Titulo:
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico
.
Sumario:
This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitan
Guardado en:
2389-8186
2389-8194
7
2020-12-01
6
18
Revista Perspectiva Empresarial - 2020
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http://purl.org/coar/access_right/c_abf2
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CEIPA BUSINESS SCHOOL |
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Colombia |
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Revista Perspectiva Empresarial |
| title |
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico |
| spellingShingle |
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico Paredes Valverde, Mario Andrés Sánchez Cervantes, José Luis Suárez Gutiérrez, Manuel Social media data analysis pattern recognition violence perception semantics |
| title_short |
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico |
| title_full |
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico |
| title_fullStr |
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico |
| title_full_unstemmed |
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico |
| title_sort |
online social network model through twitter to build a social perception variable to measure the violence in mexico |
| title_eng |
An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico |
| description |
This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitan
|
| description_eng |
This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitan
|
| author |
Paredes Valverde, Mario Andrés Sánchez Cervantes, José Luis Suárez Gutiérrez, Manuel |
| author_facet |
Paredes Valverde, Mario Andrés Sánchez Cervantes, José Luis Suárez Gutiérrez, Manuel |
| topic |
Social media data analysis pattern recognition violence perception semantics |
| topic_facet |
Social media data analysis pattern recognition violence perception semantics |
| citationvolume |
7 |
| citationissue |
2 Supl.1 |
| citationedition |
Núm. 2 Supl.1 , Año 2020 : “1th International Workshop on Enterprise Decision-Making Applying Artificial Intelligence Techniques (WEDMAIT 2020)” |
| publisher |
Sabaneta: Fundación Universitaria Ceipa, 2014- |
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Revista Perspectiva Empresarial |
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https://revistas.ceipa.edu.co/index.php/perspectiva-empresarial/article/view/665 |
| language |
Inglés |
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Article |
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https://creativecommons.org/licenses/by-nc-sa/4.0/ Revista Perspectiva Empresarial - 2020 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| references_eng |
Agarwal, B., Ravikumar, A. and Saha, S. (2017). A Novel Approach to Big Data Veracity Using Crowdsourcing Techniques and Bayesian Predictors. In 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, USA. https://doi.org/10.1109/ICMLA.2016.0184 Al-Hajjar, D. and Syed, A. (2015). Applying Sentiment and Emotion Analysis on Brand Tweets for Digital Marketing. In IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Amman, Jordan. https://doi.org/10.1109/AEECT.2015.7360592 Ashwin, K., Kammarpally, P. and George, K. (2016). Veracity of Information in Twitter Data: A Case Study. In International Conference on Big Data and Smart Computing (BigComp), Hong Kong, China. https://doi.org/10.1109/BIGCOMP.2016.7425811 Baydogan, C. and Alatas, B. (2018). Sentiment Analysis Using Konstanz Information Miner in Social Networks. In 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, Turkey. https://doi.org/10.1109/ISDFS.2018.8355395 Brogueira, G., Batista, F. and Carvalho, J.P. (2016). Using Geolocated Tweets for Characterization of Twitter in Portugal and the Portuguese Administrative Regions. Social Network Analysis and Mining, 6(1), 1-20. https://doi.org/10.1007/s13278-016-0347-8 Bustos López, M. et al. (2018). EduRP: An Educational Resources Platform Based on Opinion Mining and Semantic Web. Journal of Universal Computer Science, 24(11), 1515-1535. Devraj, N. and Chary, M. (2015). How Do Twitter, Wikipedia, and Harrison's Principles of Medicine Describe Heart Attacks? In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, Atlanta, Georgia. https://doi.org/10.1145/2808719.2812591 Garg, P., Garg, H. and Ranga, V. (2017). Sentiment Analysis of the Uri Terror Attack Using Twitter. In International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India. https://doi.org/10.1109/CCAA.2017.8229812 Khatua, A., Cambria, E. and Khatua, A. (2018). Sounds of Silence Breakers: Exploring Sexual Violence on Twitter. