| Challenge: | Our system detects heavy rain disaster using social and physical sensors. |
| Approach: | They propose a system that detects heavy rain disaster by analyzing tweets and physical sensors. |
| Outcome: | The proposed system detects heavy rain disaster using social and physical sensors in Japan. |
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| Challenge: | Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. |
| Approach: | They propose to train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification. |
| Outcome: | The proposed model removes event-specific biases and improves on tweet importance classification. |
Multimodal Semi-supervised Learning for Disaster Tweet Classification (2022.coling-1)
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| Challenge: | During natural disasters, people use social media platforms to post information about casualties and damage . annotating data can be burdensome, subjective and expensive . et al., 2018b; sohn e.t., 2020) proposed semi-supervised multimodal approach to improve performance on multimodal tasks. |
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Natural Disaster Tweets Classification Using Multimodal Data (2023.emnlp-main)
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| Challenge: | Social media platforms are used for expressing opinions or conveying information. |
| Approach: | They propose a hierarchical system that can integrate multimodal data and perform sequential hierarchic classification. |
| Outcome: | The proposed system can find the damage and its severity along with classify the data into humanitarian categories. |
Social Media Attributions in the Context of Water Crisis (2020.emnlp-main)
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| Challenge: | In this paper, we analyze social media discussions to identify attribution factors for natural disasters/collective misfortunes. |
| Approach: | They propose a task of attribution tie detection to identify factors held responsible for a water crisis in a social media document. |
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FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events (2020.lrec-1)
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| Challenge: | Detecting events from tweets can help to predict real-world events precisely. |
| Approach: | They propose to use tweets written in Arabic and Saudi dialects to identify events from tweets. |
| Outcome: | The proposed system can detect flood, dust storm, traffic accident, and non-event. |
SEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia (N19-3)
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| Challenge: | Recent work on event detection from tweets has focused on localized events or breaking news only. |
| Approach: | They propose to split tweets into segments, extract bursty segments, cluster them, summarize them. |
| Outcome: | The proposed system can detect newsworthy events occurring at different locations of the world from a wide range of categories. |
Recognizing Social Cues in Crisis Situations (2024.lrec-main)
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| Challenge: | During natural disasters, observations of other people's behavior can play an essential role in a person's decision-making. |
| Approach: | They propose a task to categorize social cues in tweets during crisis situations using an annotated dataset of 6,000 tweets. |
| Outcome: | The proposed task is challenging for existing systems and a manual task is based on a dataset of 6,000 tweets labeled with eight social cue categories. |
Detecting Perceived Emotions in Hurricane Disasters (2020.acl-main)
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| Challenge: | Existing methods for emotion detection are limited in disaster-centric domains due to distributional shifts. |
| Approach: | They propose to use a Twitter emotion dataset to analyze emotions in natural disasters . they propose to apply classification tasks to discriminate between coarse-grained emotions . |
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Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets (2020.coling-main)
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| Challenge: | Despite the widespread use of Twitter during emergencies, the majority of tweets do not have geoinformation. |
| Approach: | They propose to use Twitter to train location mention recognition models using different training settings. |
| Outcome: | The results show that training on near or far-away events boosts the performance compared to training on distant events. |
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)
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| Challenge: | Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare . |
| Approach: | They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods . |
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