Papers

SWDM 2018 accepted five papers.

Public Sentiment and Demand for Used Cars after A Large-Scale Disaster: Social Media Sentiment Analysis with Facebook Pages

Yuya Shibuya and Hideyuki Tanaka

There have been various studies analyzing public sentiment after a large-scale disaster. However, few studies have focused on the relationship between public sentiment on social media and its results on people's activities in the real world. In this paper, we conduct a long-term sentiment analysis after the Great East Japan Earthquake and Tsunami of 2011 using Facebook Pages with the aim of investigating the correlation between public sentiment and people's actual needs in areas damaged by water disasters. In addition, we try to analyze whether different types of disaster-related communication created different kinds of relationships on people's activities in the physical world. Our analysis reveals that sentiment of geo-info-related communication, which might be affected by sentiment inside a damaged area, had a positive correlation with the prices of used cars in the damaged area. On the other hand, the sentiment of disaster-interest-based-communication, which might be affected more by people who were interested in the disaster, but were outside the damaged area, had a negative correlation with the prices of used cars. The result could be interpreted to mean that when people begin to recover, used-car prices rise because they become more positive in their sentiment. This study suggests that, for long-term disaster-recovery analysis, we need to consider the different characteristics of online communication posted by locals directly affected by the disaster and non-locals not directly affected by the disaster. [paper, slides]

A Pipeline for Post-Crisis Twitter Data Acquisition

Mayank Kejriwal and Yao Gu

Due to instant availability of data on social media platforms like Twitter, and advances in machine learning and data management technology, real-time crisis informatics has emerged as a prolific research area in the last decade. Although several benchmarks are now available, especially on portals like CrisisLex, an important, practical problem that has not been addressed thus far is the rapid acquisition and benchmarking of data from free, publicly available streams like the Twitter API. In this paper, we present ongoing work on a pipeline for facilitating immediate post-crisis data collection, curation and relevance filtering from the Twitter API. The pipeline is minimally supervised, alleviating the need for feature engineering by including a judicious mix of data preprocessing and fast text embeddings, along with an active learning framework. We illustrate the utility of the pipeline by describing a recent case study wherein it was used to collect and analyze millions of tweets in the immediate aftermath of the Las Vegas shootings. [paper, slides]

Demo: Unsupervised Hashtag Retrieval and Visualization for Crisis Informatics

Yao Gu and Mayank Kejriwal

In social media like Twitter, hashtags carry a lot of semantic information and can be easily distinguished from the main text. Exploring and visualizing the space of hashtags in a meaningful way can offer important insights into a dataset, especially in crisis situations. In this demonstration paper, we present a functioning prototype, HashViz, that ingests a corpus of tweets collected in the aftermath of a crisis situation (such as the Las Vegas shootings) and uses the fastText bag-of-tricks semantic embedding algorithm (from Facebook Research) to embed words and hashtags into a vector space. Hashtag vectors obtained in this way can be visualized using the t-SNE dimensionality reduction algorithm in 2D. Although multiple Twitter visualization platforms exist, HashViz is distinguished by being simple, scalable, interactive and portable enough to be deployed on a server for million-tweet corpora collected in the aftermath of arbitrary disasters, without special-purpose installation, technical expertise, manual supervision or costly software or infrastructure investment. Although simple, we show that HashViz offers an intuitive way to summarize, and gain insight into, a developing crisis situation. HashViz is also completely unsupervised, requiring no manual inputs to go from a raw corpus to a visualization and search interface. Using the recent Las Vegas mass shooting massacre as a case study, we illustrate the potential of HashViz using only a web browser on the client side. [paper, slides]

Social Media Data Analysis and Feedback for Advanced Disaster Risk Management

Markus Enenkel, Sofía Martinez Sáenz, Denyse Dookie, Lisette Braman, Nick Obradovich and Yury Kryvasheyeu

Social media are more than just a one-way communication channel. Data can be collected, analyzed and contextualized to support disaster risk management. However, disaster management agencies typically use such added-value information to support only their own decisions. A feedback loop between contextualized information and data suppliers would result in various advantages. First, it could facilitate the near real-time communication of early warnings derived from social media, linked to other sources of information. Second, it could support the staff of aid organizations during response operations. Based on the example of Hurricanes Harvey and Irma we show how filtered, geolocated Tweets can be used for rapid damage assessment. We claim that the next generation of big data analyses will have to generate actionable information resulting from the application of advanced analytical techniques. These applications could include the provision of social media-based training data for algorithms designed to forecast actual cyclone impacts or new socio-economic validation metrics for seasonal climate forecasts. [paper]

Geolocating social media posts for emergency mapping

Barbara Pernici, Chiara Francalanci, Gabriele Scalia, Marco Corsi, Domenico Grandoni and Mariano A. Biscardi

The demo will illustrate the features of a webGIS interface to support the rapid mapping activities after a natural disaster, with the goal of providing additional information from social media to the mapping operators. This demo shows the first results of the E2mC H2020 European project, where the goal is to extract precisely located information from available social media sources, providing accurate geolocating functionalities and, starting from posts searched in Twitter, extending the social media exploration to Flickr, YouTube, and Instagram. [paper, slides]

Please note these links are provided for the convenience of workshop attendees but the workshop does not have formal proceedings.