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Perception of Trend Topic in Twitter: A Case Study

Tejasree Samakoti*, Shaik Naseera**, Tella Pramila***

* Research Scholar, School of Computer Science and Engineering (SCOPE), VIT University, Vellore, Tamilnadu, India.

** Associate Professor, School of Computer Science and Engineering (SCOPE), VIT University, Vellore, Tamilnadu, India.

*** Postgraduate, SV Engineering College, Tirupathi, Andhra Pradesh, India.

Periodicity:April - June'2017
DOI : https://doi.org/10.26634/jse.11.4.13816

Abstract

One had to perceive the trend topic in Twitter concentrated on how the particular topic is trending suddenly. Based on factors like that coverage, popularity and reputation, etc., Kalman filter method is used for finding how the topic is trending. Existing methods concentrated on trend detection, trend taxonomy. But the disadvantage is based on Google trend manipulation, where a malicious user have the facility to manipulate Twitter trends. To overcome the disadvantage of existing method dynamic factors are provided for knowing a trending popular topic. A brief description about related terms used in Twitter was also depicted. In this work, the admin has the facility to view the end users and friend requests by the new users, new tweet given by the user, also display the hash tags of the number and also detecting the positive and negative words along with the number of words, to find top frequent tags detecting fake tweets given by the people, and also capturing IP address of the particular system.

Keywords

Twitter, Topic Trending, Malicious Accounts, SVM Classification

How to Cite this Article?

Samakoti, T., Naseera, S., and Pramila, T. (2017). Perception of Trend Topic in Twitter: A Case Study. i-manager’s Journal on Software Engineering, 11(4), 12-17. https://doi.org/10.26634/jse.11.4.13816

References

[1]. Agarwal, M. K., Ramamritham, K., & Bhide, M. (2012). Real time discovery of dense clusters in highly dynamic graphs: Identifying real world events in highly dynamic environments. Proceedings of the VLDB Endowment, 5(10), 980-991.

[2]. Becker, H., Naaman, M., & Gravano, L. (2011). Beyond Trending Topics: Real-World Event Identification on Twitter. ICWSM, 11(2011), 438-441.

[3]. Cover, T. M., & Thomas, J. A. (2012). Elements of Information Theory. John Wiley & Sons.

[4]. Elder, J. (2013). Inside a Twitter Robot Factory. In Wall Street Journal. Retrieved from https://www.wsj.com/ articles/bogus-accounts-dog-twitter-1385335134

[5]. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.

[6]. Just, M. R., Crigler, A. N., Metaxas, P. T., & Mustafaraj, E. (August, 2012). 'It's Trending on Twitter'-An Analysis of the Twitter Manipulations in the Massachusetts 2010 Special Senate Election. APSA 2012 Annual Meeting Paper.

[7]. Kasiviswanathan, S. P., Melville, P., Banerjee, A., & Sindhwani, V. (2011, October). Emerging topic detection th using dictionary learning. In Proceedings of the 20 ACM International Conference on Information and Knowledge Management (pp. 745-754). ACM.

[8]. Lee, K., Palsetia, D., Narayanan, R., Patwary, M. M. A., Agrawal, A., & Choudhary, A. (2011, December). Twitter trending topic classification. In Data Mining Workshops th (ICDMW), 2011 IEEE 11 International Conference on (pp. 251-258). IEEE.

[9]. Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151.

[10]. Lu, R., Xu, Z., Zhang, Y., & Yang, Q. (2012). Life activity modeling of news event on twitter using energy function. In: Tan PN., Chawla S., Ho C.K., Bailey J. (Eds) Advances in Knowledge Discovery and Data Mining (Vol. 7302, pp. 73- 84). PAKDD 2012. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg.

[11]. Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013, June). Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp.400-408).

[12]. Naaman, M., Becker, H., & Gravano, L. (2011). Hip and trendy: Characterizing emerging trends on Twitter. Journal of the Association for Information Science and Technology, 62(5), 902-918.

[13]. Nikolov, S. (2012). Trend or no trend: A novel nonparametric method for classifying time series (Doctoral Dissertation, Massachusetts Institute of Technology).

[14]. Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Patil, S., Flammini, A. et al. (2010). Detecting and tracking the spread of Astroturf memes in microblog streams. arXiv preprint arXiv:1011.3768.

[15]. Zubiaga, A., Spina, D., Fresno, V., & Martínez, R. (2011, October). Classifying trending topics: A typology of th conversation triggers on twitter. In Proceedings of the 20 ACM International Conference on Information and Knowledge Management (pp. 2461-2464). ACM.

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