From coincidence to purposeful flow? properties of transcendental information cascades

In this paper, we investigate a method for constructing cascades of information co-occurrence, which is suitable to trace emergent structures in information in scenarios where rich contextual features are unavailable. Our method relies only on the temporal order of content-sharing activities, and intrinsic properties of the shared content itself. We apply this method to analyse information dissemination patterns across the active online citizen science project Planet Hunters, a part of the Zooniverse platform. Our results lend insight into both structural and informational properties of different types of identifiers that can be used and combined to construct cascades. In particular, significant differences are found in the structural properties of information cascades when hashtags as used as cascade identifiers, compared with other content features. We also explain apparent local information losses in cascades in terms of information obsolescence and cascade divergence; e.g., when a cascade branches into multiple, divergent cascades with combined capacity equal to the original

When resources collide: Towards a theory of coincidence in information spaces

This paper is an attempt to lay out foundations for a general theory of coincidence in information spaces such as the World Wide Web, expanding on existing work on bursty structures in document streams and information cascades. We elaborate on the hypothesis that every resource that is published in an information space, enters a temporary interaction with another resource once a unique explicit or implicit reference between the two is found. This thought is motivated by Erwin Shroedingers notion of entanglement between quantum systems. We present a generic information cascade model that exploits only the temporal order of information sharing activities, combined with inherent properties of the shared information resources. The approach was applied to data from the world's largest online citizen science platform Zooniverse and we report about findings of this case study

Characterizing Attention Cascades in WhatsApp Groups

An important political and social phenomena discussed in several countries, like India and Brazil, is the use of WhatsApp to spread false or misleading content. However, little is known about the information dissemination process in WhatsApp groups. Attention affects the dissemination of information in WhatsApp groups, determining what topics or subjects are more attractive to participants of a group. In this paper, we characterize and analyze how attention propagates among the participants of a WhatsApp group. An attention cascade begins when a user asserts a topic in a message to the group, which could include written text, photos, or links to articles online. Others then propagate the information by responding to it. We analyzed attention cascades in more than 1.7 million messages posted in 120 groups over one year. Our analysis focused on the structural and temporal evolution of attention cascades as well as on the behavior of users that participate in them. We found specific characteristics in cascades associated with groups that discuss political subjects and false information. For instance, we observe that cascades with false information tend to be deeper, reach more users, and last longer in political groups than in non-political groups.Comment: Accepted as a full paper at the 11th International ACM Web Science Conference (WebSci 2019). Please cite the WebSci versio

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

Communities, Knowledge Creation, and Information Diffusion

In this paper, we examine how patterns of scientific collaboration contribute to knowledge creation. Recent studies have shown that scientists can benefit from their position within collaborative networks by being able to receive more information of better quality in a timely fashion, and by presiding over communication between collaborators. Here we focus on the tendency of scientists to cluster into tightly-knit communities, and discuss the implications of this tendency for scientific performance. We begin by reviewing a new method for finding communities, and we then assess its benefits in terms of computation time and accuracy. While communities often serve as a taxonomic scheme to map knowledge domains, they also affect how successfully scientists engage in the creation of new knowledge. By drawing on the longstanding debate on the relative benefits of social cohesion and brokerage, we discuss the conditions that facilitate collaborations among scientists within or across communities. We show that successful scientific production occurs within communities when scientists have cohesive collaborations with others from the same knowledge domain, and across communities when scientists intermediate among otherwise disconnected collaborators from different knowledge domains. We also discuss the implications of communities for information diffusion, and show how traditional epidemiological approaches need to be refined to take knowledge heterogeneity into account and preserve the system's ability to promote creative processes of novel recombinations of idea

Efficiency of Human Activity on Information Spreading on Twitter

Understanding the collective reaction to individual actions is key to effectively spread information in social media. In this work we define efficiency on Twitter, as the ratio between the emergent spreading process and the activity employed by the user. We characterize this property by means of a quantitative analysis of the structural and dynamical patterns emergent from human interactions, and show it to be universal across several Twitter conversations. We found that some influential users efficiently cause remarkable collective reactions by each message sent, while the majority of users must employ extremely larger efforts to reach similar effects. Next we propose a model that reproduces the retweet cascades occurring on Twitter to explain the emergent distribution of the user efficiency. The model shows that the dynamical patterns of the conversations are strongly conditioned by the topology of the underlying network. We conclude that the appearance of a small fraction of extremely efficient users results from the heterogeneity of the followers network and independently of the individual user behavior.Comment: 29 pages, 10 figure