Korea, Republic of

With the Surge in COVID-19, the number of social media postings related to the vaccine has grown, specifically tracing the confirmed reports by the users regarding the COVID-19 vaccine dose termed as Vaccine Surveillance. To mitigate this research problem, we present our novel ensembled approach for self-reporting COVID-19 vaccination status tweets into two labels, namely Vaccine Chatter and Self Report. We utilize state-of-the-art models, namely BERT, RoBERTa, and XLNet. We also present an empirical analysis of the results to present how well the tweet was able to classify and report.

COLING 2022

An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets

promising results

efficient methodology

novel idea

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

This paper presents our submission for the Shared Task-2 of classification of stance and premise in tweets about health mandates re- lated to COVID-19 at the Social Media Mining for Health 2022. There have been a plethora of tweets about people expressing their opinions on the COVID-19 epidemic since it first emerged. The shared task emphasizes finding the level of cooperation within the mandates for their stance towards the health orders of the pandemic. Overall the shared subjects the participants to propose system’s that can efficiently perform 1) Stance Detection, which focuses on determining the author’s point of view in
the text. 2) Premise Classification, which indicates whether or not the text has arguments. Through this paper we propose an orchestra- tion of multiple transformer based encoders to derive the output for stance and premise clas- sification. Our best model achieves a F1 score of 0.771 for Premise Classification and an aggregate macro-F1 score of 0.661 for Stance Detection

An Ensembles Approach for Classification of Stance and Premise in COVID-19 Related Tweets

This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task. We explored the limits of Transformer based models on text classification, entity extraction and entity normalization, tackling
Tasks 1, 2, 5, 6 and 10. The main takeaways we got from participating in different
tasks are: the overwhelming positive effects of combining different architectures when using ensemble learning, and the great potential of generative models for term normalization.

Limits of Transformers and BERT Ensembles

This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English). The goal of this task was to accurately determine if the contents of a given tweet demonstrated someone reporting their own experience with intimate partner violence. The submitted system is an ensemble of five RoBERTa models each weighted by their respective F1-scores on the validation data-set. This system performed 13% better than the baseline and was the best performing system overall for this shared task.

Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models

With the increase in the use of social media, people have become more outspoken and are using platforms like Reddit, Facebook, and Twitter to express their views and share the medical challenges they are facing. This data is a valuable source of medical insight and is often used for healthcare research. This paper describes our participation in Task 1a, 2a, 2b, 3, 5, 6, 7, and 9 organized by SMM4H 2022. We have proposed two transformer-based approaches to handle the classification tasks. The first approach is fine-tuning single language models. The second approach is ensembling the results of BERT, RoBERTa, and ERNIE 2.0.

Classification of HealthRelated Tweets Using Ensemble, Zero-Shot andFine-Tuned Language Model

This paper presents our approaches to SMM4H’22 task 5 - Classification of tweets of self-reported COVID-19 symptoms in Spanish, and task 10 - Detection of disease mentions in tweets – SocialDisNER (in Spanish). We have presented hybrid systems that combine Deep Learning techniques with linguistic rules and medical ontologies, which have allowed us to achieve outstanding results in both tasks.

Tasks 5 \& 10 - Hybrid models everywhere

This paper reports on the performance of Edinburgh_UCL_Health's models in the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the 
classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine). Our best performing models are based on DeepADEMiner (with respective F1= 0.64, 0.62 and 0.39 for ADE identification), on a GloVe model trained on Twitter (with F1=0.11 for the change-med) and finally on a stack embedding including a layer of Glove embedding and two layers of Flair 
embedding (with F1= 0.77 for self-report).

From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination

This paper describes models developed for the Social Media Mining for Health 2022 shared tasks. Our team participated in the first sub-task that classifies tweets with Adverse Drug Effect mentions. Our best-performing model comprises of a template augmented task adaptive pre-training and further fine-tuning on target task data. Augmentation with random prompt templates increases the amount of task-specific data to generalize the pretrained language model to the target task domain. We explore 2 pre-training strategies: masked language modeling and simple contrastive pre-training and the impact of adding template augmentations with these pre-training strategies. Our system achieves an F1 score of 0.433 on the test set without using supplementary resources and medical dictionaries.

Template Augmented Adaptive Pre-training for Tweet Classification

This poster presentation covers our approaches for the Social Media Mining for Health (SMM4H) Shared Tasks 2a and 2b. Apart from the baseline architectures, we experiment with Parts of Speech (PoS), dependency parsing, and Tf-Idf features. Additionally, we perform contrastive pretraining on our best models using a supervised contrastive loss function. In both the tasks, we outperformed the mean and median scores and ranked first on the validation set. For stance classification, we achieved an F1-score of 0.636 using the CovidTwitterBERT model, while for premise classification, we achieved an F1-score of 0.664 using BART-base model on test dataset.

Leveraging Pre-trained Language Models for Stance And Premise Classification

We present our response to Task 5 of the Social Media Mining for Health Applications (SMM4H) 2022 competition. Our goal was to classify whether a tweet in Spanish regarding COVID-19 symptoms pertained to the author of the tweet, others, or presented news regarding COVID-19. Using ensembles of BERT-based models, we were able to achieve results that were higher than the median result of the competition. 


Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling

This paper presents my submission for Tasks 1 and 2 for the Social Media Mining for Health (SMM4H) 2022 Shared Tasks competition. I first describe the background behind each of these tasks, followed by the descriptions of the various subtasks of Tasks 1 and 2, then present the methodology. Through model ensembling, this methodology was able to achieve higher results than the mean and median of the competition for classification tasks.

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An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets

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An Ensembles Approach for Classification of Stance and Premise in COVID-19 Related Tweets

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