title: Fast Distributed Dynamics of Semantic Networks via Social Media.
abstract: We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.
authors.affiliation: Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación, Ciudad Universitaria, 1428 Buenos Aires, Argentina.
authors.forename: Facundo
authors.initials: F
authors.lastname: Carrillo
authors.affiliation: Computational Biology Center, T. J. Watson Research Center, IBM, P.O. Box 218, Yorktown Heights, NY 10598, USA.
authors.forename: Guillermo A
authors.initials: GA
authors.lastname: Cecchi
authors.affiliation: Universidad Torcuato Di Tella, Avenida Figueroa Alcorta 7350, 1428 Buenos Aires, Argentina.
authors.forename: Mariano
authors.initials: M
authors.lastname: Sigman
authors.affiliation: Laboratorio de Inteligencia Artificial Aplicada, Departamento de Computación, Ciudad Universitaria, 1428 Buenos Aires, Argentina.
authors.forename: Diego Fernández
authors.initials: DF
authors.lastname: Slezak
mesh_terms: D000465:Algorithms; D006801:Humans; D016247:Information Storage and Retrieval; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012660:Semantics; D061108:Social Media / Q000706:statistics & numerical data; D013850:Thinking / Q000502:physiology*