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

Karthick D,Dr G Vadivu

doi:10.31142/ijtsrd2366

Abstract

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

Highlights

Online social networking services, such as Facebook, Youtube and Twitter, allow their users to post and share content in the form of posts, images, and videos As a user is exposed to posts of others she follows, the user may in turn reshare a post with her own followers, who may further reshare it with their respective sets of followers
The most direct way to formulate this question is to consider predicting the final size of a information cascade
EXISTING SYSTEM: In present 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. they only kept tweets such that it has at least 50 retweets, the text of the tweet does not contain a pound sign #, and the language of the original poster is English

Summary

INTRODUCTION

Online social networking services, such as Facebook, Youtube and Twitter, allow their users to post and share content in the form of posts, images, and videos As a user is exposed to posts of others she follows, the user may in turn reshare a post with her own followers, who may further reshare it with their respective sets of followers. METHODOLOGY: In this project we are going to formulate the total number of retweets i.e, the popularity of the tweets with the help of SEISMIC (self exciting point process) algorithm .Initially with the help of the twitter application we made an authentication, with the help of Rstudio. In which it requires the following packages such as Seismic, twitter, devtools. In a self-exciting point process, each reshare increases the cumulative count by one, it exposes new followers who may further reshare the post This property is ideal to model the "rich get richer" phenomenon in information spreading. BETWEENESS CENTRALITY: In graph theory, betweeness centrality is a measure of centrality in a graph based on shortest paths

11 Godse7Shankar

CONCLUSION

RESULT