Social Media Mining for Health 2022 (#SMM4H)


*** New: proceedings are available ***

  • Workshop
  • Shared Task 
  • Past events 
SMM4H logo

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, Facebook) for health informatics. The 7th #SMM4H Workshop, co-located at Coling 2022, invites the submission of papers on original, unpublished research in all aspects at the intersection of social media mining and health.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:

  • Methods for the automatic detection and extraction of health-related concept mentions in social media
  • Mapping of health-related mentions in social media to standardized vocabularies
  • Deriving health-related trends from social media
  • Information retrieval methods for obtaining relevant social media data
  • Geographic or demographic data inference from social media discourse
  • Virus spread monitoring using social media
  • Mining health-related discussions in social media
  • Drug abuse and alcoholism incidence monitoring through social media
  • Disease incidence studies using social media
  • Sentinel event detection using social media
  • Semantic methods in social media analysis
  • Classifying health-related messages in social media
  • Automatic analysis of social media messages for disease surveillance and patient education
  • Methods for validation of social media-derived hypotheses and datasets

Important dates (tentative)

Workshop papers due Aug. 17 – Extension: Aug. 20
Acceptance notification Sept. 1Sept. 7
Camera ready paper Sept. 12Sept. 15
Workshop date October 17, 2022

Keynote: Social media listening for pharmaceutical R&D

Traditionally, social media listening (SML) in the pharmaceutical setting has been limited to marketing and communication purposes and performed with manual, qualitative methods. Pharmaceutical companies, with the encouragement of regulatory agencies, have started utilizing social media listening to integrate the patient perspective in the clinical development process to ensure relevant treatments and outcomes. Additionally, there is a growing acknowledgement that quantitative methods for SML (QSML) can provide new and more rigorous analyses that enhance the value of social media data to enable a patient-centric approach to understanding disease burden and influence drug discovery decisions at all stages. During this talk, I will present some examples of QSML supporting pharmaceutical R&D.

Speaker: Raul Rodriguez-Esteban

Raul Rodriguez-Esteban is Senior Principal Scientist at Roche Pharmaceuticals in Basel, Switzerland, where he works on natural language processing, machine learning and real-world data applied to pharmaceutical R&D. Previously, he worked in pharmaceutical R&D at Boehringer Ingelheim and Pfizer. He completed his PhD in machine learning applied to text mining at the laboratory of Andrey Rzhetsky at Columbia University. He was a winner of the Bio-IT World Innovative Practices Award in 2020 and is editorial board member of the journal BMC Digital Health.

Paper Submission and Presentation Information

Paper submissions may consist of up to 4 pages, plus unlimited references, and must describe completed, original, and unpublished work. Papers may make small, focused contributions, but the work must be completed; we will not accept papers describing work-in-progress. We also will not accept papers that overlap significantly with papers that have been or will be published elsewhere, or are currently under consideration for other venues. All accepted papers are required to be presented orally or as a poster, as determined by the program committee, in order to be included in the workshop proceedings. At least one author of each accepted paper must register for #SMM4H 2022 to present.

All paper submissions must follow the Coling 2022 guidelines and be submitted as a PDF.

