We Are Forgotten. Framing Disaster via Twitter in the Aftermath of Hurricane Maria: Implications for Social Work Policy Practice

This work presents a comprehensive study of the disaster discourses generated by key social media user groups in the aftermath of Hurricane Maria in Puerto Rico. Hurricane Maria, the third most destructive hurricane in American history, resulted in billions of dollars in damage and the loss of nearly 3,000 lives. Disasters result in widespread geophysical impacts as well as social, political, and economic upheavals for individuals, families, communities, and nation-states in the storm’s wake. The discourses that emerge on social media are significant in how they frame public narratives in the aftermath of disaster. The social construction of disaster points to the contested nature of these frames as they vie for dominance in the public sphere, including social media communicative spaces. The literature suggests that there are numerous key interpretive communities and narratives present at any given time. The current study explores six of these communities (individuals, government, military, media, nonprofits, and others) and their corresponding disaster narratives as communicated via Twitter. By utilizing a social constructionist/critical theoretical framework, the prevalent frames embedded in the disaster discourses are identified. These frames include the political frame, destruction frame, victim/hero frame, military/humanitarian aid frame, and counter-narratives

A Twitter narrative of the COVID-19 pandemic in Australia

Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management.Comment: Accepted to ISCRAM 202

Social Media Behaviour Analysis in Disaster-Response Messages of Floods and Heat Waves via Artificial Intelligence

This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark datasets of disaster responses and extreme events. The resulting models are applied to perform a qualitative analysis via topic inference in text data. We further analyse a set of behavioural indicators and match them with climate variables via decoding synoptical records to analyse thermal comfort. We highlight the advantages of aligning behavioural indicators along with climate variables to provide with 7 additional valuable information to be considered especially in different phases of a disaster and applicable to extreme weather periods. The positiveness of messages is around 8% for disaster, 1% for disaster and medical response, 7% for disaster and humanitarian related messages. This shows the reliability of such data for our case studies. We show the transferability of this approach to be applied to any social media data collection

@Houstonpolice: an exploratory case of Twitter during Hurricane Harvey

Abstract Purpose The purpose of this paper is to examine the Houston Police Department (HPD)’s public engagement efforts using Twitter during Hurricane Harvey, which was a large-scale urban crisis event. Design/methodology/approach This study harvested a corpus of over 13,000 tweets using Twitter’s streaming API, across three phases of the Hurricane Harvey event: preparedness, response and recovery. Both text and social network analysis (SNA) techniques were employed including word clouds, n-gram analysis and eigenvector centrality to analyze data. Findings Findings indicate that departmental tweets coalesced around topics of protocol, reassurance and community resilience. Twitter accounts of governmental agencies, such as regional police departments, local fire departments, municipal offices, and the personal accounts of city’s police and fire chiefs were the most influential actors during the period under review, and Twitter was leveraged as de facto a 9-1-1 dispatch. Practical implications Emergency management agencies should consider adopting a three-phase strategy to improve communication and narrowcast specific types of information corresponding to relevant periods of a crisis episode. Originality/value Previous studies on police agencies and social media have largely overlooked discrete periods, or phases, in crisis events. To address this gap, the current study leveraged text and SNA to investigate Twitter communications between HPD and the public. This analysis advances understanding of information flows on law enforcement social media networks during crisis and emergency events

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Analyzing Twitter Feeds to Facilitate Crises Informatics and Disaster Response During Mass Emergencies

It is a common practice these days for general public to use various micro-blogging platforms, predominantly Twitter, to share ideas, opinions and information about things and life. Twitter is also being increasingly used as a popular source of information sharing during natural disasters and mass emergencies to update and communicate the extent of the geographic phenomena, report the affected population and casualties, request or provide volunteering services and to share the status of disaster recovery process initiated by humanitarian-aid and disaster-management organizations. Recent research in this area has affirmed the potential use of such social media data for various disaster response tasks. Even though the availability of social media data is massive, open and free, there is a significant limitation in making sense of this data because of its high volume, variety, velocity, value, variability and veracity. The current work provides a comprehensive framework of text processing and analysis performed on several thousands of tweets shared on Twitter during natural disaster events. Specifically, this work em- ploys state-of-the-art machine learning techniques from natural language processing on tweet content to process the ginormous data generated at the time of disasters. This study shall serve as a basis to provide useful actionable information to the crises management and mitigation teams in planning and preparation of effective disaster response and to facilitate the development of future automated systems for handling crises situations

Interoperability Performance Among Campus Law Enforcement Agencies

The September 11, 2001 terrorist attacks exposed considerable breakdowns in communications interoperability and information sharing among first responders. Multijurisdictional responses to the active-shooter incidents at the University of Texas in 2010; Sandy Hook Elementary of Newtown, Connecticut in 2012, and the Reynolds High School shooting of Multnomah County, Oregon in 2014 were replete with interoperability failures as well. Recent multijurisdictional response events continue to illuminate difficulties with first-responder interoperability and minimal research exists to promote understanding of the interoperability challenges of university police departments. The purpose of this study was to explore the barriers that impede communications of campus based law enforcement agencies during multiagency or multijurisdictional response. General systems theory and the unified theory of acceptance and use of technology model provided the conceptual framework for this qualitative case study. Face-to-face interviews were conducted with 10 leaders of university public safety agencies in California. Data were collected, inductively coded, and thematically analyzed. Key findings indicate that participants perceived barriers of funding, policy, inclusiveness, and training that affect communications interoperability performance. The positive social change implications from this study include recommendations of policy change for improved interoperability during multiagency or multijurisdictional response which can contribute to increased first-responder safety, more efficient multijurisdictional response, and improved safety of students and society at large