Looking at this time-lapse visualization of protests around the world since 1979, one can easily surmise that the spirit of protest exploded during the 1990s, steadily increasing until it seemed to overtake entire continents post-2010. But is the world really overrun with dissent and revolution?

Only sort of. The data driving the visualization is taken from the Global Database of Events, Language, and Tone (GDELT), which has drawn on media reporting to georeference nearly a quarter-million events over the past 34 years. And while there has been an increase in media coverage of every type of event since 1979, the number of protests as a percentage of the total number of “events” reported has not increased.

But that doesn’t mean there aren’t spikes in the data, as one of the project’s lead researchers, John Beieler, wrote in an email. “There are times when the amount of protests behavior goes up and is higher than average. The past few years is actually one of those times.”

A write-up about this map in Foreign Policy discussed some of these spikes: the protests in South Africa against apartheid, the Arab Spring, the fall of the Berlin Wall, Poland in the 1980s. If you know where to look in the visualization, you can watch them unfold.

But, with data comes problems. Commenters on the Foreign Policy post quickly pointed out what they saw were the map’s limitations. “Maybe I’m being nit-picky, but I’m just looking at Latin America and I can tell you this is severely underestimating the number of protests, both now and prior to the third wave of democratization,” wrote one user. “Clearly, wrong!! In México we have an permanente[sic] wave of protetest [sic] since 1950 and in the map only appears since 1994,” wrote another.

All the underlying data for this map are derived from text-mining a cross-section of all major international, national, regional, local, and hyper-local news sources; no human hand is involved.”The GDELT dataset is generated completely by fully automatic software algorithms operating with no human oversight or intervention and is based on global news media reporting,” reads GDELT’s Web site.

In general, all data can contain bias, which can lead to a flawed representation of events. And while visualizations can make complex information look simple, they must be coupled with a deeper understanding of the political and social complexities, as well as an awareness of the shortcomings in the data itself. Without this expertise and analysis, the possibility of misinterpretation is high.

The work of GDELT goes beyond the realm of visualization. Its initiative— “to construct a catalog of human societal-scale behavior and beliefs across all countries of the world over the last two centuries”—has huge implications for machine-learning predictive applications, which are thought to be the driver of the next wave of innovation. One climate scientist said recently that social science has not yet caught up with the physical sciences in terms of predictive modeling. But predicting outbreaks of violence using big data may not be that far off.

And data maps like this can leave a much more lasting impression than spreadsheet numbers. As one FP commenter wrote, “Huge challenges for Big Data, indeed. But at least I see an occasional dot over Uganda…”