicting the 12 month window, we consider the aggregate outcome over the next 12 months, and not for each of the next 12 months separately. For example, an armed conflict forecast in 2015 m1 for a window of 12 months is predicting the likelihood of an armed conflict outbreak in any of the following 12 months. The same applies to the violence intensity forecast—for a forecasting window of 12 months, we are predicting the average number of fatalities per capita per month over the next 12 months. We train a model to learn a functional form using all data from 1989m1 to 2009m12 as follows:

$$ {y}_{i,T<t\le T+W}={F}_T\left({\mathbf{X}}_{i,T}\right), $$

With the resulting model, we then produce out-of-sample predictions on a rolling basis from 2010m1 onwards:

$$ {\hat{y}}_{i,T<t\le T+W}={F}_T\left({\mathbf{X}}_{i,T}\right), $$

For any violence and armed conflict, hyperparameters are chosen by maximizing the area under the curve (AUC) of the receiver operating characteristics (ROCs) curve via pseudo-out-of-sample rolling forecasting on the sample 2010 to 2015. In the case of violence intensity, we seek to minimize the mean squared error (MSE). To solidify the rolling forecast methodology, we provide an example of the algorithm employed where the full data sample ranges from January 1989 to August 2023. This can obviously be modified by updating Aug 2023 to the latest available date for which data is available:

In this way, we are able to generate a full history of predictions via pseudo-out-of-sample forecasts. For example, imagine rewinding back to 2015m1. A policymaker would only have access to data up until 2014m12 to inform a forecast for 2015m1 to 2015m3 (for a time horizon of 3 months ahead). This is exactly the process we seek to simulate via the rolling forecast methodology. This enables a realistic evaluation of what is possible in terms of forecasting power in actual applications, as no data that has been used for training purposes is included in the test set (Mueller and Rauh, Reference Mueller and Rauh2022b).

3.1. National

3.1.1. Any violence and armed conflict

To evaluate performance, we compare realizations $ {y}_{i,T<t\le T+W} $ for all $ t\in 2010m1,.\dots, last\_ month $ with the predicted values $ {\hat{y}}_{i,T<t\le T+W} $ .Footnote ⁶ For any violence and armed conflict, we present AUC-ROC and precision–recall curves. The receiver operating characteristic–area under the curve (ROC-AUC) and precision–recall curves serve as essential evaluation metrics for binary classification models. The ROC-AUC curve offers a graphical representation of a classifier’s ability to discriminate between positive and negative classes across various threshold values, while its associated AUC quantifies the overall discriminative power of the model. The x-axis of the ROC-AUC curve represents the false positive rate (FPR). The y-axis represents the True Positive Rate (TPR), also known as sensitivity or recall.

Conversely, the precision–recall curve provides insights into the model’s trade-off between precision and recall, making it particularly valuable for scenarios involving imbalanced datasets or instances where false positives are of concern. In the precision–recall curve, the x-axis represents recall, which is the same as the TPR on the ROC curve. Precision is the ratio of true positives to the sum of true positives and false positives. In both cases, the curves display how the model’s performance changes as the classification threshold is varied.

When evaluating performance, it is important to distinguish the two forecast horizons (3 and 12 months) as well as the difference between the armed conflict model that features less onsets (higher imbalance) and any violence that features more onsets. We also distinguish performance on hard onsets (after more than 60 months of peace) and all onsets. An important element of evaluations is that they can only be run on outcomes that are realized. When evaluating an onset model forecasting up to 12 months ahead in August 2023, one can only evaluate the model until August 2022 as the onsets in the 12 months following September 2022 are not realized yet in August 2023. The 95% confidence intervals of the ROC-AUCs run on the data from 2010m1 to 2022m8 are presented in Figure 2. The confidence intervals are generated via bootstrapping.Footnote ⁷

Figure 2. ROC-AUC 95% confidence intervals.

One striking feature of the forecast model is that performance holds up extremely well over time—despite the fact that the period 2010 to 2023 is characterized by dramatic shifts in geopolitical dynamics, types of conflicts, and the news landscape. Figure 3 shows performance of the armed conflict model over time by showing AUCs by year.Footnote ⁸ As before, the best model performs better than the text model. However, neither model shows dramatic fluctuations despite the dramatic aforementioned changes. While not significant, there is even a tendency of improvement visible in the best model.

