How to Handle Armed Conflict Data in a Real-World Scenario?
- PDF / 2,828,064 Bytes
- 13 Pages / 439.642 x 666.49 pts Page_size
- 62 Downloads / 182 Views
How to Handle Armed Conflict Data in a Real-World Scenario? Anusua Trivedi1 · Kate Keator2 · Michael Scholtens2 · Brandon Haigood2 · Rahul Dodhia1 · Juan Lavista Ferres1 · Ria Sankar1 · Avirishu Verma1 Received: 30 September 2019 / Accepted: 17 August 2020 / © Springer Nature B.V. 2020
Abstract Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Transformation of armed conflict data tends to be a manual, timeconsuming task that nonprofits with limited budgets struggle to keep up with. In this paper, we explore the use of a deep natural language processing (NLP) model to aid the transformation of armed conflict data for conflict analysis. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Thus, our model contributes to the incorporation of technology in the peace building and conflict resolution sector. Keywords Armed conflict · NLP · Deep learning
1 Conflict Resolution and the Digital Age Rather than discussing the intangible idea of using AI in peacemaking, the authors will discuss a project currently underway. On a frequent basis, social science majors with a deep understanding of structural violence or the role identity plays in peacebuilding efforts must make ethical decisions on data. In the nonprofit realm, employees often must wear many hats and in today’s digital world, one of these hats
Anusua Trivedi and Kate Keator contribute equally as first authors. Anusua Trivedi
[email protected]
Extended author information available on the last page of the article.
A. Trivedi et al.
now include data guru or at least some form of data literacy. Yet how do you collect and structure information from something as messy as conflict? What are the decisions that a peacebuilder must make on a recurring basis that shape that information into something coherent and consistent? These analytical choices ranging from where to collect conflict data to what terminology to use impact the resulting analysis—a reality that peacebuilders are still grappling to understand. Just as information and communication technologies (ICTs) have changed the way people interact with each other (online chat rooms, Reddit, Facebook, Twitter etc.), ICTs have also changed how conflict resolution actors conduct conflict analysis and stakeholder mapping (Tufekci and Wilson 2012). The Syrian conflict has been one of the first major conflicts to unfold fully online. The unprecedented role digital information and communication technologies such as Twitter, Facebook, YouTube, and Reddit have ha
Data Loading...
