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title: "Social Media Mining for Health Research and Applications"
description: "All 54 papers of Social Media Mining for Health Research and Applications Workshop and Shared Tasks 2022, the most cited first; those with…"
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# Social Media Mining for Health Research and Applications Workshop and Shared Tasks 2022: all accepted papers

54 papers · the most cited first · [← Social Media Mining for Health Research and Applications Workshop and Shared Tasks 2022](https://md.research.elamur.ai/free/en/venues/smm4h/2022/)

1. **[AILAB-Udine@SMM4H'22: Limits of Transformers and BERT Ensembles ↗](https://aclanthology.org/2022.smm4h-1.36/)**Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni et al.
2. **[AIR-JPMC@SMM4H'22: BERT + Ensembling = Too Cool: Using Multiple BERT Models Together for Various COVID-19 Tweet Identification Tasks ↗](https://aclanthology.org/2022.smm4h-1.44/)**Leung Wai Liu, Akshat Gupta, Saheed Obitayo et al.
3. **[AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models ↗](https://aclanthology.org/2022.smm4h-1.37/)**Alec Louis Candidato, Akshat Gupta, Xiaomo Liu et al.
4. **[AIR-JPMC@SMM4H'22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling ↗](https://aclanthology.org/2022.smm4h-1.43/)**Adrian Garcia Hernandez, Leung Wai Liu, Akshat Gupta et al.
5. **[ARGUABLY@SMM4H'22: Classification of Health Related Tweets using Ensemble, Zero-Shot and Fine-Tuned Language Model ↗](https://aclanthology.org/2022.smm4h-1.38/)**Prabsimran Kaur, Guneet Kohli, Jatin Bedi
6. **[BOUN-TABI@SMM4H'22: Text-to-Text Adverse Drug Event Extraction with Data Balancing and Prompting ↗](https://aclanthology.org/2022.smm4h-1.9/)**Gökçe Uludoğan, Zeynep Yirmibeşoğlu
7. **[BioInfo@UAVR@SMM4H'22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models ↗](https://aclanthology.org/2022.smm4h-1.19/)**Edgar Morais, José Luis Oliveira, Alina Trifan et al.
8. **[CAISA@SMM4H'22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods ↗](https://aclanthology.org/2022.smm4h-1.45/)**Akbar Karimi, Lucie Flek
9. **[CASIA@SMM4H'22: A Uniform Health Information Mining System for Multilingual Social Media Texts ↗](https://aclanthology.org/2022.smm4h-1.39/)**Jia Fu, Sirui Li, Hui Ming Yuan et al.
10. **[CHAAI@SMM4H'22: RoBERTa, GPT-2 and Sampling - An interesting concoction ↗](https://aclanthology.org/2022.smm4h-1.24/)**Christopher Palmer, Sedigheh Khademi Habibabadi, Muhammad Javed et al.
11. **[CIC NLP at SMM4H 2022: a BERT-based approach for classification of social media forum posts ↗](https://aclanthology.org/2022.smm4h-1.17/)**Atnafu Lambebo Tonja, Olumide Ebenezer Ojo, Mohammed Arif Khan et al.
12. **[CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets ↗](https://aclanthology.org/2022.smm4h-1.16/)**Harsh Verma, Parsa Bagherzadeh, Sabine Bergler
13. **[COVID-19-related Nepali Tweets Classification in a Low Resource Setting ↗](https://aclanthology.org/2022.smm4h-1.52/)**Rabin Adhikari, Safal Thapaliya, Nirajan Basnet et al.
14. **[CSECU-DSG@SMM4H'22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and WebMD Reviews ↗](https://aclanthology.org/2022.smm4h-1.33/)**Afrin Sultana, Nihad Karim Chowdhury, Abu Nowshed Chy
15. **[CompLx@SMM4H'22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets](https://md.research.elamur.ai/free/en/papers/xherija-2022-complx-smm4h-22-domain-pretrained/)**Orest Xherija, Hojoon Choi*our summary*
16. **[Edinburgh_UCL_Health@SMM4H'22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination ↗](https://aclanthology.org/2022.smm4h-1.40/)**Imane Guellil, Jinge Wu, Honghan Wu et al.