Figure 3. ROC-AUCs by year for armed conflict, 12 months ahead.

Figure 3 shows the performance of the armed conflict model, focusing on hard onsets of conflict after 60 months of peace. The AUCs shown are for 2-year periods. The best model now sometimes performs worse than the text model. However, both models show little performance trends over time and do not show a dramatic variation over time in general. This is despite the fact that hard onsets are much rarer and much more specific to the international landscape. The period we look at does, for example, include the Arab Spring period, which was characterized by instability in several countries that were previously stable for long periods.

One important takeaway from these figures is that the COVID-19 period with its dramatic change in reporting patterns did not consistently damage the performance of the models. The trend in the best model is overall positive over time, and there is no clear pattern in the text model. This suggests that over time, performance should stay constant or even improve.

However, as discussed above, the AUC still does not tell the full truth about performance as imbalance does not have an effect on this performance measure. Policymakers should care at least as much about precision. This is also where the issue of increasing imbalance in hard onsets appears most clearly. In Figure 4, we show the precision–recall curves for the armed conflict 12 months ahead forecast. Figure 4a shows the pseudo-out-of-sample performance for all onsets. Precision in the best model remains above 80% for a recall rate of over 50%. The text model performs much worse as violence history is a very important driver of risk. However, it is important to keep in mind that armed conflict onsets are rare events. Even for the 12 months ahead forecast, the precision eventually falls far below 20% for all onsets. It is a significant feat to bring overall precision to over 80% for low recall rates.

Figure 4. Precision recall curve for armed conflict forecast, 12 months ahead.

Figure 4b shows the pseudo-out-of-sample performance for the same model but only for onsets that happened after 60 months of peace. Precision in the best model is now significantly lower and is around 20% for a recall rate of 20%. However, the text model now performs relatively much better. For very low recall rates, the text model can even beat the best model. The imbalance problem is now extreme with a baseline likelihood of only a few percentage points when forecasting armed conflict 12 months ahead.

For onsets in countries with even longer histories of peace, the relative performance changes further. The best model relies heavily on information about past violence, since due to the conflict trap, past violence is an excellent predictor of future violence. Therefore, this type of model will not detect risk emerging in previously peaceful countries. In contrast, the text model reacts strongly to news and might thereby generate spikes in risk even if a country has experienced a sustained period of peace. On average, it is clear that the text model will perform worse as it is missing important information. Nonetheless, it can provide valuable complementary information to raise red flags in unsuspected settings. In summary, if average performance is a user’s main criterion, then the best model is the indicator of choice. However, if the user wants to potentially spot black swan events, then he/she should consider the text model as well.

3.1.2. Violence intensity

For violence intensity, we rely on the mean squared error (MSE) as our metric for evaluation and focus on the performance of the 3 months ahead forecast. The MSE is computed as $ {\left(\ln \left(y+1\right)-\ln \left(\hat{y}+1\right)\right)}^2 $ , where $ y $ is the observed average fatalities per capita over the next 3 months and $ \hat{y} $ is the predicted average fatalities per capita over the next 3 months.Footnote ⁹ The same principles for evaluation apply—for a 3 months ahead violence intensity forecast in August 2023, one can only evaluate the model until May 2023 as the violence in the 3 months from June 2023 onwards are not realized yet in August 2023.

First we must define a benchmark model. In this case, we define a “no-change” model as our benchmark—average fatalities per capita for the previous 3 months are used as the prediction for the average fatalities per capita for the next 3 months. Across the full sample, our best model outperforms the no-change model. Next, we undertake a closer inspection of performance by grouping fatalities per capita into bins. Figure 5 shows that the distribution is heavily right skewed—in other words, most observations in our sample are country/months without any battle-related deaths. Hence, we define our bins according to the distribution of average fatalities per capita, where they exceed 0. In total, we have four bins, which align with the 25th, 50th, and 75th percentile as per the distribution shown in Figure 5b.

Figure 5. Distribution of average fatalities per capita.

Table 4 shows the average MSE for the best model and no-change model for the observations where the true average fatalities per capita in the next 3 months fall into the respective bins. The normalized column represents the best model divided by the no-change model—this gives an indication of how well our model performs relative to the benchmark.

