Análise visual do Twitter

Autores

  • Elias Estevao Goulart
  • Sidney Fels University of British Columbia

DOI:

https://doi.org/10.13037/ci.vol15n29.2931

Palavras-chave:

inovação, trecnologias digitais, Twitter], Folkson

Resumo

A mídia social tornou-se um canal muito importante de comunicação e interação para pessoas de todo o mundo e uma grande quantidade de conteúdo está sendo criado. Como resultado, o processo de análise de tal enorme quantidade de dados requer o suporte de ferramentas e técnicas de visualização. Este estudo est[a centrdoa nas relações entre as palavras postadas no Twitter, usando o princípio da Folksonomia para categorizar as palavras mais recorrentes como etiquetas (ou tags). Além disso, ele propõe um modelo visual baseado no princípio de atração física que tem como objetivo mostrar a maneira que as principais etiquetas estão correlacionadas. Os resultados indicam o potencial do Modelo Orbital, porque pode ser utilizado para representar a dinâmica das relações ao longo do tempo.

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Biografia do Autor

Elias Estevao Goulart

Professor do PPGcom da USCS

Referências

Becker, H., Naaman, M. & Gravano, L. (2011). “Beyond trending topics: real-world event identification on

Twitter”. In: Proceedings of Fifth International AAAI Conference on Weblogs and Social Media.

Barcelona, p. 438-441.

Boguta, K. (2009). “Evolution of a revolution: visualizing millions of Iran tweets”. ReadWriteWeb.

Retrieved June, 2012, from http://www.readwriteweb.com/archives/evolution_revolution_visualizing_millions_iran_tweets.php

Boyd, D. & Crawford, K. (2012). “Critical questions for big data”. Information, Communication & Society,

15 (5), 662–679.

Cantadora, I., Konstas, I. & Josec, J. M. (2011). “Categorising social tags to improve folksonomia-based

recommendations”. Web Semantics: Science, Services and Agents on the World Wide Web, 9,

1–15.

Chen, I.-X. & Yang, C.-Z. (2010). “Visualization of social networks”. In: B. Furht (ed.). Handbook of

Social Network Technologies and Applications: Springer Science+Business Media.

Cheong, M. & Lee, V. C. S. (2010). “Twitmographics: learning the emergent properties of the Twitter community”. In: From Sociology to Computing in Social Networks, 323-342: Springer-Verlag.

1_[Dos1].indd 20 06/11/14 16:44

21 Comunicação & Inovação, PPGCOM/USCS

v. 15, n. 29 (7-22) jul-dez 2014

Análise visual do Twitter

______. (2011). “A microblogging-based approach to terrorism informatics: exploration and chronicling

civilian sentiment and response to terrorism events via Twitter”. Information Systems Frontiers,

13, 45–59.

Fleishman, J. (2009). “Mideast hanging on every text and tweet from Iran”. Los Angeles Times. Retrieved

May, 2012, from http://articles.latimes.com/2009/jun/17/world/fg-iran-image17

Gilbert, E., Karahalios, K. & Sandvig, C. (2010). “The network in the garden: designing social media for

rural life”. American Behavioral Scientist, 53(9), 1367–1388: SAGE Publications.

Go, A., Bhayani, R. & Huang, L. (2009). “Twitter sentiment classification using distant supervision”.

Stanford University. Retrieved June, 2012, from http://www.stanford.edu/~alecmgo/papers/

TwitterDistantSupervision09.pdf

Goolsby, R. (2009). “Lifting elephants: Twitter and blogging in a global perspective”. In: Social computing

and behavioral modeling: Springer-Verlag.

Gupta, M., Li, R., Yin, Z. & Han, J. (2011). “An overview of social tagging and applications”. In: Aggarwal

, C. C. (ed.). Social Network Data Analytics: Springer Science+Business Media.

Highfield, T., Kirchhoff, L. & Nicolai, T. (2011). “Challenges of tracking topical discussion networks

online”. Social Science Computer Review, 29(3), 340-353.

Himelboim, I., Hansen, D. & Bowser, A. (2012). “Playing in the same Twitter network. Information”,

Communication & Society. DOI: 0.1080/1369118X.2012.706316.

Jansen, B. J., Zhang, M., Sobel, K. & Chowdury, A. (2009). “Micro-blogging as online word of mouth

branding”. Conference on Human Factors in Computing Systems (CHI 2009) (pp. 3859-3864).

Boston, MA: ACM.

Java, A., Song, X., Finin, T. & Tseng, B. (2007). “Why we Twitter: understanding microblogging usage

and communities”. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web

mining and social network analysis, pp. 56-65, 2007. Proceedings of the 9th WEBKDD and 1st

SNA-KDD 2007, (pp.118-138): San Jose, CA.