17. **[Enolp musk@SMM4H'22 : Leveraging Pre-trained Language Models for Stance And Premise Classification ↗](https://aclanthology.org/2022.smm4h-1.42/)**Millon Madhur Das, Archit Mangrulkar, Ishan Manchanda et al.
18. **[FRE at SocialDisNER: Joint Learning of Language Models for Named Entity Recognition ↗](https://aclanthology.org/2022.smm4h-1.20/)**Kendrick Cetina, Nuria García-Santa
19. **[Fraunhofer FKIE @ SMM4H 2022: System Description for Shared Tasks 2, 4 and 9 ↗](https://aclanthology.org/2022.smm4h-1.29/)**Daniel Claeser, Samantha Kent
20. **[Fraunhofer SIT@SMM4H'22: Learning to Predict Stances and Premises in Tweets related to COVID-19 Health Orders Using Generative Models ↗](https://aclanthology.org/2022.smm4h-1.31/)**Raphael Frick, Martin Steinebach
21. **[HaleLab_NITK@SMM4H'22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data ↗](https://aclanthology.org/2022.smm4h-1.27/)**Reshma Unnikrishnan, Sowmya Kamath S, Ananthanarayana V. S.
22. **[IAI @ SocialDisNER : Catch me if you can! Capturing complex disease mentions in tweets ↗](https://aclanthology.org/2022.smm4h-1.25/)**Aman Sinha, Cristina Garcia Holgado, Marianne Clausel et al.
23. **[ITAINNOVA at SocialDisNER: A Transformers cocktail for disease identification in social media in Spanish ↗](https://aclanthology.org/2022.smm4h-1.21/)**Rosa Montañés-Salas, Irene López-Bosque, Luis García-Garcés et al.
24. **[Innovators @ SMM4H'22: An Ensembles Approach for self-reporting of COVID-19 Vaccination Status Tweets ↗](https://aclanthology.org/2022.smm4h-1.34/)**Mohammad Zohair, Nidhir Bhavsar, Aakash Bhatnagar et al.
25. **[Innovators@SMM4H'22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets ↗](https://aclanthology.org/2022.smm4h-1.35/)**Vatsal Savaliya, Aakash Bhatnagar, Nidhir Bhavsar et al.
26. **[John_Snow_Labs@SMM4H'22: Social Media Mining for Health (\#SMM4H) with Spark NLP ↗](https://aclanthology.org/2022.smm4h-1.13/)**Veysel Kocaman, Cabir Celik, Damla Gurbaz et al.
27. **[KUL@SMM4H'22: Template Augmented Adaptive Pre-training for Tweet Classification ↗](https://aclanthology.org/2022.smm4h-1.41/)**Sumam Francis, Marie-Francine Moens
28. **[KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish ↗](https://aclanthology.org/2022.smm4h-1.23/)**Antoine Lain, Wonjin Yoon, Hyunjae Kim et al.
29. **[Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge ↗](https://aclanthology.org/2022.smm4h-1.51/)**Jia-Zhen Michelle Chan, Florian Kunneman, Roser Morante et al.
30. **[MANTIS at SMM4H'2022: Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress ↗](https://aclanthology.org/2022.smm4h-1.5/)**Sourabh Zanwar, Daniel Wiechmann, Yu Qiao et al.
31. **[MaNLP@SMM4H'22: BERT for Classification of Twitter Posts ↗](https://aclanthology.org/2022.smm4h-1.12/)**Keshav Kapur, Rajitha Harikrishnan, Sanjay Singh
32. **[NCUEE-NLP@SMM4H'22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models ↗](https://aclanthology.org/2022.smm4h-1.18/)**Tzu-Mi Lin, Chao-Yi Chen, Yu-Wen Tzeng et al.
33. **[NLP-CIC-WFU at SocialDisNER: Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation ↗](https://aclanthology.org/2022.smm4h-1.6/)**Antonio Tamayo, Alexander Gelbukh, Diego Burgos
34. **[OFU@SMM4H'22: Mining Advent Drug Events Using Pretrained Language Models ↗](https://aclanthology.org/2022.smm4h-1.46/)**Omar Adjali, Fréjus A. A. Laleye, Umang Aggarwal
35. **[Overview of the Seventh Social Media Mining for Health Applications (\#SMM4H) Shared Tasks at COLING 2022 ↗](https://aclanthology.org/2022.smm4h-1.54/)**Davy Weissenbacher, Juan Banda, Vera Davydova et al.
