An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico

An Online Social Network model through Twitter to build a social perception variable to measure the violence in Mexico


José Luis Sánchez Cervantes
Mario Andrés Paredes Valverde

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


Los datos de descargas todavía no están disponibles.


Biografía del autor/a / Ver

Manuel Suárez Gutiérrez, Universidad Veracruzana

PhD Engineering in Emerging Technologies

José Luis Sánchez Cervantes, Instituto Tecnológico de Orizaba

PhD in Artificial Intelligence

Mario Andrés Paredes Valverde, Instituto Tecnológico Superior de Teziutlán

PhD in Computer Science


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.

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.

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.

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.

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.

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.

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.

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.

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. Kononenko, O. et al. (2014). Mining Modern Repositories with Elasticsearch. In Proceedings of the 11th Working Conference on Mining Software Repositories, Chicago, USA.

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.

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.

Mahata, D. et al. (2018). Detecting Personal Intake of Medicine from Twitter. IEEE Intelligent Systems, 33(4), 87-95.

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.

Nguyen, H.-L. and Jung, J.E. (2018). SocioScope: A Framework for Understanding Internet of Social Knowledge. Future Generation Computer Systems, 83, 358-365.

Ottoni, R. et al. (2018). Analyzing Right-Wing Youtube Channels: Hate, Violence and Discrimination. In Proceedings of the 10th ACM Conference on Web Science.

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.

Ristea, A., Langford, C. and Leitner, M. (2017). Relationships between Crime and Twitter Activity around Stadiums. In 25th International Conference on Geoinformatics, Buffalo, USA.

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.

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.

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.

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.

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.

Citado por