Crisis communication and social media : the changing environment for natural disaster response

Over the past two years there have been several large-scale disasters (Haitian earthquake, Australian floods, UK riots, and the Japanese earthquake) that have seen wide use of social media for disaster response, often in innovative ways. This paper provides an analysis of the ways in which social media has been used in public-to-public communication and public-to-government organisation communication. It discusses four ways in which disaster response has been changed by social media: 1. Social media appears to be displacing the traditional media as a means of communication with the public during a crisis. In particular social media influences the way traditional media communication is received and distributed. 2. We propose that user-generated content may provide a new source of information for emergency management agencies during a disaster, but there is uncertainty with regards to the reliability and usefulness of this information. 3. There are also indications that social media provides a means for the public to self-organise in ways that were not previously possible. However, the type and usefulness of self-organisation sometimes works against efforts to mitigate the outcome of the disaster. 4. Social media seems to influence information flow during a disaster. In the past most information flowed in a single direction from government organisation to public, but social media negates this model. The public can diffuse information with ease, but also expect interaction with Government Organisations rather than a simple one-way information flow. These changes have implications for the way government organisations communicate with the public during a disaster. The predominant model for explaining this form of communication, the Crisis and Emergency Risk Communication (CERC), was developed in 2005 before social media achieved widespread popularity. We will present a modified form of the CERC model that integrates social media into the disaster communication cycle, and addresses the ways in which social media has changed communication between the public and government organisations during disasters.

Crisis communication and social media:the changing environment for natural disaster response

Over the past two years there have been several large-scale disasters (Haitian earthquake, Australian floods, UK riots, and the Japanese earthquake) that have seen wide use of social media for disaster response, often in innovative ways. This paper provides an analysis of the ways in which social media has been used in public-to-public communication and public-to-government organisation communication. It discusses four ways in which disaster response has been changed by social media: 1. Social media appears to be displacing the traditional media as a means of communication with the public during a crisis. In particular social media influences the way traditional media communication is received and distributed. 2. We propose that user-generated content may provide a new source of information for emergency management agencies during a disaster, but there is uncertainty with regards to the reliability and usefulness of this information. 3. There are also indications that social media provides a means for the public to self-organise in ways that were not previously possible. However, the type and usefulness of self-organisation sometimes works against efforts to mitigate the outcome of the disaster. 4. Social media seems to influence information flow during a disaster. In the past most information flowed in a single direction from government organisation to public, but social media negates this model. The public can diffuse information with ease, but also expect interaction with Government Organisations rather than a simple one-way information flow. These changes have implications for the way government organisations communicate with the public during a disaster. The predominant model for explaining this form of communication, the Crisis and Emergency Risk Communication (CERC), was developed in 2005 before social media achieved widespread popularity. We will present a modified form of the CERC model that integrates social media into the disaster communication cycle, and addresses the ways in which social media has changed communication between the public and government organisations during disasters.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.

A four-factor user interaction model for content-based image retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.

Enabling effective user interactions in content-based image retrieval

This paper presents an interactive content-based image retrieval framework—uInteract, for delivering a novel four-factor user interaction model visually. The four-factor user interaction model is an interactive relevance feedback mechanism that we proposed, aiming to improve the interaction between users and the CBIR system and in turn users overall search experience. In this paper, we present how the framework is developed to deliver the four-factor user interaction model, and how the visual interface is designed to support user interaction activities. From our preliminary user evaluation result on the ease of use and usefulness of the proposed framework, we have learnt what the users like about the framework and the aspects we could improve in future studies. Whilst the framework is developed for our research purposes, we believe the functionalities could be adapted to any content-based image search framework.

Originator or propagator? Incorporating social role theory into topic models for twitter content analysis

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently in different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles. Copyright 2013 ACM.

Infer user interests via link structure regularization

Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification. © 2014 ACM.

Incorporating social role theory into topic models for social media content analysis

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

User involvement in UK cancer services:bridging the policy gap

Recent UK government initiatives aim to increase user involvement in the National Health Service (NHS) in two ways: by encouraging service users to take an active role in making decisions about their own care; and by establishing opportunities for wider public participation in service development. The purpose of this study was to examine how UK cancer service users understand and relate to the concept of user involvement. The data were collected through in-depth interviews, which were analysed for content according to the principles of grounded theory. The results highlight the role of information and communication in effective user involvement. Perhaps more importantly, this study suggests that the concept of user involvement is unclear to many cancer service users. This paper argues the need for increased awareness and understanding of what user involvement is and how it can work.

Ensuring QoE in contemporary mobile networks for video content distribution

A framework that aims to best utilize the mobile network resources for video applications is presented in this paper. The main contribution of the work proposed is the QoE-driven optimization method that can maintain a desired trade-off between fairness and efficiency in allocating resources in terms of data rates to video streaming users in LTE networks. This method is concerned with the control of the user satisfaction level from the service continuity's point of view and applies appropriate QoE metrics (Pause Intensity and variations) to determine the scheduling strategies in combination with the mechanisms used for adaptive video streaming such as 3GP/MPEG-DASH. The superiority of the proposed algorithms are demonstrated, showing how the resources of a mobile network can be optimally utilized by using quantifiable QoE measurements. This approach can also find the best match between demand and supply in the process of network resource distribution.