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Author  |
Kiran Zahra; Muhammad Imran; Frank O Ostermann |
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Title |
Understanding eyewitness reports on Twitter during disasters |
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Conference Article |
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Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Pages |
687-695 |
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Keywords |
social media, disaster response, eyewitness accounts |
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Abstract |
Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other uses. However, identification of eyewitness reports on Twitter is challenging for many reasons. This work investigates the sources of tweets and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitness, and (iii) vulnerable accounts. Moreover, we investigate various characteristics associated with each kind of eyewitness account. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We believe these characteristics can help make more efficient computational methods and systems in the future for automatic identification of eyewitness accounts. |
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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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Language |
English |
Summary Language |
English |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
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Track |
Social Media Studies CO - |
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no |
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Call Number |
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Serial |
2142 |
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Author |
Kiran Zahra; Rahul Deb Das; Frank O. Ostermann; Ross S. Purves |
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Title |
Towards an Automated Information Extraction Model from Twitter Threads during Disasters |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Pages |
637-653 |
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Keywords |
Social media threads; Text summarization; Disasters; Lexicons; Information extraction models; Word embeddings |
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Abstract |
Social media plays a vital role as a communication source during large-scale disasters. The unstructured and informal nature of such short individual posts makes it difficult to extract useful information, often due to a lack of additional context. The potential of social media threads– sequences of posts– has not been explored as a source of adding context and more information to the initiating post. In this research, we explored Twitter threads as an information source and developed an information extraction model capable of extracting relevant information from threads posted during disasters. We used a crowdsourcing platform to determine whether a thread adds more information to the initial tweet and defined disaster-related information present in these threads into six themes– event reporting, location, time, intensity, casualty and damage reports, and help calls. For these themes, we created the respective thematic lexicons from WordNet. Moreover, we developed and compared four information extraction models trained on GloVe, word2vec, bag-of-words, and thematic bag-of-words to extract and summarize the most critical information from the threads. Our results reveal that 70 percent of all threads add information to the initiating post for various disaster-related themes. Furthermore, the thematic bag-of-words information extraction model outperforms the other algorithms and models for preserving the highest number of disaster-related themes. |
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University of Zurich; University of Zurich, IBM; University of Twente; University of Zurich |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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Language |
English |
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ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
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Track |
Social Media for Crisis Management |
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no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2444 |
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