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]

[thumbnail of fakeNews-2018-mblazquez-mexico-FINAL.pdf]
Preview
 Text
fakeNews-2018-mblazquez-mexico-FINAL.pdf
Download (805kB) | Preview
[thumbnail of fakeNews-2018.pptx] Slideshow
fakeNews-2018.pptx
Download (2MB)

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

References

Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211-36.

Álvarez, R. (2018). La privacidad en Facebook no existe: Zuckerberg conoce (casi) todo de sus usuarios, hasta sus llamadas y SMS si tienen Android. En: Xataka. https://www.xataka.com/privacidad/la-privacidad-en-facebook-no-existe-zuckerberg-conoce-casi-todo-de-sus-usuarios-hasta-sus-llamadas-y-sms-si-tienen-android [Consulta 14-04-2018]

Aro, J. (2016). The cyberspace war: propaganda and trolling as warfare tools. European View, 15(1), 121-132.

Bakir, V., & McStay, A. (2018). Fake news and the economy of emotions: Problems, causes, solutions. Digital Journalism, 6(2), 154-175.

Błachnio, A., Przepiorka, A., & Pantic, I. (2016). Association between Facebook addiction, self-esteem and life satisfaction: A cross-sectional study. Computers in Human Behavior, 55, 701-705.

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.

Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., & Weber, I. (2012). Web search queries can predict stock market volumes. PloS one, 7(7), e40014.

Bowley, G. (2010). „Computers That Trade on the News‟. New York Times, 22(12), 2010.

Carey, J. W. (1974). The problem of journalism history. Journalism History, 1(1), 3.

Chen, R., & Lazer, M. (2013). Sentiment analysis of twitter feeds for the prediction of stock market movement. stanford. edu. Retrieved January, 25, 2013.

Conroy, M., Feezell, J. T., & Guerrero, M. (2012). Facebook and political engagement: A study of online political group membership and offline political engagement. Computers in Human behavior, 28(5), 1535-1546.

Desantes, J. M. (1976). La verdad en la información. Valladolid: SP Diputación Provincial de Valladolid.

Dewey, C. (2016). 98 personal data points that Facebook uses to target ads to you. En: Washington Post. https://www.washingtonpost.com/news/the-intersect/wp/2016/08/19/98-personal-data-points-that-facebook-uses-to-target-ads-to-you/?noredirect=on&utm_term=.d6905783c21c [Consulta 14-04-2018]

Espiritusanto, O., & Rodríguez, P. G. (2011). Periodismo ciudadano: evolución positiva de la comunicación (Vol. 31). Fundación Telefónica.

Facebook Newsroom. (2018). https://newsroom.fb.com/company-info/ [Consulta 14-04-2018]

Facebook. (2018). Facebook for developers: Posting to a Page. https://developers.facebook.com/docs/pages/publishing [Consulta 14-04-2018]

Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104.

Ghosh, D., & Scott, B. (2018). The Technologies Behind Precision Propaganda on the Internet.

González, M. (2018). Qué ha pasado con Facebook: del caso Cambridge Analytica al resto de polémicas más recientes. En: Xataka. https://www.xataka.com/legislacion-y-derechos/que-ha-pasado-con-facebook-del-caso-cambridge-analytica-al-resto-de-polemicas-mas-recientes [Consulta 14-04-2018]

Graham, W. (2008). Facebook API developers guide. Infobase Publishing.

Karabulut, Y. (2013). Can Facebook predict stock market activity?.

Khaldarova, I., & Pantti, M. (2016). Fake news: The narrative battle over the Ukrainian conflict. Journalism Practice, 10(7), 891-901.

Kogan, S., Moskowitz, T. J., & Niessner, M. (2017). Fake News in Financial Markets.

Larcker, D. F., & Zakolyukina, A. A. (2012). Detecting deceptive discussions in conference calls. Journal of Accounting Research, 50(2), 495-540.

Lerman, K., & Ghosh, R. (2010). Information contagion: An empirical study of the spread of news on Digg and Twitter social networks. Icwsm, 10, 90-97.

Lin, T. C. (2016). The new market manipulation. Emory LJ, 66, 1253.

Makice, K. (2009). Twitter API: Up and running: Learn how to build applications with the Twitter API. " O'Reilly Media, Inc.".

Markowitz, D. M., & Hancock, J. T. (2014). Linguistic traces of a scientific fraud: The case of Diederik Stapel. PloS one, 9(8), e105937.

Miller, G. R., & Stiff, J. B. (1993). Deceptive communication. Sage Publications, Inc.

Mittal, A., & Goel, A. (2012). Stock prediction using twitter sentiment analysis. Standford University, CS229 (2011 http://cs229. stanford. edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis. pdf), 15.

Radinsky, K., & Horvitz, E. (2013, February). Mining the web to predict future events. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 255-264). ACM.

Rao, T., & Srivastava, S. (2012, August). Analyzing stock market movements using twitter sentiment analysis. In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) (pp. 119-123). IEEE Computer Society.

Rubin, V. L., & Lukoianova, T. (2015). Truth and deception at the rhetorical structure level. Journal of the Association for Information Science and Technology, 66(5), 905-917.

Rudd, K. (2009). The global financial crisis. Monthly, The, (Feb 2009), 20.

Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017). The spread of fake news by social bots. arXiv preprint arXiv:1707.07592.

Steinberg, F. (2008). La crisis financiera global y las relaciones económicas entre Estados Unidos y China. Anuario Asia Pacífico 2008.

Turel, O., He, Q., Xue, G., Xiao, L., & Bechara, A. (2014). Examination of neural systems sub-serving Facebook “addiction”. Psychological Reports, 115(3), 675-695.

Vedwan, N. (2013). Does Facebook Make Us Happy? Happiness in an Age of Hyper-connectedness. Anthropology Now, 5(2), 87-92.

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.

Wang, X., Gerber, M. S., & Brown, D. E. (2012, April). Automatic crime prediction using events extracted from twitter posts. In International conference on social computing, behavioral-cultural modeling, and prediction (pp. 231-238). Springer, Berlin, Heidelberg.


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item
Contact us
Send email Sponsors
Sponsor: AIMS Sponsor: D&P Sponsor: UniNa CAB Get connected
Facebook Facebook Twitter Twitter Linkedin LinkedIn RSS RSS