El problema de las noticias falsas: detección y contramedidas

Blázquez-Ochando, Manuel El problema de las noticias falsas: detección y contramedidas., 2018 . In XV Seminario Hispano–Mexicano de Investigación en Biblioteconomía y Documentación, Ciudad de México, 16-18 de mayo de 2018. [Conference paper]

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English abstract

In this research we analyse the fenomena, motivations, mechanisms, and rebroadcasting vectors of fake news. In other hand, was review the main scientific solutions and identified their countermeasures. This is due to several factors, for example new topics and facts available in published news, of which there aren´t constancy or previous experience in knowledge base; the use of reverse engineering to counteract machine learning software; the difficulties to find reliable, impartial and independent information sources; and finally, the difficulties to develop technologies to assess the facts and evidences cited on news. We reach the conclussion that, it´s feasible designing methods for detect an important part of fraud news, but it´s not enought. It´s because application environment it´s restricted to a few sources, thematics or samples, that don´t represent the open environment we face. We need improve the fake news knowledge base, develop more efficient semantic models, get better informative characteristics on news, recover a new version of RSS rebroadcasting system and set previous filters before users feed on the news.

Spanish abstract

En este artículo se revisa la problemática de las noticias falsas, reflexionando al respecto de su motivación, mecanismos y vectores de distribución. Por otra parte, se revisan algunas de las soluciones científicas más relevantes e identifican sus principales contramedidas. Ello es debido a múltiples factores, como la publicación de nuevos asuntos, noticias y temáticas en tiempo real de las que no se tienen constancia o experiencia previa; el uso de ingeniería inversa para contrarrestar al machine learning e incluso al deep learning; la dificultad para encontrar fuentes fiables, independientes y realmente imparciales; y finalmente la dificultad para desarrollar un programa capaz de valorar las pruebas aportadas en las noticias publicadas. Se llega a la conclusión de que, si bien es posible desarrollar tecnologías que permitan la detección de noticias falsas, su aplicación intensiva en entornos informacionales abiertos, aún no constituyen una solución definitiva. Es necesario perfeccionar las bases de conocimiento de referencia, desarrollar modelos semánticos complejos en los que se simule la revisión pericial del ser humano, mejorar las características informativas, formales y descriptivas de las noticias, así como recuperar los medios de redifusión de sindicación de contenidos para mejorar el control y revisión de las informaciones.

Item type: Conference paper
Keywords: Noticias falsas, Fake news, Machine-learning, Deep-learning, Aprendizaje automático, Fake news, Machine-learning, Deep-learning, Fake news detection
Subjects: B. Information use and sociology of information > BA. Use and impact of information.
B. Information use and sociology of information > BD. Information society.
I. Information treatment for information services
Depositing user: Dr. Manuel Blázquez Ochando
Date deposited: 24 Jul 2018 15:46
Last modified: 24 Jul 2018 15:46
URI: http://hdl.handle.net/10760/33171

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