1,175 research outputs found

    On Identifying Hashtags in Disaster Twitter Data

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    Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as 92.22%. The dataset, code, and other resources are available on Github

    A deep multi-modal neural network for informative Twitter content classification during emergencies

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    YesPeople start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents

    Pulling Information from Social Media in the Aftermath of Unpredictable Disasters

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    Social media have become a primary communication channel among people and are continuously overwhelmed by huge volumes of User Generated Content. This is especially true in the aftermath of unpredictable disasters, when users report facts, descriptions and photos of the unfolding event. This material contains actionable information that can greatly help rescuers to achieve a better response to crises, but its volume and variety render manual processing unfeasible. This paper reports the experience we gained from developing and using a web-enabled system for the online detection and monitoring of unpredictable events such as earthquakes and floods. The system captures selected message streams from Twitter and offers decision support functionalities for acquiring situational awareness from textual content and for quantifying the impact of disasters. The software architecture of the system is described and the approaches adopted for messages filtering, emergency detection and emergency monitoring are discussed. For each module, the results of real-world experiments are reported. The modular design makes the system easy configurable and allowed us to conduct experiments on different crises, including Emilia earthquake in 2012 and Genoa flood in 2014. Finally, some possible functionalities relying on the analysis of multimedia information are introduced

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

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    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

    What roles do social media play in hurricane Ian, before, during and after the event

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    In recent years, natural disasters like wildfires, tsunamis, and floods have surged in both severity and frequency, causing widespread harm, including physical damage, loss of life, economic turmoil, and societal unrest. Among these disasters, hurricanes, defined by wind speeds surpassing 74 mph, pose a persistent threat, bringing hazards such as heavy rainfall and inland flooding. Hurricane Ian, one of the most significant in recent U.S. history, formed on September 23rd, hit Florida on September 28th, and dissipated on October 2nd, leaving widespread devastation. In the realm of disaster management, Location-Based Social Media (LBSM) has emerged as a crucial tool, aiding in early warnings, damage assessment, rescue coordination, and recovery evaluation. This thesis focuses on the analysis of English and Spanish tweets related to Hurricane Ian, covering the period from its formation to 50 days after its dissipation. The tweet datasets were divided into two categories: all tweets and the top 1% most shared tweets. Employing the Latent Dirichlet Allocation (LDA) model, the study unveiled prevalent themes within the tweets over different timeframes. Additionally, sentiment analysis was conducted on both English and Spanish tweet datasets, using the Valence Aware Dictionary and sEntimentReasoner (VADER) model for English tweets and Vader-multi for Spanish tweets. This aimed to capture the evolving sentiments of individuals and their emotional responses to various topics. The findings reveal Twitter's effectiveness as an early warning system and a valuable tool for risk assessment and recovery. Leading up to the hurricane's landfall, discussions mainly revolved around weather and disaster-related topics. During and after the hurricane, the focus shifted to disaster-related and situational topics. Sentiment analysis indicated a growing negativity as the storm approached, followed by a gradual return to less negative sentiments after the hurricane passed. This thesis emphasizes the significance of social media platforms as essential resources for rapid decision-making during crises, particularly when quick responses are imperative
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