English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In complex, high dimensional and unstructured data it is often difficult toextract meaningful patterns. This is especially the case when dealing withtextual data. Recent studies in machine learning, information theory andnetwork science have developed several novel instruments to extract thesemantics of unstructured data, and harness it to build a network of relations.Such approaches serve as an efficient tool for dimensionality reduction andpattern detection. This paper applies semantic network science to extractideological proximity in the international arena, by focusing on the data fromGeneral Debates in the UN General Assembly on the topics of high salience tointernational community. UN General Debate corpus (UNGDC) covers all high-leveldebates in the UN General Assembly from 1970 to 2014, covering all UN memberstates. The research proceeds in three main steps. First, Latent DirichletAllocation (LDA) is used to extract the topics of the UN speeches, andtherefore semantic information. Each country is then assigned a vectorspecifying the exposure to each of the topics identified. This intermediateoutput is then used in to construct a network of countries based on informationtheoretical metrics where the links capture similar vectorial patterns in thetopic distributions. Topology of the networks is then analyzed through networkproperties like density, path length and clustering. Finally, we identifyspecific topological features of our networks using the map equation frameworkto detect communities in our networks of countries.

Topology Analysis of International Networks Based on Debates in the United Nations