{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:52:35Z","timestamp":1777697555455,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2023,5,15]]},"abstract":"<jats:p>Nowadays, people share their opinions through social media. This information may be informative or non-informative. Filtering informative information from social media plays a challenging issue. Nevertheless, people will interact more with that particular disaster event on social media, primarily when a disaster occurs. They share their opinion through some textual information such as tweets or posts. In this work, we propose a generalized approach for categorizing the informative and non-informative media on Twitter. We collected the seven natural disaster events from the crisisNLP. These datasets are different disaster events containing people\u2019s opinions on that specific event. We pre-process the information, which converts the tweet information into machine-understandable vectors. Various machine learning algorithms have processed these vectors. We consider the individual performance of each ML algorithm on different disaster datasets upon choosing the best five algorithms for voting techniques. We tested the performance with matrices such as accuracy, precision, recall, and F1-score. We compared our results with existing models in which our proposed model performed better than other existing state of the art models.<\/jats:p>","DOI":"10.3233\/idt-220310","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T11:54:37Z","timestamp":1681473277000},"page":"343-355","source":"Crossref","is-referenced-by-count":9,"title":["Disaster tweet classification: A majority voting approach using machine learning algorithms"],"prefix":"10.1177","volume":"17","author":[{"given":"Dasari Siva","family":"Krishna","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, GMR Institute of Technology, Rajam, India"},{"name":"Computer Science and Systems Engineering, Andhra University, Visakhapatnam, India"}]},{"given":"Gorla","family":"Srinivas","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, GITAM Deemed to be University, Visakhapatnam, India"}]},{"given":"P.V.G.D.","family":"Prasad Reddy","sequence":"additional","affiliation":[{"name":"Computer Science and Systems Engineering, Andhra University, Visakhapatnam, India"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-220310_ref1","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1145\/2487788.2488109","article-title":"Practical extraction of disaster-relevant information from social media","author":"Imran","year":"2013","journal-title":"Proceedings of the 22nd International Conference on World Wide Web"},{"key":"10.3233\/IDT-220310_ref2","first-page":"583","article-title":"Extracting situational information from microblogs during disaster events: a classification-summarization approach","author":"Rudra","year":"2015","journal-title":"Proceedings of the 24th ACM international conference on information and knowledge management"},{"key":"10.3233\/IDT-220310_ref3","first-page":"791","article-title":"Extracting information nuggets from disaster-Related messages in social media","author":"Imran","year":"2013","journal-title":"The 10th International Conference on Information Systems for Crisis Response and Management (ISCRAM)"},{"key":"10.3233\/IDT-220310_ref4","doi-asserted-by":"crossref","unstructured":"Alam F, Ofli F, Imran M. Crisismmd: Multimodal twitter datasets from natural disasters. In: Twelfth International AAAI Conference on Web and Social Media. 2018 June.","DOI":"10.1609\/icwsm.v12i1.14983"},{"key":"10.3233\/IDT-220310_ref5","unstructured":"Alam F, Ofli F, Imran M, Aupetit M. A twitter tale of three hurricanes: Harvey, Irma, and Maria. arXiv preprint arXiv:1805.05144. 2018."},{"key":"10.3233\/IDT-220310_ref6","unstructured":"Imran M, Mitra P, Castillo C. Twitter as a lifeline: Human-annotated twitter corpora for NLP of crisis-related messages. arXiv preprint arXiv:1605.05894. 2016."},{"key":"10.3233\/IDT-220310_ref7","doi-asserted-by":"crossref","unstructured":"Nguyen DT, Al Mannai KA, Joty S, Sajjad H, Imran M, Mitra P. Robust classification of crisis-related data on social networks using convolutional neural networks. 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