Analysis of behavior of automatic learning algorithms to identify criminal messages
In this type of explanation, strictly economic or criminal motives predominate: mainly the control of routes and places, and the punishment of desertion or treason. The precarious and fragmentary nature of the public discourse of drug traffickers as well as the preponderance of police narratives has...
- Autores:
-
Varela, Noel
GALVEZ VALEGA, JESUS ARTURO
Pineda Lezama, Omar Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7698
- Acceso en línea:
- https://hdl.handle.net/11323/7698
https://doi.org/10.1016/j.procs.2020.07.019
https://repositorio.cuc.edu.co/
- Palabra clave:
- Text analysis model
Identification of authors
Criminal messages
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Analysis of behavior of automatic learning algorithms to identify criminal messages |
| title |
Analysis of behavior of automatic learning algorithms to identify criminal messages |
| spellingShingle |
Analysis of behavior of automatic learning algorithms to identify criminal messages Text analysis model Identification of authors Criminal messages |
| title_short |
Analysis of behavior of automatic learning algorithms to identify criminal messages |
| title_full |
Analysis of behavior of automatic learning algorithms to identify criminal messages |
| title_fullStr |
Analysis of behavior of automatic learning algorithms to identify criminal messages |
| title_full_unstemmed |
Analysis of behavior of automatic learning algorithms to identify criminal messages |
| title_sort |
Analysis of behavior of automatic learning algorithms to identify criminal messages |
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Varela, Noel GALVEZ VALEGA, JESUS ARTURO Pineda Lezama, Omar Bonerge |
| dc.contributor.author.spa.fl_str_mv |
Varela, Noel GALVEZ VALEGA, JESUS ARTURO Pineda Lezama, Omar Bonerge |
| dc.subject.spa.fl_str_mv |
Text analysis model Identification of authors Criminal messages |
| topic |
Text analysis model Identification of authors Criminal messages |
| description |
In this type of explanation, strictly economic or criminal motives predominate: mainly the control of routes and places, and the punishment of desertion or treason. The precarious and fragmentary nature of the public discourse of drug traffickers as well as the preponderance of police narratives has concealed the strictly political dimension of "criminal" violence in Colombia. In pragmatic terms, organized crime and politics are more similar than we would like to assume. They have in common the objective of dominating territories, resources and populations; both tend to stand as a system of "parasitic intermediation". Both mafias and the state offer "protection" in exchange for payment of fees, reward loyalty and punish treason. It is the discursive acts that accompany violence and the series of institutional procedures in which they are registered that allow us to draw the line between the political and the criminal, the legitimate and the illegitimate, the just and the unjust. In Colombia, that border has lost clarity. In this study, an analysis of narco-messages found in banners, social networks and other databases is carried out by applying data mining, in order to propose a geospatial model through which it is possible to identify and geographically distribute the authors of the messages. |
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2020 |
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2021-01-15T18:02:48Z |
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2021-01-15T18:02:48Z |
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eng |
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eng |
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[1] Srinivasan, L., & Nalini, C. (2019). An improved framework for authorship identification in online messages. Cluster Computing, 22(5), 12101-12110. [2] Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. R. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World wide web, 20(2), 135-154. [3] Gottlieb, A. (2017). The effect of message frames on public attitudes toward criminal justice reform for nonviolent offenses. Crime & Delinquency, 63(5), 636-656. [4] Iqbal, F., Fung, B. C., Debbabi, M., Batool, R., & Marrington, A. (2019). Wordnet-based criminal networks mining for cybercrime investigation. IEEE Access, 7, 22740-22755. [5] Duarte, N., Llanso, E., & Loup, A. (2018, January). Mixed Messages? The Limits of Automated Social Media Content Analysis. In FAT (p. 106). [6] Kounadi, O., Ristea, A., Leitner, M., & Langford, C. (2018). Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies. Cartography and geographic information science, 45(3), 205-220. [7] Ajao, O., Bhowmik, D., & Zargari, S. (2018, July). Fake news identification on twitter with hybrid cnn and rnn models. In Proceedings of the 9th International Conference on Social Media and Society (pp. 226-230). [8] Barbon, S., Igawa, R. A., & Zarpelão, B. B. (2017). Authorship verification applied to detection of compromised accounts on online social networks. Multimedia Tools and Applications, 76(3), 3213-3233. [9] Venckauskas, A., Karpavicius, A., Damaševičius, R., Marcinkevičius, R., Kapočiūte-Dzikiené, J., & Napoli, C. (2017, September). Open class authorship attribution of lithuanian internet comments using one-class classifier. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 373-382). IEEE. [10] Tundis, A., & Mühlhäuser, M. (2017, October). A multi-language approach towards the identification of suspicious users on social networks. In 2017 International Carnahan Conference on Security Technology (ICCST) (pp. 1-6). IEEE. [11] Tundis, A., & Mühlhäuser, M. (2017, October). A multi-language approach towards the identification of suspicious users on social networks. In 2017 International Carnahan Conference on Security Technology (ICCST) (pp. 1-6). IEEE. [12] Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. John Wiley & Sons. [13] Soundarya, V., Kanimozhi, U., & Manjula, D. (2017). Recommendation System for Criminal Behavioral Analysis on Social Network using Genetic Weighted K-Means Clustering. JCP, 12(3), 212-220. [14] Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130, 57-68. [15] Tundis, A., Bhatia, G., Jain, A., & Mühlhäuser, M. (2018, November). Supporting the identification and the assessment of suspicious users on twitter social media. In 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) (pp. 1-10). IEEE. [16] Tseng, T. Y., Krebs, P., Schoenthaler, A., Wong, S., Sherman, S., Gonzalez, M., ... & Shelley, D. (2017). Combining text messaging and telephone counseling to increase varenicline adherence and smoking abstinence among cigarette smokers living with HIV: a randomized controlled study. AIDS and Behavior, 21(7), 1964-1974. [17] Tundis, A., Jain, A., Bhatia, G., & Muhlhauser, M. (2019, July). Similarity Analysis of Criminals on Social Networks: An Example on Twitter. In 2019 28th International Conference on Computer Communication and Networks (ICCCN) (pp. 1-9). IEEE. [18] DeHart, D., Dwyer, G., Seto, M. C., Moran, R., Letourneau, E., & Schwarz-Watts, D. (2017). Internet sexual solicitation of children: a proposed typology of offenders based on their chats, e-mails, and social network posts. Journal of sexual aggression, 23(1), 77-89. [19] Vorobeva, A. A. (2016, April). Examining the performance of classification algorithms for imbalanced data sets in web author identification. In 2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT) (pp. 385-390). IEEE. [20] Sarna, G., & Bhatia, M. P. S. (2017). Content based approach to find the credibility of user in social networks: an application of cyberbullying. International Journal Of Machine Learning and Cybernetics, 8(2), 677-689. [21] Scrivens, R., Davies, G., & Frank, R. (2018). Searching for signs of extremism on the web: an introduction to Sentiment-based Identification of Radical Authors. Behavioral sciences of terrorism and political aggression, 10(1), 39-59. [22] Macnair, L., & Frank, R. (2018). The mediums and the messages: Exploring the language of Islamic State media through sentiment analysis. Critical Studies on Terrorism, 11(3), 438-457. [23] Kamatkar, S. J., Tayade, A., Viloria, A., & Hernández-Chacín, A. (2018). Application of classification technique of data mining for employee management system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10943 LNCS, pp. 434–444). Springer Verlag. https://doi.org/10.1007/978-3-319-93803-5_41 [24] Balaguera, M. I., Vargas, M. C., Lis-Gutierrez, J. P., Viloria, A., & Malagón, L. E. (2018). Architecture of an object-oriented modeling framework for human occupation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10942 LNCS, pp. 452–460). Springer Verlag. https://doi.org/10.1007/978-3-319-93818-9_43 |
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Varela, NoelGALVEZ VALEGA, JESUS ARTUROPineda Lezama, Omar Bonerge2021-01-15T18:02:48Z2021-01-15T18:02:48Z20201877-0509https://hdl.handle.net/11323/7698https://doi.org/10.1016/j.procs.2020.07.019Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In this type of explanation, strictly economic or criminal motives predominate: mainly the control of routes and places, and the punishment of desertion or treason. The precarious and fragmentary nature of the public discourse of drug traffickers as well as the preponderance of police narratives has concealed the strictly political dimension of "criminal" violence in Colombia. In pragmatic terms, organized crime and politics are more similar than we would like to assume. They have in common the objective of dominating territories, resources and populations; both tend to stand as a system of "parasitic intermediation". Both mafias and the state offer "protection" in exchange for payment of fees, reward loyalty and punish treason. It is the discursive acts that accompany violence and the series of institutional procedures in which they are registered that allow us to draw the line between the political and the criminal, the legitimate and the illegitimate, the just and the unjust. In Colombia, that border has lost clarity. In this study, an analysis of narco-messages found in banners, social networks and other databases is carried out by applying data mining, in order to propose a geospatial model through which it is possible to identify and geographically distribute the authors of the messages.Varela, NoelGALVEZ VALEGA, JESUS ARTURO-will be generated-orcid-0000-0002-1380-2032-600Pineda Lezama, Omar Bonergeapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920316987Text analysis modelIdentification of authorsCriminal messagesAnalysis of behavior of automatic learning algorithms to identify criminal messagesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Srinivasan, L., & Nalini, C. (2019). An improved framework for authorship identification in online messages. Cluster Computing, 22(5), 12101-12110.