Automatic Labeling of Tweets for Crisis Response Using Distant Supervision

Alrashdi, Reem; O’Keefe, Simon

doi:10.1145/3366424.3383757

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…The exploitation of tweets can be further improved by utilising the photos associated with them as a two-step validation of the reliability of social-media. In addition, the novel, latest published, approaches for the automatic labelling of tweets [48], [49], [50], [51], such as the one proposed by Alrashdi et al [49] using distant supervision and the approach by Roy et al for the classification and summarising of informative tweets [50], shall be evaluated, compared to the current methodology.…”

Section: Discussionmentioning

confidence: 99%

Towards a Social-media Driven Multi-Drone Tasking platform

Terzi

Kolios

Panayiotou

et al. 2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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We present a framework for utilising social-media for tasking drone swarms in search and rescue missions. As elaborated in this work, social-media are a source of timely and valuable information regarding the occurrence, the evolution and the damage produced in disasters. Exploiting this source of knowledge can be very valuable in planning search and rescue missions and identifying tasks to be conducted. In this work a novel multi-drone search and rescue framework is developed that considers social-media signals in the creation of search tasks. The key design challenges of implementing the proposed framework are discussed and a simulated scenario based on a real-life Twitter data set is presented as a means of providing evidence of the effectiveness of the proposed framework.

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Section: Discussionmentioning

confidence: 99%

Towards a Social-media Driven Multi-Drone Tasking platform

Terzi

Kolios

Panayiotou

et al. 2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…Distant Supervision Methods. Distant supervision is a successful paradigm that gathers training data for event extraction systems by automatically aligning vast databases of facts with the text [90,91,92,93,94]. For example, Reschke et al [90] present a new publicly available dataset and use the distant supervision approach to plane crash events.…”

Section: Semi-supervised and Distant Supervision Methodsmentioning

confidence: 99%

“…Experimental results show the proposed data augmentation framework outperforms other benchmark methods. To solve data lack and imbalance in coverage of crisis types, Alrashdi and O'Keefe [93] utilize distant supervision to automatically generate large-scale labeled tweet data for crisis response.…”

Section: Semi-supervised and Distant Supervision Methodsmentioning

confidence: 99%

An overview of event extraction and its applications

Liu¹,

Min²,

Huang³

2021

Preprint

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With the rapid development of information technology, online platforms have produced enormous text resources. As a particular form of Information Extraction (IE), Event Extraction (EE) has gained increasing popularity due to its ability to automatically extract events from human language. However, there are limited literature surveys on event extraction. Existing review works either spend much effort describing the details of various approaches or focus on a particular field. This study provides a comprehensive overview of the state-of-the-art event extraction methods and their applications from text, including closed-domain and open-domain event extraction. A trait of this survey is that it provides an overview in moderate complexity, avoiding involving too many details of particular approaches. This study focuses on discussing the common characters, application fields, advantages, and disadvantages of representative works, ignoring the specificities of individual approaches. Finally, we summarize the common issues, current solutions, and future research directions. We hope this work could help researchers and practitioners obtain a quick overview of recent event extraction.

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“…Various methods of domain adaptation have been suggested for addressing this issue Sopova, 2017;Alrashdi and O'Keefe, 2020). However, this type of supervised classification assumes that relevant classes remain the same from event to event.…”

Section: Limitations Of Current Methodsmentioning

confidence: 99%

Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management

Brunila,

Zhao,

Mircea

et al. 2021

Preprint

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Social media such as Twitter provide valuable information to crisis managers and affected people during natural disasters. Machine learning can help structure and extract information from the large volume of messages shared during a crisis; however, the constantly evolving nature of crises makes effective domain adaptation essential. Supervised classification is limited by unchangeable class labels that may not be relevant to new events, and unsupervised topic modelling by insufficient prior knowledge. In this paper, we bridge the gap between the two and show that BERT embeddings finetuned on crisis-related tweet classification can effectively be used to adapt to a new crisis, discovering novel topics while preserving relevant classes from supervised training, and leveraging bidirectional self-attention to extract topic keywords. We create a dataset of tweets from a snowstorm to evaluate our method's transferability to new crises, and find that it outperforms traditional topic models in both automatic, and human evaluations grounded in the needs of crisis managers. More broadly, our method can be used for textual domain adaptation where the latent classes are unknown but overlap with known classes from other domains.

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Automatic Labeling of Tweets for Crisis Response Using Distant Supervision

Cited by 12 publications

References 26 publications

Towards a Social-media Driven Multi-Drone Tasking platform

Towards a Social-media Driven Multi-Drone Tasking platform

An overview of event extraction and its applications

Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management

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