The Structures of Twitter Crowds and Conversations

Abstract

Social media promises to provide access to a vast variety of human interactions, important and trivial. More than traditional electronic media or interpersonal contact, social media allows people to find and interact based on common interests rather than physical proximity. Billions of people have embraced these tools, entering social media spaces to exchange trillions of messages. Social media interactions may not be as rich as face-to-face interactions, but they offer access to a wide range of people and topics. Success has led to new problems, as social media offers too many contacts, too many interactions, and poor tools for filtering and gaining an overview of the larger landscape of communication. Social media is created and consumed through tools that limit the observer’s view to individual messages or short chains of messages and replies. The leaf and the branch of social media is visible, but not the tree or the forest. The result is an information and interaction deluge. The overwhelming amount of data and the limited ways to understand it can be seen as a negative consequence of social media. For many ordinary users social media is an incomprehensible torrent. Proposed solutions, such as automatic filters that select relevant information for us, are often seen as worse than the problem it is meant to solve. “Filter bubbles” can trap users in homogeneous collections of information, losing sight of the larger range of discussions and content. Social media is inherently a social network, meaning that people use it to create collections of connections that have an emergent form, structure and shape. Interfaces to social media, however, lack insights into the nature, topology, and size of the networks they present. Access to social media network information is of academic and practical interest. Social Network Analysis (SNA) offers a powerful method to conceptualize, analyze and visualize social media—leading to new applications and user interfaces that help users make their own decisions about content relevance and the credibility of other users. Social media can be much more useful for users, and the information in it can be more easily evaluated, if its underlying network structure is made more visible and comprehensible.