SocialDisNER’s overview paper:

Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell, Salvador Lima-López, Antonio Miranda-Escalada, and Martin Krallinger. 2022. The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 182–189, Gyeongju, Republic of Korea. Association for Computational Linguistics.

URL: https://aclanthology.org/2022.smm4h-1.48/

SMM4H 2022 overview paper:

Davy Weissenbacher, Juan Banda, Vera Davydova, Darryl Estrada Zavala, Luis Gasco Sánchez, Yao Ge, Yuting Guo, Ari Klein, Martin Krallinger, Mathias Leddin, Arjun Magge, Raul Rodriguez-Esteban, Abeed Sarker, Lucia Schmidt, Elena Tutubalina, and Graciela Gonzalez-Hernandez. 2022. Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 221–241, Gyeongju, Republic of Korea. Association for Computational Linguistics.

URL: https://aclanthology.org/2022.smm4h-1.54/

Participants papers:

• Jia Fu, Sirui Li, Hui Ming Yuan, Zhucong Li, Zhen Gan, Yubo Chen, Kang Liu, Jun Zhao, and Shengping Liu. 2022. CASIA@SMM4H’22: A Uniform Health Information Mining System for Multilingual Social Media Texts. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 143–147, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Antonio Jimeno Yepes and Karin Verspoor. 2022. READ-BioMed@SocialDisNER: Adaptation of an Annotation System to Spanish Tweets. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 48–51, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Harsh Verma, Parsa Bagherzadeh, and Sabine Bergler. 2022. CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 55–57, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Matias Rojas, Jose Barros, Kinan Martin, Mauricio Araneda-Hernandez, and Jocelyn Dunstan. 2022. PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 52–54, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Antonio Tamayo, Alexander Gelbukh, and Diego Burgos. 2022. NLP-CIC-WFU at SocialDisNER: Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 19–22, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Miguel Ortega-Martín, Alfonso Ardoiz, Oscar Garcia, Jorge Álvarez, and Adrián Alonso. 2022. dezzai@SMM4H’22: Tasks 5 & 10 – Hybrid models everywhere. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 7–10, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Andrei-Marius Avram, Vasile Pais, and Maria Mitrofan. 2022. RACAI@SMM4H’22: Tweets Disease Mention Detection Using a Neural Lateral Inhibitory Mechanism. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 1–3, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Antoine Lain, Wonjin Yoon, Hyunjae Kim, Jaewoo Kang, and Ian Simpson. 2022. KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 78–80, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Mariia Chizhikova, Pilar López-Úbeda, Manuel C. Díaz-Galiano, L. Alfonso Ureña-López, and M. Teresa Martín-Valdivia. 2022. SINAI@SMM4H’22: Transformers for biomedical social media text mining in Spanish. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 27–30, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Rosa Montañés-Salas, Irene López-Bosque, Luis García-Garcés, and Rafael del-Hoyo-Alonso. 2022. ITAINNOVA at SocialDisNER: A Transformers cocktail for disease identification in social media in Spanish. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 71–74, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Kendrick Cetina and Nuria García-Santa. 2022. FRE at SocialDisNER: Joint Learning of Language Models for Named Entity Recognition. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 68–70, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Aman Sinha, Cristina Garcia Holgado, Marianne Clausel, and Matthieu Constant. 2022. IAI @ SocialDisNER : Catch me if you can! Capturing complex disease mentions in tweets. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 85–89, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Akbar Karimi and Lucie Flek. 2022. CAISA@SMM4H’22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 168–170, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Beatrice Portelli, Simone Scaboro, Emmanuele Chersoni, Enrico Santus, and Giuseppe Serra. 2022. AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 130–134, Gyeongju, Republic of Korea. Association for Computational Linguistics.
• Veysel Kocaman, Cabir Celik, Damla Gurbaz, Gursev Pirge, Bunyamin Polat, Halil Saglamlar, Meryem Vildan Sarikaya, Gokhan Turer, and David Talby. 2022. John_Snow_Labs@SMM4H’22: Social Media Mining for Health (#SMM4H) with Spark NLP. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 44–47, Gyeongju, Republic of Korea. Association for Computational Linguistics.

SocialDISNER will be part of the Social Media Mining for Health 2022 (#SMM4H) workshop at the COLING 2022 (the 29th International Conference On Computational Linguistics), that takes place in October at Gyeongju (Republic of Korea).

COLING is one of the leading conferences on natural language processing and computational linguistics and attracts participants from both top research centers and emerging countries.

SocialDISNER participants are required to write a short-paper describing the system(s) they ran on the test data. Some sample description systems can be found on pages 89-136 of the #SMM4H 2019 proceedings. Accepted system descriptions will be included in the #SMM4H 2022 proceedings.

We encourage at least one author of each accepted system description to register for the #SMM4H 2022 Workshop, co-located at COLING, and present their system as a poster. Selected participants, as determined by the program committee, will be invited to extend their system description to up to four pages, plus unlimited references, and present their system orally.

Email Martin Krallinger to Krallinger.Martin@gmail.com , Luis Gasco to luis.gasco@bsc.es , and Darryl Estrada to darryl.estrada@bsc.es

1. Q: What is the goal of the shared task?
   The goal is to predict the named entities of the tweets in the test and background sets.
   
   
2. Q: How do I register?
   Here: Google Form
   
   
3. Q: How do I submit the results?
   In CodaLab.
   
   
4. Q: Can I use additional training data to improve model performance?
   Yes, participants may use any additional training data they have available, as long as they describe it in the system description.
   
   
   
5. Q: Is there a Google Group for the SocialDisNER task?
   Yes: Google Group

TBD

To participate in the task, please be sure to complete the following steps:

1. Register on Google Form. Please, choose a team name you remember since we will use it throughout the whole competition and select the Task 10 option:

   

2. Login/Register in our CodaLab task in order to upload your predictions:

   

Important note: Student registrants are required to provide the name and email address of a faculty team member who has agreed to serve as their advisor/mentor for developing their system and writing their system description (see below). By registering for a task, participants agree to run their system on the test data and upload at least one set of predictions to CodaLab. Teams may upload up to three sets of predictions per task. By receiving access to the annotated tweets, participants agree to Twitter’s Terms of Service and may not redistribute any portion of the data.

TBA