The 6th Social Media Mining for Health (#SMM4H) Workshop & Shared Task 2021 will be held online on June 10th, co-located at the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021). SMM4H 2021 continues to serve as a venue for bringing together data mining researchers interested in building solutions for challenges involved in utilizing social media data for health informatics. This year, we have oral presentations from accepted workshop papers and top performing systems in the shared tasks.

To participate, register at https://2021.naacl.org/registration/ Registration allows access to the full conference.

Program (All times in EST)

09:00-09:15	Opening Remarks and Introduction Graciela Gonzalez-Hernandez
09:15-10:15	Oral Presentations Q&A Session 1 (15 minutes each)
	Statistically Evaluating Social Media Sentiment Trends towards COVID-19 Non-Pharmaceutical Interventions with Event Studies Jingcheng Niu, Erin Rees, Victoria Ng and Gerald Penn
	View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data Payam Karisani, Jinho D. Choi and Li Xiong
	Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021 Arjun Magge et. al. with Graciela Gonzalez-Hernandez
	The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora Antonio Miranda-Escalada et. al. with Martin Krallinger
10:15–10:30	Break
10:30–11:10	Invited Talk : How Online Data has Informed the Fight Against COVID-19 by Mark Dredze
11:10–11:25	Break
11:30–12:30	Oral Presentations Q&A Session 2 (15 minutes each)
	BERT based Transformers lead the way in Extraction of Health Information from Social Media Sidharth Ramesh et. al. with Ujjwal Verma
	KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT based Models for Adverse Drug Effects Andrey Sakhovskiy, Zulfat Miftahutdinov and Elena Tutubalina
	Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets George-Andrei Dima, Dumitru-Clementin Cercel and Mihai Dascalu
	Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications Yuting Guo, Yao Ge, Mohammed Ali Al-Garadi and Abeed Sarker
12:30–13:15	Poster Session
13:15–13:30	Break
13:30–14:45	Oral Presentations Q&A Session 3 (15 minutes each)
	BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter Alham Fikri Aji, Made Nindyatama Nityasya, et. al. with Tirana Fatyanosa
	UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts Alberto Valdes, Jesus Lopez and Manuel Montes
	System description for ProfNER – SMM4H: Optimized finetuning of a pretrained transformer and word vectors David Carreto Fidalgo, Daniel Vila-Suero, Francisco Aranda Montes and Ignacio Talavera Cepeda
	Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media Sergio Santamaría Carrasco and Roberto Cuervo Rosillo
	Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks Tong Zhou, Baoli Zhang et. al. with Shengping Liu
14:45–15:00	Conclusion and Closing Remarks Graciela Gonzalez-Hernandez

Accepted Posters

Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers Man-Chen Hung

Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media Vasile Pais

Text Augmentation Techniques in Drug Adverse Effect Detection Task Pavel Blinov

UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts Alberto Valdes

BERT based Transformers lead the way in Extraction of Health Information from Social Media Sidharth Ramesh

Classification of COVID19 tweets using Machine Learning Approaches Anupam Mondal

BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter Made Nindyatama Nityasya

Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications Yuting Guo

Statistically Evaluating Social Media Sentiment Trends towards COVID-19 Non-Pharmaceutical Interventions with Event Studies Jingcheng Niu

BERT based Adverse Drug Effect Tweet Classification Pranjal Gupta

Phoenix@SMM4H Task-8: Adversities Make Ordinary Models Do Extraordinary Things Susmita Mazumdar

NLP@NISER: Classification of COVID19 tweets containing symptoms Deepak Kumar

Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media Sergio Santamaria Carrasco

Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification Pedro Ruas

Transformer Models for Classification on Health-Related Imbalanced Twitter Datasets Varad Pimpalkhute

UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation Frances Laureano De Leon

Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021 Usama Yaseen

The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora Antonio Miranda-Escalada

KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual & Cross-modal BERT-based Models for ADEs Andrey Sakhovskiy

Identifying professions & occupations in Health-related Social Media using Natural Language Processing José Alberto Mesa Murgado

System description for ProfNER – SMMH: Optimized fine tuning of a pretrained transformer and word vectors David Fidalgo

OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models Ying Luo

Contact Information

Arjun Magge (Arjun.Magge@pennmedicine.upenn.edu)