In the midst of a global pandemic, understanding the public’s opinion of their government’s policy-level, non-pharmaceutical interventions (NPIs) is a crucial component of the health-policy-making process. Prior work on CoViD-19 NPI sentiment analysis by the epidemiological community has proceeded without a method for properly attributing sentiment changes to events, an ability to distinguish the influence of various events across time, a coherent model for predicting the public’s opinion of future events of the same sort, nor even a means of conducting significance tests. We argue here that this urgently needed evaluation method does already exist. In the financial sector, event studies of the fluctuations in a publicly traded company’s stock price are commonplace for determining the effects of earnings announcements, product placements, etc. The same method is suitable for analysing temporal sentiment variation in the light of policy-level NPIs. We provide a case study of Twitter sentiment towards policy-level NPIs in Canada. Our results confirm a generally positive connection between the announcements of NPIs and Twitter sentiment, and we document a promising correlation between the results of this study and a public-health survey of popular compliance with NPIs.

We present an algorithm based on multi-layer transformers for identifying Adverse Drug Reactions (ADR) in social media data. Our model relies on the properties of the problem and the characteristics of contextual word embeddings to extract two views from documents. Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view. Finally, the initialized classifier in each view is further trained using the initial training examples. We evaluated our model in the largest publicly available ADR dataset. The experiments testify that our model significantly outperforms the transformer-based models pretrained on domain-specific data.

Detection of occupations in texts is relevant for a range of important application scenarios, like competitive intelligence, sociodemographic analysis, legal NLP or health-related occupational data mining. Despite the importance and heterogeneous data types that mention occupations, text mining efforts to recognize them have been limited. This is due to the lack of clear annotation guidelines and high-quality Gold Standard corpora. Social media data can be regarded as a relevant source of information for real-time monitoring of at-risk occupational groups in the context of pandemics like the COVID-19 one, facilitating intervention strategies for occupations in direct contact with infectious agents or affected by mental health issues. To evaluate current NLP methods and to generate resources, we have organized the ProfNER track at SMM4H 2021, providing ProfNER participants with a Gold Standard corpus of manually annotated tweets (human IAA of 0.919) following annotation guidelines available in Spanish and English, an occupation gazetteer, a machine-translated version of tweets, and FastText embeddings. Out of 35 registered teams, 11 submitted a total of 27 runs. Best-performing participants built systems based on recent NLP technologies (e.g. transformers) and achieved 0.93 F-score in Text Classification and 0.839 in Named Entity Recognition. Corpus: https://doi.org/10.5281/zenodo.4309356

The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health. The Social Media Mining for Health Applications (#SMM4H) shared tasks in its sixth iteration sought to advance the use of social media texts such as Twitter for pharmacovigilance, disease tracking and patient centered outcomes. #SMM4H 2021 hosted a total of eight tasks that included reruns of adverse drug effect extraction in English and Russian and newer tasks such as detecting medication non-adherence from Twitter and WebMD forum, detecting self-reported adverse pregnancy outcomes, detecting cases and symptoms of COVID-19, identifying occupations mentioned in Spanish by Twitter users, and detecting self-reported breast cancer diagnosis. The eight tasks included a total of 12 individual subtasks spanning three languages requiring methods for binary classification, multi-class classification, named entity recognition and entity normalization. With a total of 97 registering teams and 40 teams submitting predictions, the interest in the shared tasks grew by 70% and participation grew by 38% compared to the previous iteration.

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2021 shared tasks. We participated in 2 tasks: (1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms (Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific pre-processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the median score across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.

This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task. We participated in two tasks on classification of tweets that mention an adverse drug effect (ADE) (Tasks 1a & 2) and two tasks on extraction of ADE concepts (Tasks 1b & 1c). For classification, we investigate the impact of joint use of BERTbased language models and drug embeddings obtained by chemical structure BERT-based encoder. The BERT-based multimodal models ranked first and second on classification of Russian (Task 2) and English tweets (Task 1a) with the F1 scores of 57% and 61%, respectively. For Task 1b and 1c, we utilized the previous year’s best solution based on the EnDR-BERT model with additional corpora. Our model achieved the best results in Task 1c, obtaining an F1 of 29%.