Table 4. Average MSE by bin

We see that for very low-intensity violence, our model performs worse than the no-change model. This is a product of our intentions to capture escalations, which leads to “over-predicting” in situations where 0 fatalities occur in the future. We also see that, on average, the error is almost approximately half of the no-change model for medium-intensity violence (bins 0.15–0.5 and 0.5–1.5), while we observe marginally better performance in cases of extreme violence.

So far, our intensity forecasting model has been less geared towards predicting the exact number of fatalities, but rather is meant to capture conflict dynamics. These dynamics, by definition, can never be predicted by a no-change model. We highlight the performance of our forecast in this context by analyzing its ability to capture escalations/de-escalations. We first define escalations/de-escalations in accordance with the bins outlined above. For example, if a country has experienced average fatalities per 1mn inhabitants in the range 0.5 to 1.5 for the previous 3 months, then we code an escalation if violence exceeds 1.5 fatalities per 1 mn inhabitants on average in the next 3 months. The reverse logic applies to de-escalations. We then observe whether the forecast prediction tracks the realized escalation/de-escalation by comparing the bin of the prediction for the next 3 months with the realizations of the past 3 months.

Across our sample there are a total of 1345 escalations (5.6%), 1201 de-escalations (5.0%) and 21,344 instances (89.3%) of no escalation. Figure 6 presents confusion matrices for the 3 ahead violence intensity best and text models. In the no-escalation cases, the best model significantly outperforms the text model, correctly classifying 87% of all cases compared to 52%. Interestingly, the text model better captures escalations, it classifies 82% of cases correctly compared to 71% for the best model, and de-escalations whereby it correctly classifies 19% compared to 7% for the best model. This suggests that our text features are able to identify signals of future changes in violence intensity levels that are not characterized by recent histories of violence. These results also highlight the value of the intensity model for policymakers seeking a data-driven perspective on the future dynamics of conflict in already violent countries. Since the any violence and armed conflict forecasts are likely to already be elevated for these situations, we encourage users of our data to look towards our intensity model to gain insight into whether further escalations might occur.

Figure 6. Violence intensity performance, escalations, and de-escalations 3 months ahead.

3.2. Subnational

Figure 7 shows the ROC and precision–recall curve for the grid cell predictions published at conflictforecast.org. We began publishing the subnational predictions on the website in March 2022 and then had some interruptions, while we were restructuring the data pipeline. Since the predictions are for the next 12 months, we, therefore, are only evaluating the 3 months that we predicted out-of-sample. The ROC-AUC is very high, with 0.97, and the precision–recall curve indicates that if we want to capture half of all conflicts, our list of grid cells would contain three-quarter of cases that were correctly classified as conflict.

Figure 7. ROC curve and precision–recall curve at subnational level.

4.1. National level forecasts

Table 5 provides a description of the variables in the datasets we make freely available at conflictforecast.org. Note that these are available to download as separate data files according to the target variable and time horizon. We publish our forecasting outputs from the text and best (i.e., combination of text and historical conflict features) models. The text model prediction tends to be updated during the first week of a month and the best model after the 20th day of a given month. This delay is due to the fact that the best model relies on updated input from the UCDP data on fatalities, which tend to be published on the 20th of a given month.

Table 5. National and subnational forecast dataset.

^a The log transformation is conducted as ln(x + 1), where x is equal to the number of fatalities divided by population.

^b See note a of Table 1.

The website provides a graphic illustration of current conflict risk levels across the globe in terms of a map. Figure 8 shows the risk of an outbreak of violence in the next 12 months across the globe, estimated with all the information available at the end of August 2023. The datasets that can be downloaded contain the information underlying the map as well as all past predictions.

Figure 8. National conflict risk across the globe at the end of August 2023.

4.2. Subnational level forecasts

The subnational-level forecasts are still in their beta version and are not derived from published academic research. In their current form, the prediction models are an extension of the national predictions and were developed as part of a project for the Foreign, Commonwealth, and Development Office of the UK (Mueller et al., Reference Mueller, Rauh and Ruggieri2022).

