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Register a new userClassification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers
تصنيف تغريدات نتائج الحمل المعاكسة ذات الإبلاغ عنها وإجراء حالات كوفي 19 محتملة باستخدام محولات روبرتا
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تصف هذه الدراسة تصميمنا النموذج المقترح لمهام SMM4H 2021 المشتركة.نحن نغلب النموذج اللغوي لمحولات روبرتا ومصنفهم التوصيل لإكمال مهام التصنيف في تغريدات لنتائج الحمل المعاكسة (المهمة 4) والحالات Covid-19 المحتملة (المهمة 5).متري التقييم هو درجة F1 للفئة الإيجابية لكلا المهام.بالنسبة للمهمة 4، تجاوزت نقاط لدينا أفضل 0.93 درجة متوسط 0.925.بالنسبة للمهمة 5، تجاوزت أفضل ما بين 0.75 درجة متوسط 0.745.
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.
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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 othe
r 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 spread of COVID-19 has been accompanied with widespread misinformation on social media. In particular, Twitterverse has seen a huge increase in dissemination of distorted facts and figures. The present work aims at identifying tweets regarding CO
VID-19 which contains harmful and false information. We have experimented with a number of Deep Learning-based models, including different word embeddings, such as Glove, ELMo, among others. BERTweet model achieved the best overall F1-score of 0.881 and secured the third rank on the above task.
The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. In recent years, supervis
ed machine learning models have been used to automatically identify false information in social media. However, most of these machine learning models focus only on the language they were trained on. Given the fact that social media platforms are being used in different languages, managing machine learning models for each and every language separately would be chaotic. In this research, we experiment with multilingual models to identify false information in social media by using two recently released multilingual false information detection datasets. We show that multilingual models perform on par with the monolingual models and sometimes even better than the monolingual models to detect false information in social media making them more useful in real-world scenarios.
Introduction: Maternal body mass index (BMI) has an impact on maternal and fetal
pregnancy outcome.
Aim : To investigate the effect of pre-pregnancy BMI on adverse maternal and neonatal
outcomes in a sample of Syrian pregnant women attending Tishreen University Hospital .
In the growth of today's world and advanced technology, social media networks play a significant role in impacting human lives. Censorship is the overthrowing of speech, public transmission, or other details that play a vast role in social media. The
content may be considered harmful, sensitive, or inconvenient. Authorities like institutes, governments, and other organizations conduct Censorship. This paper has implemented a model that helps classify censored and uncensored tweets as a binary classification. The paper describes submission to the Censorship shared task of the NLP4IF 2021 workshop. We used various transformer-based pre-trained models, and XLNet outputs a better accuracy among all. We fine-tuned the model for better performance and achieved a reasonable accuracy, and calculated other performance metrics.
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