By Sameep Mehta, L. V. Subramaniam (auth.), Vasudha Bhatnagar, Srinath Srinivasa (eds.)

ISBN-10: 3319036882

ISBN-13: 9783319036885

ISBN-10: 3319036890

ISBN-13: 9783319036892

This ebook constitutes the completely refereed convention court cases of the second one foreign convention on monstrous facts Analytics, BDA 2013, held in Mysore, India, in December 2013. The thirteen revised complete papers have been conscientiously reviewed and chosen from forty nine submissions and canopy subject matters on mining social media facts, views on substantial facts research, graph research, large facts in practice.

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Additional resources for Big Data Analytics: Second International Conference, BDA 2013, Mysore, India, December 16-18, 2013, Proceedings

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They are informal and similar in structure and syntax [13]. An exhaustive feature set considering stylometric information is built for our experiment, however with an assumption that the authors unconsciously follow a specific pattern and are consistent in their choices [12]. Various broad categories of the features are as follows: 1. Lexical Features: (a) Total number of words per tweet (b) Total number of sentences (c) Total number of words per sentences (d) Frequency of dictionary words (e) Frequency of word extensions (f) Lexical diversity (g) Mimicry by length (h) Mimicry by word Stylometric Analysis for Authorship Attribution on Twitter 41 Even though tweets are really short texts, users would manage to write them using dictionary words and framing proper sentences (features b, c, d).

To identify key users in these regimes, they use a random user as a baseline. We however, define surprising activity by a user based on his own behavior as opposed to comparing him with a random user. Yang and Leskovec [3] explore the temporal patterns of popularity of online content. Kleinberg et al [4] study evolution of topics on the blogosphere. They consider two main ingredients in modeling dynamics of news cycle - imitation and recency. Our definition of copy takes both into account. Further, instead of the users opting for recent topics, the Twitter timeline forces them to copy only from the recent past.

5 Diffusion of a Topic The spread of a topic is essential to evaluate its true popularity. While certain topics like natural disasters or panic events can make numerous people independently tweet on the topic simultaneously, for most of the other topics, the spread is by word of mouth. Many studies on RTs, @ mentions etc. have been 32 M. Jain et al. 1 1 10 Fraction of copies 100000 0 0 10 20 30 40 50 Day 60 70 (c) FOLLOW FRIDAY 80 90 0 1000 0 0 10 20 30 40 Day 50 60 70 80 0 (d) DEMI LOVATO Fig. 4.

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Big Data Analytics: Second International Conference, BDA 2013, Mysore, India, December 16-18, 2013, Proceedings by Sameep Mehta, L. V. Subramaniam (auth.), Vasudha Bhatnagar, Srinath Srinivasa (eds.)


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