Social Media Mining for Health Research and Applications Workshop and Shared Tasks 2020: all accepted papers
- Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules ↗Sedigh Khademi, Pari Delirhaghighi, Frada Burstein
- Annotating Patient Information Needs in Online Diabetes Forums ↗Julia Romberg, Jan Dyczmons, Sandra Olivia Borgmann et al.
- Approaching SMM4H 2020 with Ensembles of BERT Flavours ↗George-Andrei Dima, Andrei-Marius Avram, Dumitru-Clementin Cercel
- Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines ↗Sougata Saha, Souvik Das, Prashi Khurana et al.
- Automatic Detecting for Health-related Twitter Data with BioBERT ↗Yang Bai, Xiaobing Zhou
- BERT Implementation for Detecting Adverse Drug Effects Mentions in Russian ↗Andrey Gusev, Anna Kuznetsova, Anna Polyanskaya et al.
- CLaC at SMM4H 2020: Birth Defect Mention Detection ↗Parsa Bagherzadeh, Sabine Bergler
- COVID-19 Twitter Monitor: Aggregating and Visualizing COVID-19 Related Trends in Social Media ↗Joseph Cornelius, Tilia Ellendorff, Lenz Furrer et al.
- Conversation-Aware Filtering of Online Patient Forum Messages ↗Anne Dirkson, Suzan Verberne, Wessel Kraaij
- Ensemble BERT for Classifying Medication-mentioning Tweets ↗Huong Dang, Kahyun Lee, Sam Henry et al.
- Exploring Online Depression Forums via Text Mining: A Comparison of Reddit and a Curated Online Forum ↗Luis Moßburger, Felix Wende, Kay Brinkmann et al.
- FBK@SMM4H2020: RoBERTa for Detecting Medications on Twitter ↗Silvia Casola, Alberto Lavelli
- HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets ↗Xiaoyu Zhao, Ying Xiong, Buzhou Tang
- How Far Can We Go with Just Out-of-the-box BERT Models? ↗Lucie Gattepaille
- ISLab System for SMM4H Shared Task 2020 ↗Chen-Kai Wang, Hong-Jie Dai, You-Chen Zhang et al.
- Identification of Medication Tweets Using Domain-specific Pre-trained Language Models ↗Yandrapati Prakash Babu, Rajagopal Eswari
- Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at \#SMM4H 2020 ↗V.G.Vinod Vydiswaran, Deahan Yu, Xinyan Zhao et al.
- KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions ↗Zulfat Miftahutdinov, Andrey Sakhovskiy, Elena Tutubalina
- LITL at SMM4H: An Old-school Feature-based Classifier for Identifying Adverse Effects in Tweets ↗Ludovic Tanguy, Lydia-Mai Ho-Dac, Cécile Fabre et al.
- Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees ↗Lung-Hao Lee, Po-Han Chen, Hao-Chuan Kao et al.
- NLP@VCU: Identifying Adverse Effects in English Tweets for Unbalanced DataDarshini Mahendran, Cora Lewis, Bridget McInnesour summary
- Overview of the Fifth Social Media Mining for Health Applications (\#SMM4H) Shared Tasks at COLING 2020 ↗Ari Klein, Ilseyar Alimova, Ivan Flores et al.
- SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing ↗Isabel Metzger, Emir Y. Haskovic, Allison Black et al.
- Sentence Classification with Imbalanced Data for Health Applications ↗Farhana Ferdousi Liza
- Sentence Contextual Encoder with BERT and BiLSTM for Automatic Classification with Imbalanced Medication Tweets ↗Olanrewaju Tahir Aduragba, Jialin Yu, Gautham Senthilnathan et al.
- Towards Preemptive Detection of Depression and Anxiety in Twitter ↗David Owen, Jose Camacho-Collados, Luis Espinosa Anke
- Transformer Models for Drug Adverse Effects Detection from Tweets ↗Pavel Blinov, Manvel Avetisian
- Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa ↗Katikapalli Subramanyam Kalyan, Sivanesan Sangeetha
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