In terms of target variables, we only provide the likelihood of a battle death in a grid cell. In terms of prediction windows, we only provide a forecast for the next 12 months. In other words, we predict how likely it is that a given grid cell will experience any battle death within the next 12 months. We train six separate random forest classification models depending on whether there currently is conflict in a grid cell, whether a neighboring grid cell is experiencing conflict, and whether the grid cell has experienced any battle death in the past 5 years. More specifically, the following six models are trained on:

1. 1. Regions that had no battle deaths in the last 5 years and have no battle deaths in their immediate neighboring cells.

2. 2. Regions that had at least one battle death in the last 5 years but no ongoing violence and have no battle deaths in their immediate neighboring cells.

3. 3. Regions that had no battle deaths in the last 5 years and have battle deaths in their immediate neighboring cells.

4. 4. Regions that had at least one battle death in the last 5 years but no ongoing violence and have battle deaths in their immediate neighboring cells.

5. 5. Regions experiencing ongoing violence and no battle deaths in their immediate neighboring cells.

6. 6. Regions experiencing ongoing violence and battle deaths in their immediate neighboring cells.

The reasoning for this segmentation is twofold. First, we can derive predictions based on the conditional distribution of grid cells in a similar situation, and second, we can tailor the predictors to the current situation. For instance, for all regions without violence, the number of months of ongoing violence will be zero, and, therefore, this predictor need not be included for grid cells not experiencing violence. Again, the hyperparameters are chosen through threefold cross-validation on the sample until the year 2010. The resulting depth of trees varies between 3 and 5 nodes and the number of trees is either 300 or 400. In terms of grid-cell level predictors, we include

• • the discounted past deaths,

• • time since the last battle death,

• • current battle deaths,

• • discounted and current neighboring battle deaths,

• • local news topics,

• • neighboring news topics,

• • distance to capital,

• • population,

• • discounted and current number of riots and protests obtained from ACLED, the Armed Conflict Location & Event Data Project (Raleigh et al., Reference Raleigh, Linke, Hegre and Karlsen2010, Reference Raleigh, Linke, Hegre, Karlsen, Kishi and Linke2023),

• • time since the last riot and protest,

• • consecutive months of battle deaths.

From the country level, we include

• • discounted and current battle deaths and

• • the time since the last battle death.

The downloadable dataset is summarized in Table 5. Besides providing the predicted risk of experiencing a battle death within the next 12 months, we also provide geographic identifiers for a given grid cell. This includes the PRIO id, the country the grid cell is (mostly) located, and the longitude and latitude of the center of the grid cell. Figure 9 shows a snapshot of risk at the grid-cell level from the website, which displays the risk data from the downloadable dataset.

Figure 9. Subnational conflict risk across the globe at the end of August 2023.

4.3. National news-topic shares

Using relatively few topics helps to prevent topics from adapting to specific events, conflicts, regions or countries that dominate the news landscape. We manually label the topics for the sake of illustration, but these labels do not influence the predictions. Table 6 below lists the manual labels for our 15 topics and the top 10 keywords. Actually, topics are probability distributions across the entire dictionary of words in the corpus.

Table 6. Top 10 keywords of the 15 topics and suggested labels

The algorithm provides the topic shares for each individual article. We aggregate these at the country/month level and make them available in the risk datasets downloaded from conflictforecast.org. Topic shares are provided for all 15 topics for each country and the period 2010m1 to the latest update. The topic model changes every month but each update produces a consistent interpretation of the entire news landscape for all countries and for the entire sample period. In Figure 10, we show an example of how the topic relating to politics evolved in the USA and the UK over time. The topic share exhibits clear spikes around political events such as elections of the Brexit referendum. While elections worldwide could be coded into a dataset, topics are able to pick up more subtle movements and events across the entire world.

Figure 10. Politics topics in the USA and the UK over time.

Figure 11 provides a global snapshot of the military topic share for August 2023. Unsurprisingly, reporting about themes related to the military features heavily in Russia and Ukraine. Besides being available in the downloadable datasets on the website, further information about the topics can be obtained by clicking on countries. Here, we provide information about the current distribution of topics within a country and whether these topics tend to, on average, be negatively or positively related to conflict risk.

Figure 11. Military topic share in August 2023.

As described in Table 5, the topics are attached to the downloadable risk forecast datasets rather than as separate datasets. We have not yet provided local news information, given that the grid-cell level data and predictions are at an earlier development stage and will be subject to change.