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

Abstract—Twitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear

-trary k nodes in a network, we have developed a decentral-ized version of the influential maximization problem by influ-encing k neighbors rather than arbitrary users in the entire network. We present several reasonable neighbor selection schemes and evaluate their performance with a real dataset

validity of some implicit attitude measures. Attitudes toward blacks and whites were measured on four separate occasions, each 2 weeks apart, using three relatively implicit measures (re-sponse-window evaluative priming, the Implicit Association Test, and the response-window Implicit Association Test

Twitter is a new web application playing dual roles of online social networking and micro-blogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a

In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on a topic that is of interest to her, and follow

the static and dynamic properties of customers of these markets, and develop and evaluate multiple techniques for detecting these activities. We show that our detection system is robust and reliable, and can detect a significant number of customers in the wild.

In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allow a user to group other users who tend to tweet on a topic that is of interest to her, and follow their collec

Abstract—This paper concerns multi-agent models predicting Twitter trends. We use a step-wise approach to develop a novel agent-based model with the following properties: (1) it uses individual behavior parameters for a set of Twitter users and (2) it uses a retweet graph to model the underlying

lives. This article discusses the core properties of human agency, the different forms it takes, its ontological and epistemological status, its development and role in causal structures, its growing primacy in the coevolution process, and its influ-ential exercise at individual and collective levels