DATA SCIENCE AND INTELLIGENCE KNOWLEDGE PRODUCTION
POTENTIAL OF THE ANALYSIS OF SOCIAL NETWORKS FOR INTELLIGENCE ACTIVITY
DOI:
https://doi.org/10.58960/rbi.2021.16.196Keywords:
Intelligence, Data Science, Intelligence knowledge production, data analysis, social networksAbstract
In the last 20 years, the scope of interests of the Intelligence analysis has broadened to encompass the contents and methods which were created digitally (or began to leave digital footprints). Considering the peculiarity of social networks data, it is necessary to incorporate Data Science techniques to the Intelligence analysis skill set. This article displays the potential of Data Science techniques in the analysis of social networks data for Intelligence knowledge production. The motivation behind this essay is to publicize and foster the debate concerning the constant improvement of Data Science methods, techniques and tools adopted in social networking analysis.
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References
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Anthony; SHERBONDY, Anthony; SINGH, Raghavendra. Cognitive Computing.
Communications of the ACM, v. 54, p. 62-71, 2011. Disponível em: http://cacm.acm.org/
magazines/2011/8/114944-cognitive-computing/fulltext. Acesso em: 01 out. 2021.
BRASIL, Lei nº 9.883, de 7 de dezembro de 1999. Institui o Sistema Brasileiro de
Inteligência, cria a Agência Brasileira de Inteligência - ABIN, e dá outras providências.
Brasília, DF: Presidência da República, 1999. Disponível em: http://www.planalto.gov.
br/ccivil_03/leis/L9883.htm. Acesso em: 01 out. 2021.
______. Decreto nº 4.376, de 13 de setembro de 2002. Dispõe sobre a organização e o
funcionamento do Sistema Brasileiro de Inteligência, instituído pela Lei nº 9.883, de 7
de dezembro de 1999, e dá outras providências. Brasília, DF: Presidência da República,
2002. Disponível em: http://www.planalto.gov.br/ccivil_03/decreto/2002/d4376.htm.
Acesso em: 01 out. 2021.
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Brasília, DF: Presidência da República, [2016a]. Disponível em: http://www.planalto.gov.
br/ccivil_03/_ato2015-2018/2016/decreto/D8793.htm. Acesso em: 01 out. 2021.
______ . Gabinete de Segurança Institucional. Agência Brasileira de Inteligência. Doutrina
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ABIN, 2016 Disponível em: https://www.gov.br/abin/pt-br/centrais-de-conteudo/
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Quadro Demonstrativo dos Cargos em Comissão e das Funções de Confi ança da Agência
Brasileira de Inteligência e remaneja e transforma cargos em comissão e funções de
confi ança. Brasília, DF: Presidência da República, 2020. Disponível em: http://www.
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COLBAUGH, Richard; GLASS, Kristin. Estimating sentiment orientation in social media
for intelligence monitoring and analysis. IEEE International Conference on Intelligence
and Security Informatics (ISI), p. 135–137, 2010. Disponível em: https://www.scss.tcd.
ie/Khurshid.Ahmad/Research/Sentiments/K_Teams_Buchraest/05484760.pdf. Acesso
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company/IDC_1142.pdf . Acesso em: 01 out. 2021.
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emotions in text. Proceedings of the NAACL HLT 2010 workshop on computational approaches
to analysis and generation of emotion in text. Association for Computational Linguistics,
p. 140–146, 2010. Disponível em: https://dl.acm.org/doi/pdf/10.5555/1860631.1860648.
Acesso em: 01 out. 2021.
GILLILAND, Anne J. Introduction to Metadata. 3ª ed. Los Angeles, Getty Research Institute,
2016. Disponível em: http://www.getty.edu/publications/intrometadata/. Acesso em:
01 out. 2021.
HAN, Jiawei; KAMBER, Micheline; PEI, Jian. Data Mining: Concepts and Techniques. 3ª ed.
Waltham, Morgan Kaufmann Publishers, 2012.
HAYASHI, Chikio. What is Data Science? Fundamental Concepts and a Heuristic Example.
In: Data Science, Classifi cation, and Related Methods. Studies in Classifi cation, Data Analysis,
and Knowledge Organization. Tokyo, Springer, 1998.
HECHT, Brent; HONG, Lichan; SUH, Bongwon; CHI, Ed H. Tweets from Justin Bieber’s
heart: The dynamics of the location fi eld in user profi les. In Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems (CHI ’11). Vancouver: Canada. ACM.
p. 237–246, 2011. Disponível em: https://www-users.cs.umn.edu/~bhecht/publications/
bhecht_chi2011_location.pdf. Acesso em: 19 out. 2021.