Submission link: softconf


Graciela Gonzalez-Hernandez, Cedars-Sinai Medical Center, USA

Davy Weissenbacher, Cedars-Sinai Medical Center, USA

Arjun Magge, University of Pennsylvania, USA

Ari Z. Klein, University of Pennsylvania, USA

Ivan Flores, Cedars-Sinai Medical Center, USA

Karen O’Connor, University of Pennsylvania, USA

Raul Rodriguez-Esteban, Roche Pharmaceuticals, Switzerland

Lucia Schmidt, Roche Pharmaceuticals, Switzerland

Juan M. Banda, Georgia State University, USA

Abeed Sarker, Emory University, USA

Yuting Guo, Emory University, USA

Yao Ge, Emory University, USA

Elena Tutubalina, Insilico Medicine, Hong Kong

Luis Gasco, Barcelona Supercomputing Center, Spain

Darryl Estrada, Barcelona Supercomputing Center, Spain

Martin Krallinger, Barcelona Supercomputing Center, Spain

Program Committee

Cecilia Arighi, University of Delaware, USA

Natalia Grabar, French National Center for Scientific Research, France

Thierry Hamon, Paris-Nord University, France

Antonio Jimeno Yepes, Royal Melbourne Institute of Technology, Australia

Jin-Dong Kim, Database Center for Life Science, Japan

Corrado Lanera, University of Padova, Italy

Robert Leaman, US National Library of Medicine, USA

Kirk Roberts, University of Texas Health Science Center at Houston, USA

Yutaka Sasaki, Toyota Technological Institute, Japan

Pierre Zweigenbaum, French National Center for Scientific Research, France

Contact information

Davy Weissenbacher (

Shared Task 

Call for Participation – Shared Task

The Social Media Mining for Health Applications (#SMM4H) Shared Task involves natural language processing (NLP) challenges of using social media data for health research, including informal, colloquial expressions and misspellings of clinical concepts, noise, data sparsity, ambiguity, and multilingual posts. For each of the eight tasks below, 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 the predictions of their systems to CodaLab. Information about registration, data access, paper submissions, and presentations can be found in the individual competition sections below.

Registration: here

(Note: after registration, we communicate and release the data through dedicated google groups, please, make sure to check your spam folders)

Submission link for the system descriptions: softconf

Timeline (tentative)

Sample data set release Feb. 15
Training and validation set release March 31
Validation set submission due Jul. 4
Test set release ~Jul. 11 (See each task for details)
Test set predictions due ~Jul. 15 (See each task for details)*
Test set evaluation scores release Jul. 25
System descriptions due Aug. 1
Acceptance notification Aug. 15~ Aug. 20
Camera ready system descriptions Sep. 1Sept. 7
* All deadlines are 11:59 PM UTC (3:59 PM PST), NO extension will be provided
Task 1 – Classification, detection and normalization of Adverse Events (AE) mentions in tweets (in English)

Adverse Drug Events (ADEs), also often known as Adverse Drug Reactions (ADRs), are negative side effects related to the drug. Mining ADEs from social media is one of the most studied topics in the area of Social Media Pharmacovigilance to understand how data from non-traditional sources can be mined for early detection. In this task, the submitted system must perform one or more of the following tasks:

(1) classify tweets reporting ADEs (Adverse Drug Events),

(2) detect ADE spans in the tweets, and

(3) map these colloquial mentions to their standard concept IDs in the MedDRA vocabulary.


We will provide participants with about 18,000 labeled tweets for training and about 10,000 tweets for testing. Participants will have the option to participate in one or more subtasks:


Task 1a – Classification : Given a tweet, participants of this subtask will be required to submit only the binary annotations ADE/noADE

Task 1b – Extraction : Given a tweet, participants of this subtask will be required to submit both the ADE classification labels (Subtask 1a) and spans of expressed ADE.

Task 1c – Normalization : Given a tweet, participants of this subtask will be required to submit ADE classification labels (Subtask 1a), ADE spans (Subtask 1b) and normalization labels. This task involves development of multiple components and presents multiple challenges such as class imbalance and out-of-vocabulary labels. Hence, it will require methods going beyond simple applications of deep learning approaches to be successfully addressed.

Contact: Arjun Magge, University of Pennsylvania, USA (


Task 2 – Classification of stance and premise in tweets about health mandates related to COVID-19 (in English)

Users are actively sharing their views on various issues on social networks. Nowadays, these issues are often related to the COVID-19 pandemic. For example, users express their attitude towards a quarantine and wearing masks in public places. Some statements are reasoned by arguments, other statements are just emotional claims. Automated approaches for detecting people’s stances towards health orders related to COVID-19, using Twitter posts, can help to estimate the level of cooperation with the mandates. In this task we focus on argument mining (or argumentation mining) for extracting arguments from COVID-related tweets. According to argumentation theory, an argument must include a claim containing a stance towards some topic or object, and at least one premise/argument (“favor” or “against”) of this stance.

Participants will be provided with labeled training set containing texts from Twitter about three health mandates related to COVID-19 pandemic:

  • Face Masks
  • Stay At Home Orders
  • School closures

Participants have an option to take part in one or two subtasks. All participants will be invited to submit papers and present their results at the SMM4H 2022 workshop (see COLING’22 for more information on dates)


We will provide participants with manually labeled tweets for training, validation and testing. The train set for stance detection subtask is based on a COVID-19 stance detection dataset (Glandt et al., 2021).

