Emma S. Spiro

Warning tweets: serial transmission of messages during the warning phase of a disaster event

Serial transmission – the passing on of information from one source to another – is a phenomenon of central interest in the study of informal communication in emergency settings. Microblogging services such as Twitter make it possible to study serial transmission on a large scale and to examine the factors that make retransmission of messages more or less likely. Here, we consider factors predicting serial transmission at the interface of formal and informal communication during disaster; specifically, we examine the retransmission by individuals of messages (tweets) issued by formal organizations on Twitter. Our central question is the following: How do message content, message style, and public attention to tweets relate to the behavioral activity of retransmitting (i.e. retweeting) a message in disaster? To answer this question, we collect all public tweets sent by a set of official government accounts during a 48-hour period of the Waldo Canyon wildfire. We manually code tweets for their thematic content and elements of message style. We then create predictive models to show how thematic content, message style, and changes in number of Followers affect retweeting behavior. From these predictive models, we identify the key elements that affect public retransmission of messages during the emergency phase of an unfolding disaster. Our findings suggest strategies for designing and disseminating messages through networked social media under periods of imminent threat.

Rumoring during extreme events: a case study of deepwater horizon 2010

Social scientists have proposed many different factors thought to influence rumoring behavior. Classical rumor theory points to the perceived importance, the level of uncertainty or ambiguity, and the potential to impact decision making as influential in determining the extent of rumoring. In this work, we test some of these proposed rumor determinants in the context of the the 2010 Deepwater Horizon oil spill, using data on communication dynamics from the popular microblogging service Twitter. Using a latent factor model, we measure rates of hazard-related conversation by exploiting joint variation in multiple conversation streams. Time series analysis of the resulting rates suggests that media coverage of the event is a major driver of rumoring behavior, supporting importance/saliency theories and disconfirming theories of information substitution for this event. Relevance of the event to decision making behavior also turns out to be an influential predictor in this case. Since information diffusion via serial transmission is a fundamental process by which rumors spread, we compare rates of serial transmission between control and hazard-related communication. Twitter posts are much more likely to be retweeted when they contain hazard-related keywords (versus control words). Implications of these findings for disaster response are discussed.

The h-Index of a Graph and Its Application to Dynamic Subgraph Statistics

We describe a data structure that maintains the number of triangles in a dynamic undirected graph, subject to insertions and deletions of edges and of degree-zero vertices. More generally it can be used to maintain the number of copies of each possible three-vertex subgraph in time O (h ) per update, where h is the h-index of the graph, the maximum number such that the graph contains h vertices of degree at least h . We also show how to maintain the h -index itself, and a collection of h high-degree vertices in the graph, in constant time per update. Our data structure has applications in social network analysis using the exponential random graph model (ERGM); its bound of O (h ) time per edge is never worse than the

Extended structures of mediation: Re-examining brokerage in dynamic networks ☆

\Theta(\sqrt m)

Tweeting the Spill: Online Informal Communications, Social Networks, and Conversational Microstructures during the Deepwater Horizon Oilspill

time per edge necessary to list all triangles in a static graph, and is strictly better for graphs obeying a power law degree distribution. In order to better understand the behavior of the h -index statistic and its implications for the performance of our algorithms, we also study the behavior of the h -index on a set of 136 real-world networks.

How Information Snowballs: Exploring the Role of Exposure in Online Rumor Propagation

Abstract In this paper we revisit the concept of brokerage in social networks. We elaborate on the concept of brokerage as a process, identifying three distinct classes of brokerage behavior. Based on this process model, we develop a framework for measuring brokerage opportunities in dynamic relational data. Using data on emergent inter-organizational collaborations, we employ the dynamic brokerage framework to examine the relationship between organizational attributes and coordination in the evolving network. Comparing the findings of our process-based definition with traditional, static approaches, we identify important dimensions of organizational action that would be missed by the latter approach.

Could This Be True?: I Think So! Expressed Uncertainty in Online Rumoring

Informal online communication channels are being utilized for official communications in disaster contexts. Channels such as networked microblogging enable public officials to broadcast messages as well as engage in direct communication exchange with individuals. Here the authors investigate online information exchange behaviors of a set of state and federal organizations during the Deepwater Horizon 2010 oil spill disaster. Using data from the popular microblogging service, Twitter, they analyze the roles individual organizations play in the dissemination of information to the general public online, and the conversational aspects of official posts. The authors discuss characteristics and features of the following networks including actor centrality and differential mixing, as well as how structural features may affect information exchange in disasters. This research provides insight into the use of networked communications during an event of heightened public concern, describes implications of conversational features, and suggests directions for future research. DOI: 10.4018/jiscrm.2013010104 International Journal of Information Systems for Crisis Response and Management, 5(1), 58-76, January-March 2013 59 Copyright

The h-Index of a Graph and its Application to Dynamic Subgraph Statistics

In this paper we highlight three distinct approaches to studying rumor dynamics-volume, exposure, and content production. Expanding upon prior work, which has focused on rumor volume, we argue that considering the size of the exposed population is a vital component of understanding rumoring. Additionally, by combining all three approaches we discover subtle features of rumoring behavior that would have been missed by applying each approach in isolation. Using a case study of rumoring on Twitter during a hostage crisis in Sydney, Australia, we apply a mixed-methods framework to explore rumoring and its consequences through these three lenses, focusing on the added dimension of exposure in particular. Our approach demonstrates the importance of considering both rumor content and the people engaging with rumor content to arrive at a more holistic understanding of communication dynamics. These results have implications for emergency responders and official use of social media during crisis management.

Using Twitter for Demographic and Social Science Research Tools for Data Collection and Processing

Rumors are regular features of crisis events due to the extreme uncertainty and lack of information that often characterizes these settings. Despite recent research that explores rumoring during crisis events on social media platforms, limited work has focused explicitly on how individuals and groups express uncertainty. Here we develop and apply a flexible typology for types of expressed uncertainty. By applying our framework across six rumors from two crisis events we demonstrate the role of uncertainty in the collective sensemaking process that occurs during crisis events.

A cross-hazard analysis of terse message retransmission on Twitter

We describe a data structure that maintains the number of triangles in a dynamic undirected graph, subject to insertions and deletions of edges and of degree-zero vertices. More generally it can be used to maintain the number of copies of each possible three-vertex subgraph in time O(h) per update, where h is the h-index of the graph, the maximum number such that the graph contains