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain. https://doi.org/10.1109/ASONAM.2018.8508576 Kim, Y., Hwang, E. and Rho, S. (2016). Twitter News-in-Education Platform for Social, Collaborative, and Flipped Learning. The Journal of Supercomputing, 74(8), 3564-3582. https://doi.org/10.1007/s11227-016-1776-x Kononenko, O. et al. (2014). Mining Modern Repositories with Elasticsearch. In Proceedings of the 11th Working Conference on Mining Software Repositories, Chicago, USA. https://doi.org/10.1145/2597073.2597091 Langi, P. et al. (2016). An Evaluation of Twitter River and Logstash Performances as Elasticsearch Inputs for Social Media Analysis of Twitter. In International Conference on Information & Communication Technology and Systems (ICTS), Surabaya, Indonesia. https://doi.org/10.1109/ICTS.2015.7379895 Lee, R., Wakamiya, S. and Sumiya, K. (2013). Urban Area Characterization Based on Crowd Behavioral Lifelogs over Twitter. Personal and Ubiquitous Computing, 17(4), 605-620. https://doi.org/10.1007/s00779-012-0510-9 Mahata, D. et al. (2018). Detecting Personal Intake of Medicine from Twitter. IEEE Intelligent Systems, 33(4), 87-95. https://doi.org/10.1109/MIS.2018.043741326 Monroy-Hernández, A., Kiciman, E. and Counts, S. (2015). Narcotweets: Social Media in Wartime. Artificial Intelligence, 515-518. Nahili, W. and Rezeg, K. (2018). Digital Marketing with Social Media: What Twitter Says! In 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS), Tebessa, Algeria. https://doi.org/10.1109/PAIS.2018.8598515 Nguyen, H.-L. and Jung, J.E. (2018). SocioScope: A Framework for Understanding Internet of Social Knowledge. Future Generation Computer Systems, 83, 358-365. https://doi.org/10.1016/j.future.2018.01.064 Ottoni, R. et al. (2018). Analyzing Right-Wing Youtube Channels: Hate, Violence and Discrimination. In Proceedings of the 10th ACM Conference on Web Science. https://doi.org/10.1145/3201064.3201081 Patankar, A., Kshama, K. and Kotrappa, S. (2016). Emotweet: Sentiment Analysis Tool for Twitter. In EEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), Pune, India. https://doi.org/10.1109/ICAECCT.2016.7942573 Ristea, A., Langford, C. and Leitner, M. (2017). Relationships between Crime and Twitter Activity around Stadiums. In 25th International Conference on Geoinformatics, Buffalo, USA. https://doi.org/10.1109/GEOINFORMATICS.2017.8090933 Salas-Zárate, M. et al. (2020). Review of English Literature on Figurative Language Applied to Social Networks. Knowledge and Information Systems, 62(6), 2105-2137. https://doi.org/10.1007/s10115-019-01425-3 Saha, K. and De Choudhury, M. (2017). Modeling Stress with Social Media around Incidents of Gun Violence on College Campuses. In Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3134727 Senapati, M., Njilla, L. and Rao, P. (2019). A Method for Scalable First-Order Rule Learning on Twitter Data. In IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Macao, China. https://doi.org/10.1109/ICDEW.2019.000-1 Singh, A., Shukla, N. and Mishra, N. (2018). Social Media Data Analytics to Improve Supply Chain Management in Food Industries. Transportation Research Part E: Logistics and Transportation Review, 114, 398-415. https://doi.org/10.1016/j.tre.2017.05.008 Xie, J. and Yang, T. (n.d.). Using Social Media Data to Enhance Disaster Response and Community. In International Workshop on Big Geospatial Data and Data Science (BGDDS), Wuhan, China. |
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An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico 7 Revista Perspectiva Empresarial Artículo de revista Núm. 2 Supl.1 , Año 2020 : “1th International Workshop on Enterprise Decision-Making Applying Artificial Intelligence Techniques (WEDMAIT 2020)” 2 Supl.1 Sabaneta: Fundación Universitaria Ceipa, 2014- Paredes Valverde, Mario Andrés This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitan Sánchez Cervantes, José Luis Suárez Gutiérrez, Manuel info:eu-repo/semantics/article https://revistas.ceipa.edu.co/index.php/perspectiva-empresarial/article/view/665 Inglés https://creativecommons.org/licenses/by-nc-sa/4.0/ Revista Perspectiva Empresarial - 2020 Agarwal, B., Ravikumar, A. and Saha, S. (2017). A Novel Approach to Big Data Veracity Using Crowdsourcing Techniques and Bayesian Predictors. In 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, USA. https://doi.org/10.1109/ICMLA.2016.0184 Al-Hajjar, D. and Syed, A. (2015). Applying Sentiment and Emotion Analysis on Brand Tweets for Digital Marketing. In IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Amman, Jordan. https://doi.org/10.1109/AEECT.2015.7360592 Ashwin, K., Kammarpally, P. and George, K. (2016). Veracity of Information in Twitter Data: A Case Study. In International Conference on Big Data and Smart Computing (BigComp), Hong Kong, China. https://doi.org/10.1109/BIGCOMP.2016.7425811 Baydogan, C. and Alatas, B. (2018). Sentiment Analysis Using Konstanz Information Miner in Social Networks. In 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, Turkey. https://doi.org/10.1109/ISDFS.2018.8355395 Brogueira, G., Batista, F. and Carvalho, J.P. (2016). Using Geolocated Tweets for Characterization of Twitter in Portugal and the Portuguese Administrative Regions. Social Network Analysis and Mining, 6(1), 1-20. https://doi.org/10.1007/s13278-016-0347-8 Bustos López, M. et al. (2018). EduRP: An Educational Resources Platform Based on Opinion Mining and Semantic Web. Journal of Universal Computer Science, 24(11), 1515-1535. Devraj, N. and Chary, M. (2015). How Do Twitter, Wikipedia, and Harrison's Principles of Medicine Describe Heart Attacks? In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, Atlanta, Georgia. https://doi.org/10.1145/2808719.2812591 Garg, P., Garg, H. and Ranga, V. (2017). Sentiment Analysis of the Uri Terror Attack Using Twitter. In International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India. https://doi.org/10.1109/CCAA.2017.8229812 Khatua, A., Cambria, E. and Khatua, A. (2018). Sounds of Silence Breakers: Exploring Sexual Violence on Twitter. