Abstract: Information about location and geographical coordinates in particular, may be very important during a crisis event, especially for search and rescue operations ? but currently geo-tagged tweets are extremely rare. Improved capabilities of capturing additional location from Twitter (up to 4 times improvement) are crucial for response efforts given a vast amount of messages exchanged during a crisis event. That is why authors have designed a tool (Text Analysis TWeet lOcator ? TAT2) that relies on existing open source text analysis tools with additional services to provide additional hints about people location. Validation process, complementing experimentation and test results, included involvement of end-users (i.e. Public Protection and Disaster Relief services and citizens during a realistic crisis exercise showcase. In addition, the integration of TAT2 with external tools has also been validated.

Abstract: Micro-blogging site like Twitter, over the last decade, has evolved into a proactive communication channel during mass convergence and emergency events, especially in crisis stricken scenarios. Extracting multiple levels of information associated with the overwhelming amount of social media data generated during such situations remains a great challenge to disaster-affected communities and professional emergency responders. These valuable data, segregated into different informative categories, can be leveraged by the government agencies, humanitarian communities as well as citizens to bring about faster response in areas of necessity. In this paper, we address the above scenario by developing a deep Convolutional Neural Network (CNN) for multi-label classification of crisis related tweets.We augment deep CNN by several linguistic features extracted from Tweet, and investigate their usage in classification. Evaluation on a benchmark dataset show that our proposed approach attains the state-of-the-art performance.

Abstract: In the last decade, people have increasingly relied on social media platforms such as Twitter to share information on the response to a natural or a man-made disaster. This paper focuses on the aftermath of the Fukushima Daiichi nuclear disaster. Since the disaster, victims and volunteers have been sharing relevant information about radiation measurements by means of social media. The aim of this research is to explore the diffusion of information produced and shared by Twitter bots, to understand the degree of popularity of these sources and to check if these bots deliver original radiation measurements.

Abstract: We report initial findings around the Facebook and Twitter adoption trends of 840 fire and police departments affected by Hurricane Sandy. The data show that adoption increased during the time period directly surrounding Hurricane Sandy. Despite this increase, the creation of new online accounts since that time has been declining and overall adoption rates seem to be stabilizing. Lastly, the data report Facebook to be significantly more popular than Twitter as a form of online communication for these fire and police departments.

Abstract: Crisis Named Resources (CNRs) are the social media accounts and pages named after a crisis event. CNRs typically appear spontaneously after an event as places for information exchange. They are easy to find when searching for information about the event. Yet in most cases, it is unclear who manages these resources. Thus, it is important to understand what kinds of information they provide and what role they play in a response. This paper describes a study of Facebook and Twitter CNRs around the 2016 Fort McMurray wildfire. We report on CNR lifecycles, and their relevance to the event. Based on the information provided by these resources, we categorize them into 8 categories: donations, fundraisers, prayers, reactions, reports, needs and offers, stories, and unrelated. We also report on the most popular CNR on both Facebook and Twitter. We conclude by discussing the role of CNRs and the need for future investigation.

Abstract: Conceptual frameworks which seek to integrate social media uses into disaster management strategies are employed in a range of events. With continued variations to social media practices, developments in technology, and changes in online behaviors, it is imperative to provide conceptual frameworks which are relevant, current and insightful. This paper conceptualizes a range of recent literature through an inductive methodology, and presents the themes of Web accessibility and online information reliability as broad and emerging considerations for the identification of social media uses during disasters. It presents a new conceptual framework of current social media uses which may be used to supplement existing frameworks. The framework has been applied to a dataset of Tweets from the 2015 Nepal earthquake to demonstrate its validity. Suggestions for future applications are discussed.

Abstract: The use of social media is gaining more and more in importance in ordinary life, but also in conflicts and emer-gencies. The social big data, generated by users, is partially also used as a source for situation assessment, e.g. to receive pictures or to assess the general mood. However, the information's believability is hard to control and can deceive. Rumors, fake news and social bots are phenomenons that challenge the easy consumption of social media. To address this, our paper explores the believability of content in social media. Based on foundations of infor-mation quality we conducted a literature study to derive a three-level model for assessing believability. It summa-rizes existing assessment approaches, assessment criteria and related measures. On this basis, we describe several steps towards the development of an assessment approach that works across different types of social media.

Abstract: Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples? ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the ?bag of words? and n-grams as features on several datasets of messages from flooding events.

Abstract: The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in the computer vision field, automatic processing of the crisis-related social media imagery data remains a challenging task. It is because a majority of which consists of redundant and irrelevant content. In this paper, we present an image processing pipeline that comprises de-duplication and relevancy filtering mechanisms to collect and filter social media image content in real-time during a crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.

Abstract: This empirical analysis examines the use of the live-streaming application Periscope in three crises that occurred in 2015. Qualitative analyses of tweets relating to the Amtrak derailment in Philadelphia, Baltimore protests after Freddie Grey?s death, and Hurricane Joaquin flooding in South Carolina reveal that this recently deployed application is being used by both citizens and journalists for information sharing, crisis coverage and commentary. The accessibility and immediacy of live video directly from crisis situations, and the embedded chats which overlay on top of a video feed, extend the possibilities of real-time interaction between remote crowds and those on the ground in a crisis. These empirical findings suggest several potential challenges and opportunities for responders.

Abstract: In this paper we present a qualitative, social network based, power analysis of relief and recovery efforts in the aftermath of the 2015 earthquakes in Nepal. We examine how the interplay between humanitarian power relations and e-resilience influenced communities' ability to respond to the destruction brought about by the disaster. We focus in particular on how power dynamics affect online spaces and interactions at the hyper local level (or 'the last mile'). We explain how civic technology initiatives are affected by these power relationships and show how their efforts may reinforce social inequalities – or be sidelined – if power dynamics are not taken into consideration. However, on the basis of a case study based power analysis, we show that when civic technology initiatives do strategically engage with these dynamics, they have the potential to alter harmful power relations that limit community e-resilience.

Abstract: We discuss the results of a research project aimed at exploring the use of social media in emergency communication by officers operating at a local level. We performed 16 semi-structured interviews with national level expert informants, and with officers operating at the municipality and province (prefectures) level in an Italian region (respondents were selected based on their involvement in emergency communication and/or emergency management processes). Social media usage appears distributed over a continuum of engagement, ranging from very basic usage to using social media by adopting a broadcasting approach, to deeper engagement, which also includes continuous interaction with citizens. Two main attitudes emerge both in the narrative style and in social media representations: some respondents seem to adopt an institutional attitude, while others adopt a practical-professional attitude. Among the main barriers to a broader adoption of social media, cultural considerations seem to prevail, along with the lack of personnel, a general concern toward social media communication reliability, and the perceived distance between the formal role of institutions and the informal nature of social media communication.

Abstract: Many possible links and connections can be observed between the different types of media used for communication during a crisis. These links can be detected and assembled to provide a more complete picture of events. They can be categorized according to the type of destination which yields important information for the gathering process as well as concerning general patterns of how platforms are connected. Tweets, posts and comments thus become parts of larger, linked sets of documents forming compound-documents. These documents stretch across media borders and platforms and provide context and broader information for individual entries. In the current paper we describe some of the links and linking behavior encountered during the floods in Central Europe of 2013 from the perspective of Twitter and Facebook.