This paper presents our contribution to the Social Media Mining for Health Applications Shared Task 2021. We addressed all the three subtasks of Task 1: Subtask A (classification of tweets containing adverse effects), Subtask B (extraction of text spans containing adverse effects) and Subtask C (adverse effects resolution). We explored various pre-trained transformer-based language models and we focused on a multi-task training architecture. For the first subtask, we also applied adversarial augmentation techniques and we formed model ensembles in order to improve the robustness of the prediction. Our system ranked first at Subtask B with 0.51 F1 score, 0.514 precision and 0.514 recall. For Subtask A we obtained 0.44 F1 score, 0.49 precision and 0.39 recall and for Subtask C we obtained 0.16 F1 score with 0.16 precision and 0.17 recall.

This paper describes our approach for six classification tasks (Tasks 1a, 3a, 3b, 4 and 5) and one span detection task (Task 1b) from the Social Media Mining for Health (SMM4H) 2021 shared tasks. We developed two separate systems for classification and span detection, both based on pre-trained Transformer-based models. In addition, we applied oversampling and classifier ensembling in the classification tasks. The results of our submissions are over the median scores in all tasks except for Task 1a. Furthermore, our model achieved first place in Task 4 and obtained a 7% higher F1-score than the median in Task 1b.

This paper describes our team’s submission for the Social Media Mining for Health (SMM4H) 2021 shared task. We participated in three subtasks: Classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms. Our system is based on BERT model pre-trained on the domain-specific text. In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance. We achieved the first rank in both classifying adverse drug effects and COVID-19 self-report tasks.

This work describes the participation of the Universidad Autónoma de Chihuahua - Instituto Nacional de Astrofísica, Óptica y Electrónica team at the Social Media Mining for Health Applications (SMM4H) 2021 shared task. Our team participated in task 5 and 6, both focused on the automatic classification of Twitter posts related to COVID-19. Task 5 was oriented on solving a binary classification problem, trying to identify self-reporting tweets of potential cases of COVID-19. Task 6 objective was to classify tweets containing COVID-19 symptoms. For both tasks we used models based on bidirectional encoder representations from transformers (BERT). Our objective was to determine if a model pretrained on a corpus in the domain of interest can outperform one trained on a much larger general domain corpus. Our F1 results were encouraging, 0.77 and 0.95 for task 5 and 6 respectively, having achieved the highest score among all the participants in the latter.

This shared task system description depicts two neural network architectures submitted to the ProfNER track, among them the winning system that scored highest in the two sub-tasks 7a and 7b. We present in detail the approach, preprocessing steps and the architectures used to achieve the submitted results, and also provide a GitHub repository to reproduce the scores. The winning system is based on a transformer-based pretrained language model and solves the two sub-tasks simultaneously.

ProfNER-ST focuses on the recognition of professions and occupations from Twitter using Spanish data. Our participation is based on a combination of word-level embeddings, including pre-trained Spanish BERT, as well as cosine similarity computed over a subset of entities that serve as input for an encoder-decoder architecture with attention mechanism. Finally, our best score achieved an F1-measure of 0.823 in the official test set.

This is the system description of the CASIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. Targeting on deal with two shared challenges, the colloquial text and the imbalance annotation, among those tasks, we apply a customized pre-trained language model and propose various training strategies. Experimental results show the effectiveness of our system. Moreover, we got an F1-score of 0.87 in task 8, which is the highest among all participates.

