Verifying baselines for crisis event information classification on Twitter

Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability

Sharing news, making sense, saying thanks: patterns of talk on Twitter during the Queensland floods

Abstract: This paper examines the discursive aspects of Twitter communication during the floods in the summer of 2010–2011 in Queensland, Australia. Using a representative sample of communication associated with the #qldfloods hashtag on Twitter, we coded and analysed the patterns of communication. We focus on key phenomena in the use of social media in crisis communication: communal sense-making practices, the negotiation of participant roles, and digital convergence around shared events. Social media is used both as a crisis communication and emergency management tool, as well as a space for participants to engage in emotional exchanges and communication of distress.Authored by Frances Shaw, Jean Burgess, Kate Crawford and Axel Bruns

Crisis Event Extraction Service (CREES) - Automatic Detection and Classification of Crisis-related Content on Social Media

Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings

On Identifying Disaster-Related Tweets: Matching-based or Learning-based?

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

Tweeting in Disaster Area: An Analysis of Tweets during 2016 Major Floods in Indonesia

Social media allows people in the disaster area to communicate disaster information, to the people outside the disaster area, more quickly and accurately. Unfortunately, there are limited researches that examine the use of Twitter by people in the disaster sites. This study aims to explore the use of Twitter by users in the disaster-affected areas. We use the feature of twitter geolocation, to separate information from inside and outside the disaster site. This research gives depiction about communication behavior of people in the affected disaster area, through social media. The result showed that people in disaster location use twitter to give first-hand report, coordinate rescue effort, provide help and express grief. In addition, by focusing on the affected area, Twitter used by lay people is usually found rather than other users. From the segment of time, the researcher finds a number of tweets that will increase each day. Users will share more information the days after rather, than the day of disaster. In practical term, this research explores the used of social media by the victims of disaster, which can encourage effective communication to people or group outside the location; theoretically, this research gives more detail understanding about shared information from the people in the disaster place

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models