KAATI, Lisa; SHRESTHA, Amendra; COHEN, Katie; LINDQUIST; Sinna. Automatic detection
of xenophobic narratives: A case study on swedish alternative media. IEEE Conference on
Intelligence and Security Informatics (ISI). Tucson: USA. IEEE. 2016. Disponível em: https://
www.foi.se/download/18.7fd35d7f166c56ebe0bffd8/1542623691578/Automaticdetection-
xenophopic_FOI-S--5655--SE.pdf. Acesso em: 01 out. 2021.
KESHTKAR, Fazel; INKPEN, Diana. Using Sentiment Orientation Features for Mood Classifi cation in blogs. IEEE International Conference on Natural Language Processing
and Knowledge Engineering. Dalian: China. IEEE. 2009. Disponível em: https://www.
researchgate.net/publication/224076625_Using_sentiment_orientation_features_for_
mood_classifi cation_in_blogs. Acesso em: 01 out. 2021.
KELLEHER, John D; TIERNEY, Brendan. Data Science, 1ª ed. Cambridge, MIT Press, 2018.
LOWENTHAL, Mark. Intelligence: from secrets to policy. 8ª ed. Washington-DC, CQ Press,
2019.
NUNES, Eric; DIAB, Ahmad; GUNN, Andrew; MARIN, Ericsson; MISHRA, Vineet; PALIATH,
Vivin; ROBERTSON, John; SHAKARIAN, Jana; THART, Amanda; SHAKARIAN, Paulo. Darknet
and Deepnet mining for proactive cybersecurity threat intelligence. 2016. Disponível em:
https://arxiv.org/abs/1607. 08583. Acesso em: 01 out. 2021.
O’NEIL, Cathy. Weapons of Math Destruction: how big data increases inequality and threatens
democracy. New York, Crown Publishers, 2016.
ROWE, Matthew; SAIF, Hassan. Mining pro-isis radicalisation signals from social media
users. Proceedings of the Tenth International AAAI Conference on Web and Social Media
(ICWSM 2016). Cologny: Germany. AAAI. p. 329–338, 2016. Disponível em: http://oro.
open.ac.uk/48477/. Acesso em: 01 out. 2021.
RUSSELL, Stuart Jonathan; NORVIG, Peter. Artifi cial Intelligence: A Modern Approach. 4ª
ed. Global edition. Hoboken, Pearson, 2021.
SHERMAN, Chris; PRICE, Gary. The invisible web: uncovering information sources: search
engines can’t see. 7ª ed. Medford, CyberAge Books, Information Today, Inc., 2001.
THORLEUCHTER, Dirk; VAN DEN POEL; Dirk. Protecting research and technology from
espionage. Expert Systems with Applications. Elsevier. v. 40, issue 9, p. 3432-3440. 2013.
Disponível em: http://wps-feb.ugent.be/Papers/wp_12_824.pdf Acesso em: 1º out. 2021.
WANG, Yingxu; ZHANG, Du; LATOMBE, Jean-Claude; KINSNER, Witold. Advances in
the Fields of Cognitive Informatics and Cognitive Computing. In: Advances in Cognitive
Informatics and Cognitive Computing. Stanford, Springer, 2010.
YIN, Jie; LAMPERT, Andrew; CAMERON, Mark; ROBINSON, Bella; POWER, Robert. Using
social media to enhance emergency situation awareness. International Joint Conference
on Artifi cial Intelligence Buenos Aires: Argentina. IEEE. v. 27, p. 52–59, 2015. Disponível
em: https://www.researchgate.net/publication/280829031_Using_Social_Media_to_
Enhance_Emergency_Situation_Awareness_Extended_Abstract. Acesso em: 01 out. 2021.
ZHOU, Ning; CHEUNG, William K.; QIU, Guoping; XUE, Xiangyang. A hybrid probabilistic model for unifi ed collaborative and content-based image tagging. IEEE Transactions on
Pattern Analysis and Machine Intelligence. IEEE. v. 33, p. 1281–1294, 2011. Disponível em:
https://www.researchgate.net/publication/224196190_A_Hybrid_Probabilistic_Model_
for_Unifi ed_Collaborative_and_Content-Based_Image_Tagging. Acesso em: out. 2021.
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Published
2022-06-08
How to Cite
Fugisawa de Souza, Daniel, and David Ricardo Damasceno do Bomfim. 2022. “DATA SCIENCE AND INTELLIGENCE KNOWLEDGE PRODUCTION: POTENTIAL OF THE ANALYSIS OF SOCIAL NETWORKS FOR INTELLIGENCE ACTIVITY”. Brazilian Journal of Intelligence, no. 16 (June). Brasília, Brasil:53-77. https://doi.org/10.58960/rbi.2021.16.196.
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Research article