This paper presents our findings from participating in the SMM4H Shared Task 2021. We addressed Named Entity Recognition (NER) and Text Classification. To address NER we explored BiLSTM-CRF with Stacked Heterogeneous embeddings and linguistic features. We investigated various machine learning algorithms (logistic regression, SVM and Neural Networks) to address text classification. Our proposed approaches can be generalized to different languages and we have shown its effectiveness for English and Spanish. Our text classification submissions have achieved competitive performance with F1-score of 0.46 and 0.90 on ADE Classification (Task 1a) and Profession Classification (Task 7a) respectively. In the case of NER, our submissions scored F1-score of 0.50 and 0.82 on ADE Span Detection (Task 1b) and Profession span detection (Task 7b) respectively.

This paper describes models developed for the Social Media Mining for Health (SMM4H) 2021 shared tasks. Our team participated in the first subtask that classifies tweets with Adverse Drug Effect (ADE) mentions. Our best performing model utilizes BERTweet followed by a single layer of BiLSTM. The system achieves an F-score of 0.45 on the test set without the use of any auxiliary resources such as Part-of-Speech tags, dependency tags, or knowledge from medical dictionaries.

In this work we describe our submissions to the Social Media Mining for Health (SMM4H) 2021 Shared Task. We investigated the effectiveness of a joint training approach to Task 1, specifically classification, extraction and normalization of Adverse Drug Effect (ADE) mentions in English tweets. Our approach performed well on the normalization task, achieving an above average f1 score of 24%, but less so on classification and extraction, with f1 scores of 22% and 37% respectively. Our experiments also showed that a larger dataset with more negative results led to stronger results than a smaller more balanced dataset, even when both datasets have the same positive examples. Finally we also submitted a tuned BERT model for Task 6: Classification of Covid-19 tweets containing symptoms, which achieved an above average f1 score of 96%.

The paper researches the problem of drug adverse effect detection in texts of social media. We describe the development of such classification system for Russian tweets. To increase the train dataset we apply a couple of augmentation techniques and analyze their effect in comparison with similar systems presented at 2021 years’ SMM4H Workshop.

This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4) and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the mean score of 0.925. For Task 5, our best of 0.75 exceeded the mean score of 0.745.

In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.

In this paper we present our approach and system description on Task 7a in ProfNer-ST: Identification of profession & occupation in Health related Social Media. Our main contribution is to show the effectiveness of using BETO-Spanish BERT as a model based on transformers pretrained with a Spanish Corpus for classification tasks. In our experiments we compared several architectures based on transformers with others based on classical machine learning algorithms. With this approach, we achieved an F1-score of 0.92 in the evaluation process.

The paper describes the participation of the Lasige-BioTM team at sub-tracks A and B of ProfNER, which was based on: i) a BiLSTM-CRF model that leverages contextual and classical word embeddings to recognize and classify the mentions, and ii) on a rule-based module to classify tweets. In the Evaluation phase, our model achieved a F1-score of 0.917 (0,031 more than the median) in sub-track A and a F1-score of 0.727 (0,034 less than the median) in sub-track B.

In this paper, we describe our system entry for Shared Task 8 at SMM4H-2021, which is on automatic classification of self-reported breast cancer posts on Twitter. In our system, we use a transformer-based language model fine-tuning approach to automatically identify tweets in the self-reports category. Furthermore, we involve a Gradient-based Adversarial fine-tuning to improve the overall model’s robustness. Our system achieved an F1-score of 0.8625 on the Development set and 0.8501 on the Test set in Shared Task-8 of SMM4H-2021.

This paper describes the participation of the UoB-NLP team in the ProfNER-ST shared subtask 7a. The task was aimed at detecting the mention of professions in social media text. Our team experimented with two methods of improving the performance of pre-trained models: Specifically, we experimented with data augmentation through translation and the merging of multiple language inputs to meet the objective of the task. While the best performing model on the test data consisted of mBERT fine-tuned on augmented data using back-translation, the improvement is minor possibly because multi-lingual pre-trained models such as mBERT already have access to the kind of information provided through back-translation and bilingual data.

