This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called “following”, in which each user can choose who she wants to “follow” to receive tweets from without

in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make

ways, and that influential users tend to be individuals who express negative sentiment in part of their tweets. These findings suggest that the popularity and influence of a Twitter account cannot be simply traced back to the graph properties of the network within which it is embedded, but also depends

In this paper we investigate the attributes and relative influence of 1.6M Twitter users by tracking 74 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we find that the largest cascades tend to be generated by users who have

The problem of spreading information is a topic of consid-erable recent interest, but the traditional influence maxi-mization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbi

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

ABSTRACT Twitter offers an explicit mechanism to facilitate information diffusion and has emerged as a new medium for communication. Many approaches to find influentials have been proposed, but they do not consider the temporal order of information adoption. In this work, we propose a novel method

in nature as it evolves over period of time by adding or deleting nodes and edges. The study of influential members in a social network is an important research question in social network analysis. In order to find the most influential person, the most central node has to be identified. Centrality

the relationship between personality and use of social media. Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials. For 335 users, we gather

Abstract-Twitter is the most popular real-time microblogging service and it is a platform where users provide and obtain information at rapid pace. In this scenario, one of the biggest challenge is to find a way to automatically identify the most influential users of a given topic. Currently