Identification of Author Profiles Through Social Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1245)


The aim of this paper is to compile dictionaries of slang words, abbreviations, contractions, and emoticons to help the pre-processing of texts published in social networks. The use of these dictionaries is intended to improve the results of the tasks related to data obtained from these platforms. Therefore, a hypothesis was evaluated in the task of identifying author profiles (author profiling).


Lexicon Social networks Author profiling Text classification 


  1. 1.
    Schler J, Koppel M, Argamon S, Pennebaker JW (2006) Effects of age and gender on blogging. In: Computational approaches to analyzing weblogs, papers from the 2006 AAAI spring symposium, technical report SS-06-03, Stanford, California, USA, 27–29 Mar 2006, pp 199–205Google Scholar
  2. 2.
    Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (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, ChamGoogle Scholar
  3. 3.
    Tang J (2016) AMiner: mining deep knowledge from big scholar data. In: Proceedings of the 25th international conference companion on world wide web, international world wide web conferences steering committee, republic and canton of Geneva, Switzerland, pp 373–373Google Scholar
  4. 4.
    Obit JH, Ouelhadj D, Landa-Silva D, Vun TK, Alfred R (2011) Designing a multi-agent approach system for distributed course timetabling. pp 103–108
  5. 5.
    Lewis MRR (2006) Metaheuristics for university course timetabling. Ph.D. Thesis, Napier UniversityGoogle Scholar
  6. 6.
    Deng X, Zhang Y, Kang B, Wu J, Sun X, Deng Y (2011) An application of genetic algorithm for university course timetabling problem. pp 2119–2122
  7. 7.
    Mahiba AA, Durai CAD (2012) Genetic algorithm with search bank strategies for university course timetabling problem. Procedia Eng 38:253–263CrossRefGoogle Scholar
  8. 8.
    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Know Data Eng 17:734–749CrossRefGoogle Scholar
  9. 9.
    Camacho-Vázquez V, Sidorov G, Galicia-Haro SN (2016) Machine learning applied to a balanced and emotional corpus of tweets with many varieties of Spanish. SubmittedGoogle Scholar
  10. 10.
    Nguyen K, Lu T, Le T, Tran N (2011) Memetic algorithm for a university course timetabling problem. pp 67–71.
  11. 11.
    Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. In: Procedia computer science first international conference on information technology and quantitative management vol 17, pp 26–32Google Scholar
  12. 12.
    Hemalatha I, Varma DGPS, Govardhan DA (2012) Preprocessing the informal text for efficient sentiment analysis. Int J Emerg Trends Technol Comput Sci (IJETTCS) 1(2):58–61Google Scholar
  13. 13.
    Pinto D, Vilarinõ-Ayala D, Alemán Y, Gómez-Adorno H, Loya N, Jiménez-Salazar H (2012) The soundex phonetic algorithm revisited for sms-based information retrieval. In: II Spanish conference on information retrieval CERI 2012Google Scholar
  14. 14.
    Torres-Samuel M, Vásquez C, Viloria A, Lis-Gutiérrez JP, Borrero TC, Varela N (2018) Web visibility profiles of Top100 Latin American universities. International conference on data mining and big data. Springer, Cham, pp 254–262CrossRefGoogle Scholar
  15. 15.
    Henao-Rodríguez C, Lis-Gutiérrez JP, Bouza C, Gaitán-Angulo M, Viloria A (2019) Citescore of publications indexed in scopus: an implementation of panel data. International conference on data mining and big data. Springer, Singapore, pp 53–60CrossRefGoogle Scholar
  16. 16.
    Peersman C, Daelemans W, Van Vaerenbergh L (2011) Predicting age and gender in online social networks. In: Proceedings of the 3rd international workshop on search and mining user-generated contents. New York, NY, USA, ACM, pp 37–44Google Scholar
  17. 17.
    Nguyen D, Gravel R, Trieschnigg D, Meder T (2013) how old do you think i am?: a study of language and age in twitter. In: Proceedings of the seventh international AAAI conference on weblogs and social media. ICWSMGoogle Scholar
  18. 18.
    Rangel F, Rosso P (2013) Use of language and author profiling: Identification of gender and age. In: Proceedings of the 10th workshop on natural language processing and cognitive science (NLPCS-2013)Google Scholar
  19. 19.
    Bedford D (2013) Evaluating classification schema and classification decisions. Bull Am Soc Inform Sci Technol 39:13–21CrossRefGoogle Scholar
  20. 20.
    Toutanova K, Klein D, Manning C, Singer Y (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: Human language technology conference (HLT-NAACL 2003)Google Scholar
  21. 21.
    McGrail MR, Rickard CM, Jones R (2006) Publish or perish: a systematic review of interventions to increase academic publication rates. Higher Educ Res Dev 25:19–35CrossRefGoogle Scholar
  22. 22.
    Costas R, van Leeuwen TN, Bordons M (2010) A bibliometric classificatory approach for the study and assessment of research performance at the individual level: the effects of age on productivity and impact. J Am Soc Inf Sci 61:1564–1581CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Universidad de la CostaBarranquillaColombia
  2. 2.Universidad de la CostaBarranquillaColombia
  3. 3.Universidad LibreSan Pedro SulaHonduras
  4. 4.Corporación Universitaria Minuto de Dios—UniminutoBarranquillaColombia

Personalised recommendations