With increasing users sharing health-related information on social media, there has been a rise in using social media for health monitoring and surveillance. In this paper, we present a system that addresses classic health-related binary classification problems presented in Tasks 1a, 4, and 8 of the 6th edition of Social Media Mining for Health Applications (SMM4H) shared tasks. We developed a system based on RoBERTa (for Task 1a & 4) and BioBERT (for Task 8). Furthermore, we address the challenge of the imbalanced dataset and propose techniques such as undersampling, oversampling, and data augmentation to overcome the imbalanced nature of a given health-related dataset.

Since the outbreak of coronavirus at the end of 2019, there have been numerous studies on coro- navirus in the NLP arena. Meanwhile, Twitter has been a valuable source of news and a pub- lic medium for the conveyance of information and personal expression. This paper describes the system developed by the Ochadai team for the Social Media Mining for Health Appli- cations (SMM4H) 2021 Task 5, which aims to automatically distinguish English tweets that self-report potential cases of COVID-19 from those that do not. We proposed a model ensemble that leverages pre-trained represen- tations from COVID-Twitter-BERT (Müller et al., 2020), RoBERTa (Liu et al., 2019), and Twitter-RoBERTa (Glazkova et al., 2021). Our model obtained F1-scores of 76% on the test set in the evaluation phase, and 77.5% in the post-evaluation phase.

This paper describes our system developed for the subtask 1c of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. The aim of the subtask is to recognize the adverse drug effect (ADE) mentions from tweets and normalize the identified mentions to their mapping MedDRA preferred term IDs. Our system is based on a neural transition-based joint model, which is to perform recognition and normalization simultaneously. Our final two submissions outperform the average F1 score by 1-2%.

This paper presents our contribution to the ProfNER shared task. Our work focused on evaluating different pre-trained word embedding representations suitable for the task. We further explored combinations of embeddings in order to improve the overall results.

We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the other task. In this paper, we additionally explore how much fine- tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).

The reported work is a description of our participation in the “Classification of COVID19 tweets containing symptoms” shared task, organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. The literature describes two machine learning approaches that were used to build a three class classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. Both the developed learning models, when evaluated by the organizers, garnered F1 scores of 0.93 and 0.92 respectively.

Twitter is a valuable source of patient-generated data that has been used in various population health studies. The first step in many of these studies is to identify and capture Twitter messages (tweets) containing medication mentions. Identifying personal mentions of COVID19 symptoms requires distinguishing personal mentions from other mentions such as symptoms reported by others and references to news articles or other sources. In this article, we describe our submission to Task 6 of the Social Media Mining for Health Applications (SMM4H) Shared Task 2021. This task challenged participants to classify tweets where the target classes are:(1) self-reports,(2) non-personal reports, and (3) literature/news mentions. Our system used a handcrafted preprocessing and word embeddings from BERT encoder model. We achieved an F1 score of 93%

This paper describes the entry of the research group SINAI at SMM4H’s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses.

We describe our submissions to the 6th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (OGNLP) participated in the sub-task: Classification of tweets self-reporting potential cases of COVID-19 (Task 5). For our submissions, we employed systems based on auto-regressive transformer models (XLNet) and back-translation for balancing the dataset.

Social media platforms such as Twitter and Facebook have been utilised for various research studies, from the cohort-level discussion to community-driven approaches to address the challenges in utilizing social media data for health, clinical and biomedical information. Detection of medical jargon’s, named entity recognition, multi-word expression becomes the primary, fundamental steps in solving those challenges. In this paper, we enumerate the ULD-NUIG team’s system, designed as part of Social Media Mining for Health Applications (#SMM4H) Shared Task 2021. The team conducted a series of experiments to explore the challenges of task 6 and task 5. The submitted systems achieve F-1 0.84 and 0.53 score for task 6 and 5 respectively.