Knowledge Gained from Twitter Data
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 1133–1136 (2016)
Abstract. Social media constitute a challenging new source of information for intelligence gathering and decision making. Twitter is one of the most popular social media sites and often becomes the primary source of information. Twitter messages are short and well suited for knowledge discovery. Twitter provides both researchers and practitioners a free Application Programming Interface (API) which allows them to gather and analyse large data sets of tweets. Twitter data are not only tweet texts, as Twitter's API provides more information to perform interesting research studies. The paper briefly describes process of data gathering and the main areas of data mining, knowledge discovery and data visualisation from Twitter data.
- Pearanalitycs, “Twitter study — august 2009,” 2009. http://pearanalytics.com/wp-content/uploads/2009/08/ Twitter-Study-August-2009.pdf
- A. Go, R. Bhayani, and L. Huang, “Twitter sentiment classification using distant supervision,” Processing, pp. 1–6, 2009. http://www.stanford.edu/~alecmgo/papers/TwitterDistantSupervision09.pdf
- F. Morstatter, J. Pfeffer, H. Liu, and K. Carley, “Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose,” in International AAAI Conference on Weblogs and Social Media, 2013. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6071
- S. Kumar, F. Morstatter, and H. Liu, Twitter Data Analytics. Springer, Aug. 2013.
- 44th Hawaii International International Conference on Systems Science (HICSS-44 2011), Proceedings, 4-7 January 2011, Koloa, Kauai, HI, USA, IEEE Computer Society. IEEE Computer Society, January 5-8 2011. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5716643
- D. L. Hansen, M. A. Smith, and B. Shneiderman, “Eventgraphs: Charting collections of conference connections,” in 44th Hawaii International International Conference on Systems Science (HICSS-44 2011), Proceedings, 4-7 January 2011, Koloa, Kauai, HI, USA, IEEE Computer Society. IEEE Computer Society, January 5-8 2011. http://dx.doi.org/10.1109/HICSS.2011.196. ISBN 978-0-7695-4282-9 pp. 1–10.
- B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Found. Trends Inf. Retr., vol. 2, no. 1-2, pp. 1–135, Jan. 2008. http://dx.doi.org/10.1561/1500000011.
- E. Fisher, “Making the most detailed tweet map ever,” 03 2014. https://www.mapbox.com/blog/twitter-map-every-tweet/
- F. B. Viegas, M. Wattenberg, and J. Feinberg, “Participatory visualization with wordle,” Visualization and Computer Graphics, IEEE Transactions on, vol. 15, no. 6, pp. 1137–1144, 2009.
- M. Wattenberg and F. B. Viégas, “The word tree, an interactive visual concordance,” IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 6, pp. 1221–1228, Nov. 2008. http://dx.doi.org/10.1109/TVCG.2008.172.
- M. A. Smith, B. Shneiderman, N. Milic-Frayling, E. M. Rodrigues, V. Barash, C. Dunne, T. Capone, A. Perer, and E. Gleave, “Analyzing (social media) networks with nodexl.” in Proceedings of the 7th Conference on Creativity & Cognition, Berkeley, California, USA, October 26-30, 2009, J. M. Carroll, Ed. ACM, 2009. ISBN 978-1-60558-713-4 pp. 255–264. http://dblp.uni-trier.de/db/conf/candt/candt2009.html#SmithSMRBDCPG09
- D. Hansen, B. Shneiderman, and M. A. Smith, Analyzing Social Media Networks with NodeXL: Insights from a Connected World. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2010. ISBN 0123822297, 9780123822291