<div dir="ltr">=============================<br>First call for <b>papers</b>, submission deadline is August 15, 2022<br>First call for <b>shared task</b> participation, evaluation period starts July 11, 2022<br>=============================<br>*Apologies if you received multiple copies of this CFP*<br><br><b>Location:</b> Gyeongju, Republic of Korea<br><b>Workshop Date:</b> October 16-17, 2022<br><b>Important links:</b><br>Workshop and Shared task: <a href="https://healthlanguageprocessing.org/smm4h-2022/" target="_blank" rel="nofollow">https://healthlanguageprocessing.org/smm4h-2022/</a><br>Submission link: TBA<br><br>The workshop will include two components — a standard workshop and a shared task<br><br><b>Workshop</b><br>The
Social Media Mining for Health Applications (#SMM4H) workshop serves as
a venue for bringing together researchers interested in automatic
methods for the collection, extraction, representation, analysis, and
validation of social media data (e.g., Twitter, Reddit, Facebook) for
health informatics. The 7th #SMM4H Workshop, co-located at COLING 2022 (<a href="https://coling2022.org/index" target="_blank" rel="nofollow">https://coling2022.org/index</a>),
invites 4-page paper (unlimited references in standard COLING format)
submissions on original, unpublished research in all aspects at the
intersection of social media mining and health. Topics of interest
include, but are not limited to:<br><ul><li> Methods for the automatic detection and extraction of health-related concept mentions in social media</li></ul><ul><li> Mapping of health-related mentions in social media to standardized vocabularies</li></ul><ul><li> Deriving health-related trends from social media</li></ul><ul><li> Information retrieval methods for obtaining relevant social media data</li></ul><ul><li> Geographic or demographic data inference from social media discourse</li></ul><ul><li> Virus spread monitoring using social media</li></ul><ul><li> Mining health-related discussions in social media</li></ul><ul><li> Drug abuse and alcoholism incidence monitoring through social media</li></ul><ul><li> Disease incidence studies using social media</li></ul><ul><li> Sentinel event detection using social media</li></ul><ul><li> Semantic methods in social media analysis</li></ul><ul><li> Classifying health-related messages in social media</li></ul><ul><li> Automatic analysis of social media messages for disease surveillance and patient education</li></ul><ul><li> Methods for validation of social media-derived hypotheses and datasets </li></ul><br><b>Shared task</b><br>The
workshop organizers this year are hosting 10 shared tasks i.e. NLP
challenges as part of the workshop. Participating teams will be provided
with a set of annotated posts for developing systems, followed by a
three-day window during which they will run their systems on unlabeled
test data and upload it to Codalab for evaluation. For additional
details about the tasks and information about registration, data access,
paper submissions, and presentations, go to <a href="https://healthlanguageprocessing.org/smm4h-2022/" target="_blank" rel="nofollow">https://healthlanguageprocessing.org/smm4h-2022/</a><br><ul><li>Task 1 – Classification, detection, and normalization of Adverse Events (AE) mentions in tweets (in English)</li></ul><ul><li>Task 2 – Classification of stance and premise in tweets about health mandates related to COVID-19 (in English)</li></ul><ul><li>Task 3 – Classification of changes in medication treatments in tweets and WebMD reviews (in English)</li></ul><ul><li>Task 4 – Classification of tweets self-reporting exact age (in English)</li></ul><ul><li>Task 5 – Classification of tweets containing self-reported COVID-19 symptoms (in Spanish)</li></ul><ul><li>Task 6 – Classification of tweets which indicate self-reported COVID-19 vaccination status (in English)</li></ul><ul><li>Task 7 – Classification of self-reported intimate partner violence on Twitter (in English)</li></ul><ul><li>Task 8 – Classification of self-reported chronic stress on Twitter (in English)</li></ul><ul><li>Task 9 – Classification of Reddit posts self-reporting exact age (in English)</li></ul><ul><li>Task 10 – Detection of disease mentions in tweets – SocialDisNER (in Spanish)</li></ul><b>Organizing Committee</b><br> Graciela Gonzalez-Hernandez, University of Pennsylvania, USA<br> Davy Weissenbacher, University of Pennsylvania, USA <br> Arjun Magge, University of Pennsylvania, USA<br> Ari Z. Klein, University of Pennsylvania, USA<br> Ivan Flores, University of Pennsylvania, USA<br> Karen O’Connor, University of Pennsylvania, USA<br> Raul Rodriguez-Esteban, Roche Pharmaceuticals, Switzerland<br> Lucia Schmidt, Roche Pharmaceuticals, Switzerland<br> Juan M. Banda, Georgia State University, USA<br> Abeed Sarker, Emory University, USA<br> Yuting Guo, Emory University, USA <br> Yao Ge, Emory University, USA<br> Elena Tutubalina, Insilico Medicine, Hong Kong<br> Luis Gasco, Barcelona Supercomputing Center, Spain<br> Darryl Estrada, Barcelona Supercomputing Center, Spain<br> Martin Krallinger, Barcelona Supercomputing Center, Spain <br><br><b>Contact</b><br>All questions should be emailed to Davy Weissenbacher (<a href="mailto:dweissen@pennmedicine.upenn.edu">dweissen@pennmedicine.upenn.edu</a>)</div>