In are performing their analysis on so obtained

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In today’s world, Twitter has become a famous microblogging service in which users can write their opinion in 280 characters. Users can follow someone whose tweets he wants to see in news feed. In this way, a network of users known as followers network is created. Users can share tweet of another user with their own followers. In twitter, the fundamental aspect of information diffusion, known as act of retweeting, allows the users to propagate someone else’s tweet to their own followers. This can be accomplished in two ways. First, user can retweet by manually copying the tweet and inserting RT in ahead of text and acknowledging the original author with @. Second, The users can click the official retweet button to share the original content or by adding more text to tweet to make it quote tweet. The diffusion network obtained is known as retweet network. Research is going on in the field of analysing the diffusion network and based on that observation prediction of future spreading process. To analyse the diffusion process, a network should be created from twitter data. But actually the creation of this network is itself a challenging task. This is because, twitter does not provide the exact diffusion paths. It provides information about original user (user who posted the tweet) and all the users who retweeted the original tweet. But there is not any information of intermediate users who have influenced the users to retweet. Data obtained from twitter gives all the credit to source user. But in actual, there are many intermediate users who are more important than root user for information diffusion. That means twitter doesn’t provide information about diffusion paths. So most of the researchers are creating retweet network by giving credit to the source user and are performing their analysis on so obtained network. In order to analyse the diffusion paths, this original network should be converted into a chain network which can reveal the diffusion paths and the important users for diffusion process. Little attention has been paid on finding actual diffusion paths and who influences whom information on Twitter. So in this paper we proposed a method which helps to find out the diffusion paths and able to inference about who influenced whom information. Our method infers possible diffusion paths by combining the original retweet network and the underlying follower network. After creation of approximate retweet network, we are able to answer the following questions:

Categories: Data


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