Using social media for sub-event detection during disasters
Abstract Social media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred afte...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-06-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-021-00467-1 |
| _version_ | 1831558353469308928 |
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| author | Loris Belcastro Fabrizio Marozzo Domenico Talia Paolo Trunfio Francesco Branda Themis Palpanas Muhammad Imran |
| author_facet | Loris Belcastro Fabrizio Marozzo Domenico Talia Paolo Trunfio Francesco Branda Themis Palpanas Muhammad Imran |
| author_sort | Loris Belcastro |
| collection | DOAJ |
| description | Abstract Social media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred after a disaster (e.g., collapsed buildings, broken gas pipes, floods). SEDOM-DD has been evaluated with datasets of different sizes that contain real posts from social media related to different natural disasters (e.g., earthquakes, floods and hurricanes). Starting from such data, we generated synthetic datasets with different features, such as different percentages of relevant posts and/or geotagged posts. Experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy. For example, with a percentage of relevant posts of 80% and geotagged posts of 15%, our method detects the sub-events and their areas with an accuracy of 85%, revealing the high accuracy and effectiveness of the proposed approach. |
| first_indexed | 2024-12-17T05:07:48Z |
| format | Article |
| id | doaj.art-388557d5e833433cb3f55edf4f65faa1 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-17T05:07:48Z |
| publishDate | 2021-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-388557d5e833433cb3f55edf4f65faa12022-12-21T22:02:21ZengSpringerOpenJournal of Big Data2196-11152021-06-018112210.1186/s40537-021-00467-1Using social media for sub-event detection during disastersLoris Belcastro0Fabrizio Marozzo1Domenico Talia2Paolo Trunfio3Francesco Branda4Themis Palpanas5Muhammad Imran6University of CalabriaUniversity of CalabriaUniversity of CalabriaUniversity of CalabriaUniversity of CalabriaUniversité de ParisQatar Computing Research InstituteAbstract Social media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred after a disaster (e.g., collapsed buildings, broken gas pipes, floods). SEDOM-DD has been evaluated with datasets of different sizes that contain real posts from social media related to different natural disasters (e.g., earthquakes, floods and hurricanes). Starting from such data, we generated synthetic datasets with different features, such as different percentages of relevant posts and/or geotagged posts. Experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy. For example, with a percentage of relevant posts of 80% and geotagged posts of 15%, our method detects the sub-events and their areas with an accuracy of 85%, revealing the high accuracy and effectiveness of the proposed approach.https://doi.org/10.1186/s40537-021-00467-1Social mediaEvents detectionNatural disastersCatastrophic eventsCrisis computingDisaster management |
| spellingShingle | Loris Belcastro Fabrizio Marozzo Domenico Talia Paolo Trunfio Francesco Branda Themis Palpanas Muhammad Imran Using social media for sub-event detection during disasters Journal of Big Data Social media Events detection Natural disasters Catastrophic events Crisis computing Disaster management |
| title | Using social media for sub-event detection during disasters |
| title_full | Using social media for sub-event detection during disasters |
| title_fullStr | Using social media for sub-event detection during disasters |
| title_full_unstemmed | Using social media for sub-event detection during disasters |
| title_short | Using social media for sub-event detection during disasters |
| title_sort | using social media for sub event detection during disasters |
| topic | Social media Events detection Natural disasters Catastrophic events Crisis computing Disaster management |
| url | https://doi.org/10.1186/s40537-021-00467-1 |
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