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To Find Influential's in Twitter based on Information Propagation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 118 - Number 13 |
| Year of Publication: 2015 |
| Authors: Md Tabrez Nafis, Alok K Pathak |
10.5120/20805-3507
|
Md Tabrez Nafis, Alok K Pathak . To Find Influential's in Twitter based on Information Propagation. International Journal of Computer Applications. 118, 13 ( May 2015), 22-25. DOI=10.5120/20805-3507
Abstract
Identification of Influencers in Social Networks and targeting them for viral marketing is fast becoming the core idea in designing the strategies for brand building. The influential individuals stimulate "word-of-mouth" effects in their network[14]. This leads to triggering of long cascades of influence that convince their peers to perform a similar action (Participate in Survey , click on advertisement, 'like' a community etc). Targeting these influentials usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network. Last five years have seen tremendous growth of social media People having access to Internet are spending considerable amount of time on various platforms generating huge data content by participating in survey, approving a thought, endorsing some view, building social profile and sharing knowledge. This has resulted in social influence emerging as a complex force, governing the diffusion of the influence in the network. Individual's social influence on the network tempts various companies to go beyond the conventional marketing for finding new customers, thereby making profitable marketing decisions. In this work we have examined the identification of Influencials in the increasingly popular online social network, Twitter using Google's PageRank Algorithm [16] and have optimized the calculation of ranking score by taking into account the dynamism of 'Retweet' or 'Mention' functionality used by the twitter users which actually signify their actual participation in the idea propagation and subsequent cascade of influence in their network. The idea is to improve further on the intuition that the users with most number of followers are also the most influential in the given network. The improved PageRank algorithm not only considers the number of followers the user has , but also the number of times their tweet have been retweeted or their user name have been mentioned in the their follower's tweets.
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