Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach.
Social media such as Twitter and Facebook has been widely regarded as active communication channels during emergency events such as disasters caused by natural hazards. The Federal Emergency Management Agency (FEMA) identifies social media as an essential component of future disaster management @cite_29 . Tweets sent during catastrophic events have been known to contain information that contributes to situational awareness @cite_2 , and a recent survey of studies for analyzing social media in disaster response can be found in @cite_30 . Therefore, social media data analytics for disaster response has gained extensive interest from the research community. Existing studies focus on extracting disaster-related information from socially-generated content during natural disasters, from which actionable information can be disseminated to disaster relief workers @cite_9 . More recent studies build classifiers for identifying earthquake-relevant tweets @cite_19 , classifying tweets based on informative and uninformative tweets @cite_26 @cite_27 @cite_11 @cite_8 . Furthermore, tweets can be categorized by type @cite_17 @cite_21 @cite_20 @cite_1 (i.e., affected individuals, infrastructure and utilities, donations and volunteer, caution and advice, sympathy and condolence) or by information source @cite_25 @cite_13 @cite_33 @cite_14 @cite_17 (i.e., eyewitness, government, NGOs, business, media).
@cite_30 @cite_26 @cite_14 @cite_33 @cite_8 @cite_9 @cite_29 @cite_21 @cite_17 @cite_1 @cite_19 @cite_27 @cite_2 @cite_13 @cite_25 @cite_20 @cite_11
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Social media platforms provide active communication channels during mass convergence and emergency events such as disasters caused by natural hazards. As a result, first responders, decision makers, and the public can use this information to gain insight into the situation as it unfolds. In particular, many social media messages communicated during emergencies convey timely, actionable information. Processing social media messages to obtain such information, however, involves solving multiple challenges including: parsing brief and informal messages, handling information overload, and prioritizing different types of information found in messages. These challenges can be mapped to classical information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. We survey the state of the art regarding computational methods to process social media messages and highlight both their contributions and shortcomings. In addition, we examine their particularities, and methodically examine a series of key subproblems ranging from the detection of events to the creation of actionable and useful summaries. Research thus far has, to a large extent, produced methods to extract situational awareness information from social media. In this survey, we cover these various approaches, and highlight their benefits and shortcomings. We conclude with research challenges that go beyond situational awareness, and begin to look at supporting decision making and coordinating emergency-response actions. Social media platforms such as Twitter garner significant attention from very large audiences in response to real-world events. Automatically establishing who is participating in information production or conversation around events can improve event content consumption, help expose the stakeholders in the event and their varied interests, and even help steer subsequent coverage of an event by journalists. In this paper, we take initial steps towards building an automatic classifier for user types on Twitter, focusing on three core user categories that are reflective of the information production and consumption processes around events: organizations, journalists media bloggers, and ordinary individuals. Exploration of the user categories on a range of events shows distinctive characteristics in terms of the proportion of each user type, as well as differences in the nature of content each shared around the events. Social media is already a fixture for reporting for many journalists, especially around breaking news events where non-professionals may already be on the scene to share an eyewitness report, photo, or video of the event. At the same time, the huge amount of content posted in conjunction with such events serves as a challenge to finding interesting and trustworthy sources in the din of the stream. In this paper we develop and investigate new methods for filtering and assessing the verity of sources found through social media by journalists. We take a human centered design approach to developing a system, SRSR ("Seriously Rapid Source Review"), informed by journalistic practices and knowledge of information production in events. We then used the system, together with a realistic reporting scenario, to evaluate the filtering and visual cue features that we developed. Our evaluation offers insights into social media information sourcing practices and challenges, and highlights the role technology can play in the solution. During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general. Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems. We analyze microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service. We focus on communications broadcast by people who were "on the ground" during the Oklahoma Grassfires of April 2009 and the Red River Floods that occurred in March and April 2009, and identify information that may contribute to enhancing situational awareness (SA). This work aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques. Two weeks after the Great Tohoku earthquake followed by the devastating tsunami, we have sent open-ended questionnaires to a randomly selected sample of Twitter users and also analysed the tweets sent from the disaster-hit areas. We found that people in directly affected areas tend to tweet about their unsafe and uncertain situation while people in remote areas post messages to let their followers know that they are safe. Our analysis of the open-ended answers has revealed that unreliable retweets (RTs) on Twitter was the biggest problem the users have faced during the disaster. Some of the solutions offered by the respondents included introducing official hash tags, limiting the number of RTs for each hash tag and adding features that allow users to trace information by maintaining anonymity. During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes. Social media platforms such as Twitter have been considered as a vital source of useful information for disaster response and management. Despite advances in natural language processing techniques, processing short and informal Twitter messages is a challenging task. In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes. DNNs use distributed representation of words and learn the representation as well as higher level features automatically for the classification task. We propose a new online algorithm based on stochastic gradient descent to train DNNs in an online fashion during disaster situations. We test our models using a crisis-related real-world Twitter dataset. In this paper we examine the information sharing practices of people living in cities amid armed conflict. We describe the volume and frequency of microblogging activity on Twitter from four cities afflicted by the Mexican Drug War, showing how citizens use social media to alert one another and to comment on the violence that plagues their communities. We then investigate the emergence of civic media "curators," individuals who act as "war correspondents" by aggregating and disseminating information to large numbers of people on social media. We conclude by outlining the implications of our observations for the design of civic media systems in wartime. This empirical study of "digital volunteers" in the aftermath of the January 12, 2010 Haiti earthquake describes their behaviors and mechanisms of self-organizing in the information space of a microblogging environment, where collaborators were newly found and distributed across continents. The paper explores the motivations, resources, activities and products of digital volunteers. It describes how seemingly small features of the technical environment offered structure for self-organizing, while considering how the social-technical milieu enabled individual capacities and collective action. Using social theory about self-organizing, the research offers insight about features of coordination within a setting of massive interaction. In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims’ requests for help. Social media used around crises involves self-organizing behavior that can produce accurate results, often in advance of official communications. This allows affected population to send tweets or text messages, and hence, make them heard. The ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. In this study, we developed a reusable information technology infrastructure, called Enhanced Messaging for the Emergency Response Sector (EMERSE), which classifies and aggregates tweets and text messages about the Haiti disaster relief so that non-governmental organizations, relief workers, people in Haiti, and their friends and families can easily access them. The first objective towards the effective use of microblogging services such as Twitter for situational awareness during the emerging disasters is discovery of the disaster-related postings. Given the wide range of possible disasters, using a pre-selected set of disaster-related keywords for the discovery is suboptimal. An alternative that we focus on in this work is to train a classifier using a small set of labeled postings that are becoming available as a disaster is emerging. Our hypothesis is that utilizing large quantities of historical microblogs could improve the quality of classification, as compared to training a classifier only on the labeled data. We propose to use unlabeled microblogs to cluster words into a limited number of clusters and use the word clusters as features for classification. To evaluate the proposed semi-supervised approach, we used Twitter data from 6 different disasters. Our results indicate that when the number of labeled tweets is 100 or less, the proposed approach is superior to the standard classification based on the bag or words feature representation. Our results also reveal that the choice of the unlabeled corpus, the choice of word clustering algorithm, and the choice of hyperparameters can have a significant impact on the classification accuracy.