  • Train: 3669 tweets
  • Validation: 600 tweets
  • Test: 2000 tweets


Elena Tutubalina, Insilico Medicine, Hong Kong (

Vera Davydova (


Subtask 2a. Stance Detection

The designed system for this subtask should be able to determine the point of view (stance) of the text’s author in relation to the given claim (e.g., wearing a face mask). The tweets in the training dataset are manually annotated for stance according to three categories: in-favor, against, and neither. Given a tweet, participants of this subtask will be required to submit three classes annotations:

  • FAVOR – positive stance
  • AGAINST – negative stance
  • NEITHER – neutral/unclear/irrelevant stance

Subtask 2b. Premise Classification

The second subtask is to predict whether at least one premise/argument is mentioned in the text. A given tweet is considered as having a premise if it contains a statement that can be used as an argument in a discussion. For instance, the annotator could use it to convince an opponent about the given claim.

Given a tweet, participants of this subtask will be required to submit only the binary annotations:

  • 1 – tweet contains a premise (argument)
  • 0 – tweet doesn’t contain a premise (argument)

Evaluation Metrics

The main performance metric in each of the two subtasks are F1𝑠𝑡𝑎𝑛𝑐𝑒 and F1𝑝𝑟𝑒𝑚𝑖𝑠𝑒 scores respectively,

which are calculated according to the following formula:

𝐶 = {“face masks”,”stay at home orders”,”school closures”},
𝑛 is the size of 𝐶,
𝐹1𝑟𝑒𝑙 score is macro 𝐹1-score averaged over first two relevance classes (the class “NEITHER” is excluded).

Examples of annotations

Tweet Claim Stance Premise
The fact that anti-masking is a thing is a completely terrifying insight into the nature of some beings who look, walk and breathe just like us. face masks FAVOR 0
Masks help prevent the spread of the disease. Please, #WEARAMASK face masks FAVOR 1
Woah, so you’re telling me that my five kids have to go to school while this coronavirus outbreak is happening. It was fair enough with all the strikes. school closures FAVOR 0
@GodFamilyJesus Masks Kill Our Immune Systems. They cover the mouth which needs to be seen when negotiating and conversing. Not seeing facial expressions leads to depression and misunderstanding. Fungal Respiratory Infections, Heat Stroke. Slave Muzzle, Panic Attacks Fear Mongering. face masks AGAINST 1
We are now experiencing a surge in the number of infected health care workers, with two deaths already. Prior to Covid19, we were experiencing a shortage and this is worsening with them in quarantine. You can help us by staying safe and staying home. stay at home orders FAVOR 1
0.02% chance of dying of #Covid and @GovInslee keeps our state in an “indefinite” lockdown. I’ll take those odd, thanks. stay at home orders AGAINST 1
@gryking I was just going to say. Repubs this week tried to blame protests for Covid increases, but there are still protests in NYC, and other events, and our numbers are still low despite it all. The difference …MASKS. face masks FAVOR 1
I see that @BBCOne are still showing the people on their tandem bikes before programmes. Don’t you lot not know that there is a lockdown and no one can go out right now? #coronavirus stay at home orders NEITHER 0
Close the damn schools until there is a vaccine. school closures FAVOR 1
Task 3 – Classification of changes in medication treatments in tweets and WebMD reviews (in English)

The designed binary classifier should detect tweets where Twitter users self-declare changing their medication treatments, regardless of being advised by a health care professional to do so. Such changes are, for example, not filling a prescription, stopping a treatment, changing a dosage, forgetting to take the drugs, etc. This task is the first step toward detecting patients non-adherent to their treatments and their reasons on Twitter. The data consists of two corpora: a set of tweets and a set of drug reviews from Negative and positive reviews are naturally balanced whereas positive and negative tweets are naturally imbalanced. Each set is split into a training, a validation, and a test subset. The participants will be given the training and validation subsets for both corpora and evaluated on both test sets independently. Participants are expected to submit their predictions for both test sets. This year, we will add in the test sets additional reviews and tweets as decoys to avoid manual corrections of the predicted labels. Evaluation script, annotation guidelines, and baseline code will be provided to registered participants.

