Document Type
Article
Source of Publication
IEEE Access
Publication Date
1-1-2021
Abstract
The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams synthesizes event-specific information, e.g., a terrorist attack, and alerts law enforcement, emergency services, and media outlets. Specifically, the proposed framework, Tweet-to-Act (T2A), employs word embedding to transform tweets into a vector space model and then utilizes the Word Mover's Distance (WMD) to cluster tweets for the identification of incidents. To extract reliable and valuable information from a large dataset of short and informal tweets, the proposed framework employs sequence labeling with bidirectional Long Short-Term Memory based Recurrent Neural Networks (bLSTM-RNN). Extensive experimental results suggest that our proposed framework, T2A, outperforms other state-of-the-art methods that use vector space modeling and distance calculation techniques, e.g., Euclidean and Cosine distance. T2A achieves an accuracy of 96% and an F1-score of 86.2% on real-life datasets.
DOI Link
ISSN
Publisher
IEEE
Volume
9
First Page
115535
Last Page
115547
Disciplines
Communication | Computer Sciences
Keywords
Social networking (online), Blogs, Terrorism, Data mining, Feature extraction, Media, Monitoring, Terrorist attacks, news, word embedding, word mover's distance, recurrent neural network, information extraction, bidirectional long short-term memory
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Iqbal, Farkhund; Batool, Rabia; Fung, Benjamin C. M.; Aleem, Saiqa; Abbasi, Ahmed; and Javed, Abdul Rehman, "Toward Tweet-Mining Framework for Extracting Terrorist Attack-Related Information and Reporting" (2021). All Works. 4792.
https://zuscholars.zu.ac.ae/works/4792
Indexed in Scopus
no
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series