Identifying communicator roles in Twitter

Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge

Sentiment, richness, authority, and relevance model of information sharing during social Crises—the case of #MH370 tweets

The study introduces a model of crisis information sharing based on Twitter discussions of the missing Malaysian Airlines Flight 370. Grounded in the Elaboration Likelihood Model, the study tests four salient factors: Sentiment, Richness, Authority, and Relevance, which can be measured by peripheral cues in tweets and in user profiles. Findings suggest that information sharing is positively associated with the presence of peripheral cues indicative of a confident, self-revealing and positive emotional language style, and is negatively related to an angry and informal style. Additionally, information sharing is related to the presence of multimedia cues and cues indicating source popularity

Influence of augmented humans in online interactions during voting events

The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive voting event can be used to build an accurate map of real-world political parties and electoral ranks. We provide evidence that information flow and collective attention are often driven by a special class of highly influential users, that we name "augmented humans", who exploit thousands of automated agents, also known as bots, for enhancing their online influence. We show that augmented humans generate deep information cascades, to the same extent of news media and other broadcasters, while they uniformly infiltrate across the full range of identified groups. Digital augmentation represents the cyber-physical counterpart of the human desire to acquire power within social systems.Comment: 11 page