COVID-19 Surveillance through Twitter using Self-Supervised and Few Shot Learning

Lwowski, Brandon; Najafirad, Peyman

doi:10.18653/v1/2020.nlpcovid19-2.9

Cited by 10 publications

(7 citation statements)

References 16 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning techniques were applied in [64,65] for epidemic monitoring. Few shot learning was used in [66] to fine-tune a semi-supervised model built on an unlabeled Covid-19 dataset. A BERT-based model for the Personal Health Mention Identification was employed in [67] for the detection of disease-infected individuals in tweets, and a spatial analysis is performed for the identification of disease-infected regions.…”

Section: A Epidemic Surveillance and Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Asaad¹,

Khaouja²,

Ghogho³

et al. 2022

Preprint

View full text Add to dashboard Cite

BACKGROUND Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media sites offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. OBJECTIVE The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. METHODS Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. RESULTS Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. CONCLUSIONS Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment.

show abstract

Section: A Epidemic Surveillance and Forecastingmentioning

confidence: 99%

“…Twitter [20,64,66,86] Ebola Twitter [68] Zika Twitter [68] Influenza/Flu Twitter [65] Multiple Epidemics Twitter [67] Mathematical Dengue Fever Twitter [82] Ebola Twitter [83] Influenza/Flu Twitter [79] Weibo [80] Zika Twitter [81] Multiple Epidemics Twitter [84] b. Public opinion understanding…”

Section: Classification Covid-19mentioning

confidence: 99%

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Asaad¹,

Khaouja²,

Ghogho³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In the second stage, the vanilla deep learning algorithm is applied to detect proximity [18]. A similar project under the TC4TL challenge compares several deep learning models including Conv 1d [29], support vector machines [40], and decision tree-based algorithms [14] to evaluate the accuracy of Bluetooth-based distance measurement [39,33]. The performance of different techniques is measured based the lowest normalized decision cost function (NDCF) which represents proximity detection performance considering the combination of false negatives and false positives.…”

Section: Contact Tracingmentioning

confidence: 99%

Machine learning applications for COVID-19: A state-of-the-art review

Kamalov¹,

Cherukuri²,

Sulieman³

et al. 2021

Preprint

View full text Add to dashboard Cite

The COVID-19 pandemic has galvanized the machine learning community to create new solutions that can help in the fight against the virus. The body of literature related to applications of machine learning and artificial intelligence to COVID-19 is constantly growing. The goal of this article is to present the latest advances in machine learning research applied to COVID-19. We cover four major areas of research: forecasting, medical diagnostics, drug development, and contact tracing. We review and analyze the most successful state of the art studies. In contrast to other existing surveys on the subject, our article presents a high level overview of the current research that is sufficiently detailed to provide an informed insight.

show abstract

“…Due to the fast-spreading nature of the infection, it is also difficult to manually trace the spread of the pandemic. However, with twitter event-specific entity extraction and Geo-location, one could potentially build a real-time pandemic surveillance system (Lwowski and Najafirad, 2020;Al-Garadi et al, 2020). Bal et al (2020) show that healthissues related misinformation is prevalent in social media, while Alam et al (2020) talks about covidspecific misinformation.…”

Section: Related Workmentioning

confidence: 99%

Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets

Kaushal

Vaidhya

2020

Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-Nut 2020)

View full text Add to dashboard Cite

Twitter has acted as an important source of information during disasters and pandemic, especially during the times of COVID-19. In this paper, we describe our system entry for WNUT 2020 Shared Task-3. The task was aimed at automating the extraction of a variety of COVID-19 related events from Twitter, such as individuals who recently contracted the virus, someone with symptoms who were denied testing and believed remedies against the infection. The system consists of separate multi-task models for slot-filling subtasks and sentence-classification subtasks while leveraging the useful sentence-level information for the corresponding event. The system uses COVID-Twitter-Bert with attention-weighted pooling of candidate slot-chunk features to capture the useful information chunks. The system ranks 1st at the leader-board with F1 of 0.6598, without using any ensembles or additional datasets. The code and trained models are available at this https url 1 .

show abstract

COVID-19 Surveillance through Twitter using Self-Supervised and Few Shot Learning

Cited by 10 publications

References 16 publications

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Machine learning applications for COVID-19: A state-of-the-art review

Winners at W-NUT 2020 Shared Task-3: Leveraging Event Specific and Chunk Span information for Extracting COVID Entities from Tweets

Contact Info

Product

Resources

About