Whom to Listen to? Trusted Source and Retweet Behavior of the NYC Marathon and Hawaii False Alarm

Terror attacks disturb social order, and impact the communications between stakeholders. Responsible organizations seek to learn the rules of public information dissemination, thus better manage the crisis communication during terror attack. Guided by social capital theory, this study took the 2017 NYC marathon attack and Hawaii false missile alarm as examples and examined which sources the public trusts most during varying phases of terror threats. The study also explored the content of the most retweeted crisis communication information, in an aim to facilitate better communications between emergency responders and the public, which would help the organizations actively accumulate social capital in the long term. Keywords: Terrorism Attack, Crisis Communication, Social Capital, Social Network, Social Medi

An Unprecedented Approach of Detecting and Reporting System of Earthquakes Using Tweet Analysis

Social media has got an exponential growth in recent years. One of the most representative examples is Twitter, which allows users to publish short tweets (messages within a 140-character limit) about “what’s happening”. This paper focuses on detecting those events to have a better understanding of what users are really discussing about in Twitter. Event detection has long been a research topic. The underlying assumption is that some related words would show an increase in the usage when an event is happening. An event is therefore conventionally represented by a number of keywords showing burst in appearance count. In this paper, we investigated the real-time nature of Twitter, devoting particular attention to event detection like earthquake. We developed an earthquake reporting system that extracts earthquakes from Twitter. It is possible to detect an earthquake by monitoring tweets. Our system detects an earthquake occurrence and sends an e-mail, possibly before an earthquake actually arrives at a certain location. This paper is the first of its kind using social media for detecting natural calamities

Passengers on social media: A real-time estimator of the state of the US air transportation system

International audienceThis paper aims at investigating further the use of the social media Twitter as a real-time estimator of the US Air Transportation system. Two different machine learning regressors have been trained on this 2017 passenger-centric dataset and tested on the first two months of 2018 for the estimation of air traffic delays at departure and arrival at 34 different US airports. Using three different levels of content-related features created from the flow of social media posts led to the extraction of useful information about the current state of the air traffic system. The resulting methods yield higher estimation performances than traditional state-of-the-art and off-the-shelf time-series forecasting techniques performed on flight-centric data for more than 28 airports. Moreover the features extracted can also be used to start a passenger-centric analysis of the Air Transportation system. This paper is the continuation of previous works focusing on estimating air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study suggest a method to use passenger-centric data-sources as an estimator of the current state of the different actors of the air transportation system in real-time

Avalanche: Prepare, Manage, and Understand Crisis Situations Using Social Media Analytics

ABSTRACT The recent rise of Social Media services has created huge streams of information which can be very valuable in a variety of scenarios. One specific scenario that has received interest is how Social Media analytics can be beneficial in crisis situations. In this paper, we describe our vision for a Social Media-ready command and control center. As motivation for our work, we present a short analysis of tweets issued in NYC during Hurricane Sandy in late October 2012 and we give an overview of the architecture of our event detection subsystem

CrisisMMD: Multimodal Twitter Datasets from Natural Disasters

During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this on- line information, if processed timely and effectively, is ex- tremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.Comment: 9 page

The Structure of Citizen Bystander Offering Behaviors Immediately After the Boston Marathon Bombing

In April of 2013, two pressure cooker bombs detonated near the finish line of the Boston Marathon. The resulting crowdsourced criminal investigation has been subject to intense scrutiny. What has not been discussed are the offering behaviors of Twitter users immediately following the detonations. The hashtag #BostonHelp offers a case study of what emergent, computer-mediated groups offer victims of a crisis event. Through creative appropriation of at-hand technologies (CAAT), this emergent group organized online offering and information about tangible resources on the ground. In this case, #BostonHelp participants harnessed blogs, social media, Google Forms, and pre-existing services to organize help for those in need. The resulting structure stabilized and became a symbol of the response itself. This case study offers an analysis of the structure created by computer-mediated crowds. We conclude with a discussion of trying to design, or even detect these behaviors at the start of a crisis response

Entwicklung eines SOA-basierten und anpassbaren Bewertungsdienstes für Inhalte aus sozialen Medien

Dieser Beitrag soll aufzeigen, wie ein anpassbarer Bewertungsdienst die Nutzung bürgergenerierter Inhalte aus sozialen Medien unterstützen kann. Dabei soll insbesondere geklärt werden, wie dieser gestaltet werden kann und wie Nutzer die Qualitätskriterien angemessen artikulieren können. Nach einer Darstellung von Grundlagen und verwandten Arbeiten wird anhand einer empirischen Vorstudie der Umgang von Behörden und Organisationen mit Sicherheitsaufgaben (BOS) mit bürgergenerierten Informationen betrachtet. Basierend auf den dort gewonnen Erkenntnissen wurde ein service-orientierter Bewertungsdienst entwickelt und in eine Anwendung integriert, welche so den Zugang zu bürgergenerierten Informationen aus verschiedenen sozialen Medien inklusive einer anpassbaren Qualitätsbewertung ermöglicht. Eine abschließende Evaluation illustriert deren mögliche Anwendung in der Praxis