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain. https://doi.org/10.1109/ASONAM.2018.8508576 Kim, Y., Hwang, E. and Rho, S. (2016). Twitter News-in-Education Platform for Social, Collaborative, and Flipped Learning. The Journal of Supercomputing, 74(8), 3564-3582. https://doi.org/10.1007/s11227-016-1776-x Kononenko, O. et al. (2014). Mining Modern Repositories with Elasticsearch. In Proceedings of the 11th Working Conference on Mining Software Repositories, Chicago, USA. https://doi.org/10.1145/2597073.2597091 Langi, P. et al. (2016). An Evaluation of Twitter River and Logstash Performances as Elasticsearch Inputs for Social Media Analysis of Twitter. In International Conference on Information & Communication Technology and Systems (ICTS), Surabaya, Indonesia. https://doi.org/10.1109/ICTS.2015.7379895 Lee, R., Wakamiya, S. and Sumiya, K. (2013). Urban Area Characterization Based on Crowd Behavioral Lifelogs over Twitter. Personal and Ubiquitous Computing, 17(4), 605-620. https://doi.org/10.1007/s00779-012-0510-9 Mahata, D. et al. (2018). Detecting Personal Intake of Medicine from Twitter. IEEE Intelligent Systems, 33(4), 87-95. https://doi.org/10.1109/MIS.2018.043741326 Monroy-Hernández, A., Kiciman, E. and Counts, S. (2015). Narcotweets: Social Media in Wartime. Artificial Intelligence, 515-518. Nahili, W. and Rezeg, K. (2018). Digital Marketing with Social Media: What Twitter Says! In 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS), Tebessa, Algeria. https://doi.org/10.1109/PAIS.2018.8598515 Nguyen, H.-L. and Jung, J.E. (2018). SocioScope: A Framework for Understanding Internet of Social Knowledge. Future Generation Computer Systems, 83, 358-365. https://doi.org/10.1016/j.future.2018.01.064 Ottoni, R. et al. (2018). Analyzing Right-Wing Youtube Channels: Hate, Violence and Discrimination. In Proceedings of the 10th ACM Conference on Web Science. https://doi.org/10.1145/3201064.3201081 Patankar, A., Kshama, K. and Kotrappa, S. (2016). Emotweet: Sentiment Analysis Tool for Twitter. In EEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), Pune, India. https://doi.org/10.1109/ICAECCT.2016.7942573 Ristea, A., Langford, C. and Leitner, M. (2017). Relationships between Crime and Twitter Activity around Stadiums. In 25th International Conference on Geoinformatics, Buffalo, USA. https://doi.org/10.1109/GEOINFORMATICS.2017.8090933 Salas-Zárate, M. et al. (2020). Review of English Literature on Figurative Language Applied to Social Networks. Knowledge and Information Systems, 62(6), 2105-2137. https://doi.org/10.1007/s10115-019-01425-3 Saha, K. and De Choudhury, M. (2017). Modeling Stress with Social Media around Incidents of Gun Violence on College Campuses. In Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3134727 Senapati, M., Njilla, L. and Rao, P. (2019). A Method for Scalable First-Order Rule Learning on Twitter Data. In IEEE 35th International Conference on Data Engineering Workshops (ICDEW), Macao, China. https://doi.org/10.1109/ICDEW.2019.000-1 Singh, A., Shukla, N. and Mishra, N. (2018). Social Media Data Analytics to Improve Supply Chain Management in Food Industries. Transportation Research Part E: Logistics and Transportation Review, 114, 398-415. https://doi.org/10.1016/j.tre.2017.05.008 Xie, J. and Yang, T. (n.d.). Using Social Media Data to Enhance Disaster Response and Community. In International Workshop on Big Geospatial Data and Data Science (BGDDS), Wuhan, China. Social media http://purl.org/coar/resource_type/c_6501 data analysis http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text info:eu-repo/semantics/publishedVersion Publication pattern recognition violence image/png application/pdf perception semantics This paper describes the methodology and the model that used in Twitter to create an indicator that allows us to denote a social perception about violence, a topic of high impact in Mexico. We investigated and validated the keywords that Mexicans used related to this topic, in a specific time-lapse defined by the researchers. We implemented two analysis levels, the first one relative to the sum of tweets, and the second one with a rate of total tweets per 100,000 inhabitan Journal article An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico 2389-8186 2020-12-01T00:00:00Z https://revistas.ceipa.edu.co/index.php/perspectiva-empresarial/article/download/665/933 https://revistas.ceipa.edu.co/index.php/perspectiva-empresarial/article/download/665/942 2020-12-01T00:00:00Z 18 6 2020-12-01 https://doi.org/10.16967/23898186.665 10.16967/23898186.665 2389-8194 |