Although #NoDAPL is a hyperlocal issue, it has received attention across the country. Briefly looking at the locations stated by Twitter users (in their bios), they seem to be geographically diverse. Given that the Dakota Access Pipeline is a domestic issue, I expect to see tweets mostly from the northwest hemisphere, specifically the United States. For tweets actually using geolocation, I would expect to see many from Standing Rock, from users actually at the site. Recently, a Facebook campaign to stymie law enforcement officials had users checking in at Standing Rock remotely, which created a glut of checkins in the area, making it difficult to determine exactly how many people were actually present in North Dakota. Given that Twitter uses actual location data, I do not expect to see a similar phenomenon on the site.
My hometown, in Connecticut, is roughly at 40ºN 90ºW, relatively far from the epicenter of the protest. Given that Glastonbury is a small, suburban-rural community without major political activism, I doubt there will be many tweets regarding #NoDAPL in the area.
I found 22 mappable tweets out of the 27,556 I scraped, meaning 0.079% of the tweets collected had a geolocation. As far as I can tell, all of the tweets are in the northwest hemisphere, most likely in the United States. Given that this is primarily a US issue, this is unsurprising. All of the tweets are in English, which is also consistent with my expectations.
Given that law enforcement is known for using location data to track protestors, this may be a disincentive for people to share their geolocation. Standing Rock, the epicenter of the protest, is also remote, meaning that access to the internet and penetration of access to the internet may be limited.
The small sample size leaves a high degree of uncertainty in any conclusions made from the data. Yau states that any assumption based on a small or incomplete data set runs the risk of misrepresenting the case. Especially considering the potential negative consequences of using a geolocation for those already at risk, I doubt the sample I have obtained will fully represent the actors involved.
I don’t believe that my dataset is ‘big data.’ Using boyd and Crawford’s categorizations of “fire hose,” “garden hose,” and “spritzer,” my mappable data falls well below even the lowest bar, 1% of the available data. My data was also collected over a short period of time—14 days out of the roughly 2.5 years for which the Dakota Access Pipeline has been publicly known. boyd and Crawford do not set a hard limit for the size of big data, saying instead it is about the “capacity to search, aggregate, and cross-reference large data sets.” While my data has been aggregated and is searchable, there are no complementary data sets that can be used in conjunction. Therefore, it is difficult to argue that my data is ‘big’ either in scale or interconnectedness.
Jordan’s #BlackLivesMatter hashtag had roughly 85,000 tweets over the same period of time, just over triple the number. #BlackLivesMatter seems to directly relate to a much larger population than #NoDAPL, meaning there is a higher likelihood for it to be recognized and talked about. The digital presence of both movements is important to consider as well. While the #BlackLivesMatter movement has taken the form of physical protests, it lacks a physical location much of its activity is conducted online. Conversely, the #NoDAPL protest has a highly specific geographic location in the Standing Rock Reservation in North Dakota and a continued presence there. Perhaps the physical presence has reduced the need (or at least the appearance of the need) to continue the conversation online.
The above map shows geotagged tweets using the #NoDAPL hashtag. The animation displays the time of day at which the tweets were posted. With a limited number of tweets, it is difficult to draw many conclusions, but the geographic and temporal diversity seem to indicate that conversation about the pipeline is not limited to a single region, nor is it dictated by any specific events. All of the geotagged tweets were posted from North America, and all but one from within the United States.
I chose to explore two locations with geotagged tweets that are relevant to the #NoDAPL movement and the tweets being posted about it: the Standing Rock Sioux Reservation in North Dakota, and the city of Seattle, Washington, where a city council vote recently took place to divest from a bank over its support of the pipeline. I chose to organize the maps the percentage of Native Americans in the population; in 1970, this was impossible, as the only race options were white, black, ‘some other race,’ and Hispanic, and the category for Native Americans went by two different names in the 1990 and 2010 censuses.
In North Dakota, the relevant data is almost nonexistent in the 1970 census. Only a handful of census tracts registered any ‘other’ population, and most have insufficient data. The 1990 and 2010 censuses are more complete: both show a significant American Indian population on the reservation land, and other pockets (presumably other reservations) of high-density American Indian populations. These centers, however, are surrounded by significantly lower percentages of American Indian population.
In Seattle, the ‘other’ population was significant in some tracts in 1970, but the percentage of American Indians was revealed to be low in the 1990 Census when more detailed racial categories were added. This is supported by the 2010 results, which registered even smaller percentages of American Indians in that year.
This presents an interesting picture of the #NoDAPL movement. While the Dakota Access Pipeline affects American Indians, specifically the Standing Rock Sioux, most directly, the cause has been championed by those around the country. In spite of the very small American Indian population in Seattle, two of the geotagged tweets (9% of all that were found) came from the area. I was somewhat surprised to see that most of the online activity surrounding Standing Rock comes from other locations. This supports my initial assumption that the issue would attract attention not only from American Indians and indigenous rights groups, but from those with other concerns as well.
A 2012 article about a similar pipeline, the Keystone XL, in Science Scope discusses the pros and cons of building the pipeline, but makes no mention of the impact on indigenous peoples in the United States and Canada. Although the potential environmental impacts—water contamination, pollution, human health hazards—are listed, there is no acknowledgement of who will feel the effects most acutely. It’s interesting to read a critical piece regarding a pipeline that entirely ignores one of the major issues surrounding it. Another article, from Mountain Research and Development, looks at the implications of a pipeline project in Russia’s Altai mountains, home to the indigenous Altai people. This article directly recognizes the significance of land to the Altai people and offers a methodology, using remote imaging and GIS, for analyzing the potential impact of a pipeline on native land and architectural monuments.
It is worth noting that most of the articles pertaining to my search terms (oil, pipeline, indigenous, American Indian) were from environmental or scientific journals. This may simply be a reflection of existing power structures, but it seems that the Dakota Access Pipeline and other similar projects are treated primarily as an environmental issue, and only secondarily as a social one. It appears that this phenomenon is supported by my mapped Twitter data, as much of the conversation is taking place in areas with small indigenous populations, far away from the epicenter of the event.
These mapped data points are the beginning of what Graham terms a “software-sorted geography:” a definition of a place based on aggregated characteristics of its inhabitants. With my Twitter data, an additional column—interaction with the #NoDAPL hashtag—can be added to that list. While my data is hardly large enough to constitute a ‘geography,’ it can be used to determine what kind of people are reacting to the Dakota Access Pipeline and draw some conclusions about the movement’s supporters.
boyd, danah and Kate Crawford. “Critical Questions For Big Data.” Information, Communication, & Society 15, no. 5 (2012): 662-679.
Yau, Nathan. 2013. Data Points: Visualization That Means Something. Hoboken, N.J.: Wiley.