Seismic: A Self-Exciting Point Process Model for Predicting Tweet Popularity using Hashtags IJTSRD


In existing paper they had used a full month of Twitter data to evaluate SEISMIC .In which the original data set contains over 3.2 billion tweets and retweets on Twitter from Octobor 7 to November 7, 2011.Also they only kept tweets such that it has at least 50 retweets, the text of the tweet does not contain a pound sign # (hashtag), and the language of the original poster is English. There are 166,076 tweets satisfying these criteria in the end.So here we are going to propose the mining of tweets with a particular #hashtags and going to formulate the number of retweets in an efficient manner ,so that it will be more efficient in terms of organizing particular categories while mining the popularity of retweets.

by Karthick.D | Dr. G. Vadivu”Seismic: A Self-Exciting Point Process Model for Predicting Tweet Popularity using Hashtags”

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017,

URL: http://www.ijtsrd.com/papers/ijtsrd2366.pdf

http://www.ijtsrd.com/computer-science/data-miining/2366/seismic-a-self-exciting-point-process-model-for-predicting-tweet-popularity-using-hashtags/karthickd

call for paper Computer Hardware, international journal Simulation, science journal




Seismic: A Self-Exciting Point Process Model for Predicting Tweet Popularity using Hashtags IJTSRD IEEE PAPER





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