[2] Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. R. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World wide web, 20(2), 135-154.[3] Gottlieb, A. (2017). The effect of message frames on public attitudes toward criminal justice reform for nonviolent offenses. Crime & Delinquency, 63(5), 636-656.[4] Iqbal, F., Fung, B. C., Debbabi, M., Batool, R., & Marrington, A. (2019). Wordnet-based criminal networks mining for cybercrime investigation. IEEE Access, 7, 22740-22755.[5] Duarte, N., Llanso, E., & Loup, A. (2018, January). Mixed Messages? The Limits of Automated Social Media Content Analysis. In FAT (p. 106).[6] Kounadi, O., Ristea, A., Leitner, M., & Langford, C. (2018). Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies. Cartography and geographic information science, 45(3), 205-220.[7] Ajao, O., Bhowmik, D., & Zargari, S. (2018, July). Fake news identification on twitter with hybrid cnn and rnn models. In Proceedings of the 9th International Conference on Social Media and Society (pp. 226-230).[8] Barbon, S., Igawa, R. A., & Zarpelão, B. B. (2017). Authorship verification applied to detection of compromised accounts on online social networks. Multimedia Tools and Applications, 76(3), 3213-3233.[9] Venckauskas, A., Karpavicius, A., Damaševičius, R., Marcinkevičius, R., Kapočiūte-Dzikiené, J., & Napoli, C. (2017, September). Open class authorship attribution of lithuanian internet comments using one-class classifier. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 373-382). IEEE.[10] Tundis, A., & Mühlhäuser, M. (2017, October). A multi-language approach towards the identification of suspicious users on social networks. In 2017 International Carnahan Conference on Security Technology (ICCST) (pp. 1-6). IEEE.[11] Tundis, A., & Mühlhäuser, M. (2017, October). A multi-language approach towards the identification of suspicious users on social networks. In 2017 International Carnahan Conference on Security Technology (ICCST) (pp. 1-6). IEEE.[12] Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact. John Wiley & Sons.[13] Soundarya, V., Kanimozhi, U., & Manjula, D. (2017). Recommendation System for Criminal Behavioral Analysis on Social Network using Genetic Weighted K-Means Clustering. JCP, 12(3), 212-220.[14] Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130, 57-68.[15] Tundis, A., Bhatia, G., Jain, A., & Mühlhäuser, M. (2018, November). Supporting the identification and the assessment of suspicious users on twitter social media. In 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) (pp. 1-10). IEEE.[16] Tseng, T. Y., Krebs, P., Schoenthaler, A., Wong, S., Sherman, S., Gonzalez, M., ... & Shelley, D. (2017). Combining text messaging and telephone counseling to increase varenicline adherence and smoking abstinence among cigarette smokers living with HIV: a randomized controlled study. AIDS and Behavior, 21(7), 1964-1974.[17] Tundis, A., Jain, A., Bhatia, G., & Muhlhauser, M. (2019, July). Similarity Analysis of Criminals on Social Networks: An Example on Twitter. In 2019 28th International Conference on Computer Communication and Networks (ICCCN) (pp. 1-9). IEEE.[18] DeHart, D., Dwyer, G., Seto, M. C., Moran, R., Letourneau, E., & Schwarz-Watts, D. (2017). Internet sexual solicitation of children: a proposed typology of offenders based on their chats, e-mails, and social network posts. Journal of sexual aggression, 23(1), 77-89.[19] Vorobeva, A. A. (2016, April). Examining the performance of classification algorithms for imbalanced data sets in web author identification. In 2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT) (pp. 385-390). IEEE.[20] Sarna, G., & Bhatia, M. P. S. (2017). Content based approach to find the credibility of user in social networks: an application of cyberbullying. International Journal Of Machine Learning and Cybernetics, 8(2), 677-689.[21] Scrivens, R., Davies, G., & Frank, R. (2018). Searching for signs of extremism on the web: an introduction to Sentiment-based Identification of Radical Authors. Behavioral sciences of terrorism and political aggression, 10(1), 39-59.[22] Macnair, L., & Frank, R. (2018). The mediums and the messages: Exploring the language of Islamic State media through sentiment analysis. Critical Studies on Terrorism, 11(3), 438-457.[23] Kamatkar, S. J., Tayade, A., Viloria, A., & Hernández-Chacín, A. (2018). Application of classification technique of data mining for employee management system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10943 LNCS, pp. 434–444). Springer Verlag. https://doi.org/10.1007/978-3-319-93803-5_41[24] Balaguera, M. I., Vargas, M. C., Lis-Gutierrez, J. P., Viloria, A., & Malagón, L. E. (2018). Architecture of an object-oriented modeling framework for human occupation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10942 LNCS, pp. 452–460). 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