Jin, Y., Li, R., Wen, K., Gu, X. & Xiao, F. (2011). “Topic-based ranking in folksonomia via probabilistic

model”. Artificial Intelligence Review, 36, 139–151.

Jungherr, A. (2009). “The DigiActive guide to Twitter for activism”. Retrieved May, 2012, from http://

tinyurl.com/6tr2pfp

Kakali, C. & Papatheodorou, C. (2010). “Exploitation of folksonomies in subject analysis”. Library &

Information Science Research, 32, 192–202.

Karpinski, R. (2009). Twitter tools. B to B, vol. 94, n. (2), 2009, pp. 15-20.

Kawano, Y., Kishimoto, Y. & Yonekura, T. (2011). “A Prototype of Attention Simulator on Twitter”.

International Conference on Network-Based Information Systems. IEEE Computer Society.

Kim, H.-N., Rawashdeh, M., Alghamdi, A. & El Saddik, A. (2012). “Folksonomia-based personalized

search and ranking in social media services”. Information Systems, 37, 61–76.

1_[Dos1].indd 21 06/11/14 16:44

22 Comunicação & Inovação, PPGCOM/USCS

v. 15, n. 29 (7-22) jul-dez 2014

Elias Estevão Goulart & Sidney Fels

Lin, J. & Dyer, C. (2010). Data-Intensive Text Processing with MapReduce. University of Maryland:

College Park.

Liu, B. (2010). “Sentiment analysis and subjectivity”. In: Indurkhya, N & Damerau F. J. (Eds). Handbook

of Natural Language Processing (pp. 627-666). Boca Raton, FL: Chapman & Hall/CRC.

López-Juárez, P. & Olivas, J. A. (2011). “Intentional tags in folksonomia based ranking systems”. The 2011

World Congress in Computer Science, Computer Engineering, and Applied Computing. Retrieved

June, 2012, from http://world-comp.org/p2011/ICA5049.pdf

Luo, X., Ouyang, Y. & Xiong, Z. (2012). “Improving neighborhood based Collaborative Filtering via integrated folksonomia information”. Pattern Recognition Letters, 33, 263–270.

MerlesWorld. A Beginner’s Guide to Using & Marketing with Twitter. Retrieved Disponível em: June, 2012,

from de http://www.merlesworld.com/e-books/Twitter_Fast_Start.pdf. Acesso em junho de 2012.

Naaman, M., Becker, H. & Gravano, L. (2011). “Hip and trendy: characterizing emerging trends on Twitter”.

Journal of the American Society for Information Science and Technology, 62(5), 902–918.

Nicholls, J. (2012). “Everyday, everywhere: alcohol marketing and social media - current trends”. Alcohol

and Alcoholism. DOI:10.1093/alcalc/ags043.

Pang, B. & Lee, L. (2008). “Opinion mining and sentiment analysis”. Foundations and Trends in Information

Retrieval, 2 (1), 1–135.

Ran, Z. & Erpeng, J. (2011). “Folksonomia - based library information organization in China”. International

Conference on Business Management and Electronic Information (BMEI), 5, 270-272.

Romero, D. M., Meeder, B. & Kleinberg, J. (2011). “Differences in the mechanics of information diffusion

across topics: idioms, political hashtags, and complex contagion on Twitter”. International World

Wide Web Conference (WWW 2011): India.

Schmitz,C., Hotho, A., Jaschke, R. & Stumme, G. (2006). “Mining association rules in folksonomies”. In:

Data Science and Classification: Proceedings of the 10th IFCS Conference (pp. 261-270), Studies

in Classification, Data Analysis, and Knowledge Organization: Springer.

Shearman, S. (2012). “Twitter’s branded venture”. Marketing, 10-11.

Skold, M. (2008). Social network visualization. Master Thesis. Royal Institute of Technology: Sweden.

Thelwall, M., Buckley, K. & Paltoglou, G. (2011). “Sentiment in Twitter events”. Journal of the American

Society for Information Science and Technology, 62(2), 406-418.

Tsytsauru, M. & Palpanas, T. (2012). “Survey on mining subjective data on the web”. Data Mining and

Knowledge Discovery, 24(3), 478-514.

Wilson, V. “Research methods: content analysis”. Evidence Based Library and Information Practice, 6(4),

177-179.

Zhang, L., Ghosh, R., Dekhil, M., Hsu, M. & Liu, B. (2011). “Combining lexicon based and learning-based

methods for twitter sentiment analysis”. Technical Report HPL-2011-89: HP.

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Publicado

2014-11-26

Como Citar

Goulart, E. E., & Fels, S. (2014). Análise visual do Twitter. Comunicação & Inovação, 15(29), 7–22. https://doi.org/10.13037/ci.vol15n29.2931