36. **[PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features ↗](https://aclanthology.org/2022.smm4h-1.15/)**Matias Rojas, Jose Barros, Kinan Martin et al.
37. **[PingAnTech at SMM4H task1: Multiple pre-trained model approaches for Adverse Drug Reactions ↗](https://aclanthology.org/2022.smm4h-1.2/)**Xi Liu, Han Zhou, Chang Su
38. **[RACAI@SMM4H'22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism ↗](https://aclanthology.org/2022.smm4h-1.1/)**Andrei-Marius Avram, Vasile Pais, Maria Mitrofan
39. **[READ-BioMed@SocialDisNER: Adaptation of an Annotation System to Spanish Tweets ↗](https://aclanthology.org/2022.smm4h-1.14/)**Antonio Jimeno Yepes, Karin Verspoor
40. **[Romanian micro-blogging named entity recognition including health-related entities ↗](https://aclanthology.org/2022.smm4h-1.49/)**Vasile Pais, Verginica Barbu Mititelu, Elena Irimia et al.
41. **[SINAI@SMM4H'22: Transformers for biomedical social media text mining in Spanish ↗](https://aclanthology.org/2022.smm4h-1.8/)**Mariia Chizhikova, Pilar López-Úbeda, Manuel C. Díaz-Galiano et al.
42. **[SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19 ↗](https://aclanthology.org/2022.smm4h-1.53/)**Vera Davydova, Elena Tutubalina
43. **[The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts ↗](https://aclanthology.org/2022.smm4h-1.50/)**Sourabh Zanwar, Daniel Wiechmann, Yu Qiao et al.
44. **[The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora ↗](https://aclanthology.org/2022.smm4h-1.48/)**Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell et al.
45. **[Transformer-based classification of premise in tweets related to COVID-19 ↗](https://aclanthology.org/2022.smm4h-1.30/)**Vadim Porvatov, Natalia Semenova
46. **[UB Health Miners@SMM4H'22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines. ↗](https://aclanthology.org/2022.smm4h-1.32/)**Roshan Khatri, Sougata Saha, Souvik Das et al.
47. **[UCCNLP@SMM4H'22:Label distribution aware long-tailed learning with post-hoc posterior calibration applied to text classification ↗](https://aclanthology.org/2022.smm4h-1.26/)**Paul Trust, Provia Kadusabe, Ahmed Zahran et al.
48. **[Yet@SMM4H'22: Improved BERT-based classification models with Rdrop and PolyLoss ↗](https://aclanthology.org/2022.smm4h-1.28/)**Yan Zhuang, Yanru Zhang
49. **[Zhegu@SMM4H-2022: The Pre-training Tweet & Claim Matching Makes Your Prediction Better ↗](https://aclanthology.org/2022.smm4h-1.11/)**Pan He, Chen YuZe, Yanru Zhang
50. **[dezzai@SMM4H'22: Tasks 5 & 10 - Hybrid models everywhere ↗](https://aclanthology.org/2022.smm4h-1.3/)**Miguel Ortega-Martín, Alfonso Ardoiz, Oscar Garcia et al.
51. **[mattica@SMM4H'22: Leveraging sentiment for stance & premise joint learning ↗](https://aclanthology.org/2022.smm4h-1.22/)**Oscar Lithgow-Serrano, Joseph Cornelius, Fabio Rinaldi et al.
52. **[uestcc@SMM4H'22: RoBERTa based Adverse Drug Events Classification on Tweets ↗](https://aclanthology.org/2022.smm4h-1.10/)**Chunchen Wei, Ran Bi, Yanru Zhang
53. **[yiriyou@SMM4H'22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks ↗](https://aclanthology.org/2022.smm4h-1.7/)**Huabin Yang, Zhongjian Zhang, Yanru Zhang
54. **[zydhjh4593@SMM4H'22: A Generic Pre-trained BERT-based Framework for Social Media Health Text Classification ↗](https://aclanthology.org/2022.smm4h-1.4/)**Chenghao Huang, Xiaolu Chen, Yuxi Chen et al.
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