Question: What can be accounted for by breaking the actor codes down further to examine role codes?
Evidence: Along the actor dimension, Figure 6 shows which full actor codings appear most often in the dataset. As one would expect, state actors account for most of the events, with the only outlier the IMGMOSISI which is the actor code for the Islamic State in Iraq and the Levant. This pattern also holds for just the entity codings, which could be either a state code or a few other important codings such as IGOs.
It is possible to break the actor codes down further to examine role codes, which account for more specific functions that a specific actor performs within a given country such as military or business. Figure 8 shows that the most common role code is government actors (GOV). Following the GOV role are military (MIL) and rebel (REB) codes.
In order to better understand how the dataset is performing it is helpful to pull out a specific case and examine a similar set of attributes as seen in the previous section. One of the major, ongoing events in the international arena during the time currently covered by the Phoenix dataset is the conflict in Syria. Given this, I extract any events that contain the Syria country code, SYR, as the SourceActorEntity or TargetActorEntity. Figure 9 shows the plot of the daily aggregated event counts. In this plot it is possible to see actions such as the beginning of United State intervention against ISIL, along with other significant events within the country. As with any event data, it is important to note that the event counts shown do not represent the on-the-ground truth of events in Syria, but instead reflect the media coverage of said events. Thus, some of the peaks and troughs are the result of media coverage instead of any actual shift in reality. In order to provide more context to the time series, Figure 10 shows the breakout of the QuadClass variable for this data subset. The dominant event types are the low-level events described in the previous section, but the “Material Conflict” class is higher than in the broader dataset. This is, of course, as expected given the ongoing conflict within Syria.
Figures 11 and 12 show the top actor and entity codes for the Syria subset. Various Syrian actors appear most often, with other Middle East countries also accounting for a fairly high portion of events. Also seen within this group of top actors is ISIL and the United States. Additionally, Russia appears high in the rankings of actors within Syria, capturing the recent activity by Russian forces in support of the Assad regime.
Overall, the Syrian subset of the data suggests that the Phoenix dataset is doing an adequate job of picking up events of interest in a conflict zone. The actor and event distributions follow what one would expect for the Syrian conflict. Additionally, there are no obvious errors in which actors make up the top participants in the subset. This examination provides confidence that the dataset is “working” in terms of face validity.
It is possible to break the actor codes down further to examine role codes, which account for more specific functions that a specific actor performs within a given country such as military or business.
Answer: More specific functions that a specific actor performs within a given country such as military or business.