arXiv subjects

Matthew Connelly

Publications and source records attributed to Matthew Connelly.

3 recordsLinked to original sources

arXiv2023

KG-FRUS: a Novel Graph-based Dataset of 127 Years of US Diplomatic Relations

In the current paper, we present the KG-FRUS dataset, comprised of more than 300,000 US government diplomatic documents encoded in a Knowledge Graph (KG). We leverage the data of the Foreign Relations of the United States (FRUS) (available as XML files) to extract information about the documents and the individuals and countries mentioned within them. We use the extracted entities, and associated metadata, to create a graph-based dataset. Further, we supplement the created KG with additional entities and relations from Wikidata. The relations in the KG capture the synergies and dynamics required to study and understand the complex fields of diplomacy, foreign relations, and politics. This goes well beyond a simple collection of documents which neglects the relations between entities in the text. We showcase a range of possibilities of the current dataset by illustrating different approaches to probe the KG. In the paper, we exemplify how to use a query language to answer simple research questions and how to use graph algorithms such as Node2Vec and PageRank, that benefit from the complete graph structure. More importantly, the chosen structure provides total flexibility for continuously expanding and enriching the graph. Our solution is general, so the proposed pipeline for building the KG can encode other original corpora of time-dependent and complex phenomena. Overall, we present a mechanism to create KG databases providing a more versatile representation of time-dependent related text data and a particular application to the all-important FRUS database.

cs.CL↗

arXiv2017

Mining Events with Declassified Diplomatic Documents

Since 1973 the State Department has been using electronic records systems to preserve classified communications. Recently, approximately 1.9 million of these records from 1973-77 have been made available by the U.S. National Archives. While some of these communication streams have periods witnessing an acceleration in the rate of transmission; others do not show any notable patterns in communication intensity. Given the sheer volume of these communications -- far greater than what had been available until now -- scholars need automated statistical techniques to identify the communications that warrant closer study. We develop a statistical framework that can semi-automatically identify from a large corpus of documents a handful that historians would consider more interesting electronic records. Our approach brings together related but distinct statistical concepts from nonparametric signal estimation and statistical hypothesis testing -- which when put together help us identify and analyze various geometrical aspects of the communication streams. Dominant periods of heightened and sustained activities aka bursts, as identified through these methods, correspond well with historical events recognized by standard reference works on the 1970s.

stat.ME↗

arXiv2016

Using Artificial Intelligence to Identify State Secrets

Whether officials can be trusted to protect national security information has become a matter of great public controversy, reigniting a long-standing debate about the scope and nature of official secrecy. The declassification of millions of electronic records has made it possible to analyze these issues with greater rigor and precision. Using machine-learning methods, we examined nearly a million State Department cables from the 1970s to identify features of records that are more likely to be classified, such as international negotiations, military operations, and high-level communications. Even with incomplete data, algorithms can use such features to identify 90% of classified cables with <11% false positives. But our results also show that there are longstanding problems in the identification of sensitive information. Error analysis reveals many examples of both overclassification and underclassification. This indicates both the need for research on inter-coder reliability among officials as to what constitutes classified material and the opportunity to develop recommender systems to better manage both classification and declassification.

cs.CY↗