Method for collecting relevant topics from twitter supported by Big Data
There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloa...
- Autores:
-
silva d, jesus g
Senior Naveda, Alexa
GAMBOA SUAREZ, RAMIRO
Hernández Palma, Hugo
Niebles Nuñez, William
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
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- Acceso en línea:
- https://hdl.handle.net/11323/5961
https://repositorio.cuc.edu.co/
- Palabra clave:
- Collection methods
Big Data
Twitter
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Method for collecting relevant topics from twitter supported by Big Data |
| title |
Method for collecting relevant topics from twitter supported by Big Data |
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Method for collecting relevant topics from twitter supported by Big Data Collection methods Big Data |
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Method for collecting relevant topics from twitter supported by Big Data |
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Method for collecting relevant topics from twitter supported by Big Data |
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Method for collecting relevant topics from twitter supported by Big Data |
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Method for collecting relevant topics from twitter supported by Big Data |
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Method for collecting relevant topics from twitter supported by Big Data |
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silva d, jesus g Senior Naveda, Alexa GAMBOA SUAREZ, RAMIRO Hernández Palma, Hugo Niebles Nuñez, William |
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silva d, jesus g Senior Naveda, Alexa GAMBOA SUAREZ, RAMIRO Hernández Palma, Hugo Niebles Nuñez, William |
| dc.subject.spa.fl_str_mv |
Collection methods Big Data |
| topic |
Collection methods Big Data |
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There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics. |
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2020 |
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[1] Amelec, V., & Carmen, V. (2015). Relationship Between Variables of Performance Social and Financial of Microfinance Institutions. Advanced Science Letters, 21(6), 1931-1934. [2] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [3] Guyon, I., Elisseeff, A., An introduction to variable and feature selection, Journal of machine learning research, 3, 2003, pp. 1157-1182. [4] Kohavi, R., John, G., Wrappers for feature subset selection, Artificial Intelligence Journal, Special issue on relevance, 1997, pp. 273-324. [5] Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M.: Inice: A New Approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.034 [6] Nic Newman, William H Dutton, Grant Blank: Social media in the changing ecology of news: The fourth and fifth estates in britain. InternationalJournalofInternetScience,7(1):6–22, 2012. [7] Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [8] Avery E Holton Hsiang Iris Chyi: News and the overloaded consumer:Factors influencing information overload among news consumers. Cyberpsychology, Behavior, and Social Networking, 15(11):619–624, 2012. [9] Eytan Bakshy, Jake M Hofman, Winter A Mason, Duncan J Watts: Identifying influencers on twitter. In Fourth ACM International Conference on Web Seach and Data Mining (WSDM), 2011. [10] Kathy Lee, Diana Palsetia, Ramanathan Narayanan, Md Mostofa Ali Patwary, Ankit Agrawal, Alok Choudhary: Twitter trending topic classification. In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, pages 251– 258. IEEE, 2011. [11] Leite, R., Brazdil, P., Decisión tree-based attribute selection via subsampling, Workshop de minería de datos y aprendizaje, VIII Iberamia, Sevilla, Spain, Nov, 2002, pp. 77-83. [12] Piramuthu, S., Evaluating feature selection methods for learning in data mining applications, Proc. 31st annual Hawaii Int. conf. on system sciences, 1998, pp. 294-301. [13] Liangjie Hong, Brian D Davison: Empirical study of topic modeling in twitter. In Proceedings of the First Workshop on Social Media Analytics, pages 80–88. ACM, 2010. [14] Ian Porteous, David Newman, Alexander Ihler, Arthur Asuncion, Padhraic Smyth, and Max Welling. Fast collapsed gibbs sampling for latent dirichlet allocation. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 569– 577. ACM, 2008. [15] Kira, K., Rendell, L., The feature selection problem: traditional methods and a new algorithm, Tenth nat. conf. on AI, MIT Press, 1992, pp. 129-134. [16] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371 |
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silva d, jesus gSenior Naveda, AlexaGAMBOA SUAREZ, RAMIROHernández Palma, HugoNiebles Nuñez, William2020-01-30T13:48:37Z2020-01-30T13:48:37Z20201742-65881742-6596https://hdl.handle.net/11323/5961Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.silva d, jesus g-will be generated-orcid-0000-0003-3555-9149-600Senior Naveda, AlexaGAMBOA SUAREZ, RAMIRO-will be generated-orcid-0000-0002-5727-6409-600Hernández Palma, HugoNiebles Nuñez, WilliamengJournal of Physics: Conference Series10.1088/1742-6596/1432/1/012094/pdf[1] Amelec, V., & Carmen, V. (2015). Relationship Between Variables of Performance Social and Financial of Microfinance Institutions. Advanced Science Letters, 21(6), 1931-1934.[2] Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham[3] Guyon, I., Elisseeff, A., An introduction to variable and feature selection, Journal of machine learning research, 3, 2003, pp. 1157-1182.[4] Kohavi, R., John, G., Wrappers for feature subset selection, Artificial Intelligence Journal, Special issue on relevance, 1997, pp. 273-324.[5] Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M.: Inice: A New Approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.034[6] Nic Newman, William H Dutton, Grant Blank: Social media in the changing ecology of news: The fourth and fifth estates in britain. InternationalJournalofInternetScience,7(1):6–22, 2012.[7] Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham[8] Avery E Holton Hsiang Iris Chyi: News and the overloaded consumer:Factors influencing information overload among news consumers. Cyberpsychology, Behavior, and Social Networking, 15(11):619–624, 2012.[9] Eytan Bakshy, Jake M Hofman, Winter A Mason, Duncan J Watts: Identifying influencers on twitter. In Fourth ACM International Conference on Web Seach and Data Mining (WSDM), 2011.[10] Kathy Lee, Diana Palsetia, Ramanathan Narayanan, Md Mostofa Ali Patwary, Ankit Agrawal, Alok Choudhary: Twitter trending topic classification. In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, pages 251– 258. IEEE, 2011.[11] Leite, R., Brazdil, P., Decisión tree-based attribute selection via subsampling, Workshop de minería de datos y aprendizaje, VIII Iberamia, Sevilla, Spain, Nov, 2002, pp. 77-83.[12] Piramuthu, S., Evaluating feature selection methods for learning in data mining applications, Proc. 31st annual Hawaii Int. conf. on system sciences, 1998, pp. 294-301.[13] Liangjie Hong, Brian D Davison: Empirical study of topic modeling in twitter. In Proceedings of the First Workshop on Social Media Analytics, pages 80–88. ACM, 2010.[14] Ian Porteous, David Newman, Alexander Ihler, Arthur Asuncion, Padhraic Smyth, and Max Welling. Fast collapsed gibbs sampling for latent dirichlet allocation. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 569– 577. ACM, 2008.[15] Kira, K., Rendell, L., The feature selection problem: traditional methods and a new algorithm, Tenth nat. conf. on AI, MIT Press, 1992, pp. 129-134.[16] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. 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