2017/07/29 by Stefano Gurciullo, Gurciullo, Stefano, Slava Mikhaylov +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Algebraic Topology (math.AT) #Applications (stat.AP) #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Computation and Language (cs.CL) #Computational Geometry (cs.CG) #FOS: Computer and information sciences #FOS: Mathematics #Topological and Geometric Data Analysis #cs.CG #cs.CL #math.AT #stat.AP
paper · pdf · doi:10.48550/arxiv.1707.09491
arxiv created 2017/07/29 · openalex publication_date 2017/07/29 · arxiv updated 2017/08/01 · openalex created_date 2017/08/08 · openalex updated_date 2026/07/28
In complex, high dimensional and unstructured data it is often difficult to extract meaningful patterns. This is especially the case when dealing with textual data. Recent studies in machine learning, information theory and network science have developed several novel instruments to extract the semantics of unstructured data, and harness it to build a network of relations. Such approaches serve as an efficient tool for dimensionality reduction and pattern detection. This paper applies semantic network science to extract ideological proximity in the international arena, by focusing on the data from General Debates in the UN General Assembly on the topics of high salience to international community. UN General Debate corpus (UNGDC) covers all high-level debates in the UN General Assembly from 1970 to 2014, covering all UN member states. The research proceeds in three main steps. First, Latent Dirichlet Allocation (LDA) is used to extract the topics of the UN speeches, and therefore semantic information. Each country is then assigned a vector specifying the exposure to each of the topics identified. This intermediate output is then used in to construct a network of countries based on information theoretical metrics where the links capture similar vectorial patterns in the topic distributions. Topology of the networks is then analyzed through network properties like density, path length and clustering. Finally, we identify specific topological features of our networks using the map equation framework to detect communities in our networks of countries.