Deconstructing Afghan displacement data: Acknowledging the elephant in the dark

People are migrating at record levels, with the International Organization for Migration (IOM) estimating that one in seven people worldwide are on the move either by choice or force (IOM, 2015). The Office of the United Nations High Commissioner for Refugees (UNHCR), for example, reported forced displacement at the end of 2015 at “the highest [level] since the aftermath of World War II” (UNHCR, 2016). With so many people on the move, it is worth reexamining how well the phenomenon is documented.Timely and reliable information is the cornerstone of sound decision-making for policy and service provision, including for the protection of those fleeing from conflict. The ongoing and protracted nature ofdisplacement within and from Afghanistan provides a useful case to examine

Afghanistan: A unity government at last or woes to come?

Analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

© Springer Nature Switzerland AG 2018. Text documents often contain information relevant for a particular domain in short “snippets”. The social science field of peace and conflict studies is such a domain, where identifying, classifying and tracking drivers of conflict from text sources is important, and snippets are typically classified by human analysts using an ontology. One issue in automating this process is that snippets tend to contain infrequent “rare” terms which lack class-conditional evidence. In this work we develop a method to enrich a bag-of-words model by complementing rare terms in the text to be classified with related terms from a Word Vector model. This method is then combined with standard linear text classification algorithms. By reducing sparseness in the bag-of-words, these enriched models perform better than the baseline classifiers. A second issue is to improve performance on “small” classes having only a few examples, and here we show that Paragraph Vectors outperform the enriched models