  • Training data: 5,898 Tweets / 10,378 Reviews
  • Validation data: 1,572 Tweets / 1,297 Reviews
  • Test data: 2,360 Tweets / 1,297 Reviews
  • Evaluation metric: F1-score for the change class

Contact: Davy Weissenbacher, Cedars-Sinai, USA (


Subtask 3a. Tweet Classification

Submission format: Please use the format below for submission. Submissions should contain two columns tweet_id and label separated by tabspaces. All other columns will be ignored. Predictions for each task should be contained in a single .tsv (tab separated values) file. This file (and only this file) should be compressed into a .zip file. Please upload this zip file as submission.

tweet_id label
123 0
435 1
276 0
167 0

Subtask 3b. WebMD Classification

Submission format: Please use the format below for submission. Submissions should contain two columns SOURCE_FILE and label separated by tabspaces. All other columns will be ignored. Predictions for each task should be contained in a single .tsv (tab separated values) file. This file (and only this file) should be compressed into a .zip file. Please upload this zip file as submission.

reviews_parsed/119_049.txt 1
reviews_parsed/219_879.txt 0
reviews_parsed/123_839.txt 0
reviews_parsed/179_022.txt 1
Task 4 – Classification of tweets self-reporting exact age (in English)

Advancing the utility of social media data for research applications requires methods for automatically detecting demographic information about social media study populations, including users’ age. Automatically identifying the exact age of social media users, rather than their age groups, would enable the large-scale use of social media data for applications that do not align with the predefined age groupings of extant models, including health applications such as identifying specific age-related risk factors for observational studies, or selecting age-based study populations. As a first step, this binary classification task involves automatically distinguishing tweets that self-report the user’s exact age from those that do not. A benchmark classifier, based on a RoBERTa-Large pretrained model, achieved an F1-score of 0.914 for the “positive” class (i.e., tweets that self-report the user’s exact age) in the validation data.

  • Training data: 8,800 tweets
  • Validation data: 2,200 tweets
  • Test data: 10,000 tweets
  • Evaluation metric: F1-score for the “positive” class (i.e., tweets that self-report the user’s exact age)

Table 1 provides sample training data, which include the Tweet ID, the text of the Tweet Object, and the annotated binary class. Tweets were annotated as “1” if the user’s exact age could be determined, from the tweet, at the time the tweet was posted. In the first tweet, the user’s exact age is explicitly stated. Although the second tweet does not explicitly state the user’s age, it can be inferred from the fact that the user reports turning 20 tomorrow. The third tweet does not specify when the user will be 21, but it was annotated as “1” under the assumption that the tweet is referring to the user’s next birthday. The fourth tweet, however, was annotated as “0” because it is ambiguous about whether the user was 21 when the tweet was posted, or whether the user is referring to a future age. The fifth tweet was also annotated as “0” because it is ambiguous whether the user was 18 when the tweet was posted, or whether the user is referring to age further in the past. The sixth tweet was annotated as “0” because it does not refer to the age of the user, but rather the user’s brother.

Contact: Ari Klein, University of Pennsylvania, USA (


Submission format: System predictions should be submitted through CodaLab. Submissions should be formatted as a ZIP file containing a TSV file with only two columns: the tweet_id column first and the label column second. The TSV file should not be in a folder in the ZIP file, and the ZIP file should not contain any files or folders other than the TSV file.

Table 1.

Tweet ID Tweet text Class
759873324511924224 It’s my 21st birthday today. But who cares….. ITS FINALLY AUGUST!!!!!!!! That’s what really matters 😭😭😍😍😍💖💖💖💖💖 1
861628001485815809 It’s crazy, tomorrow I’ll be 20. I’m getting so OLD. 🤦🏽‍♀️ 1
802422959818145793 can’t believe im going to be 21 …. i actually want to be a teenager again with no responsibilities 😒 1
836614466846535680 I graduate in May only focusing on me and my child.. watch me at 21 😘 0
850592132356296705 Had just turned 18 then found out I was pregnant 2 weeks later 0
693155152279109632 Yesterday was my little bros 14th bday & I also found this sweet pic of us at my baby shower! Hes growing up on me 😢 0
Task 5 – Classification of tweets containing self-reported COVID-19 symptoms (in Spanish)

The purpose of this task is to bridge the gap in NLP and social media for COVID-19 research performed in languages other than English. While there has been an increased amount of non-English datasets and tasks using social media, proposed in the last couple of years, there is still a need for different applications on pressing topics. This shared task is similar to the SMM4H 2021 shared task #6, which involves identifying personal mentions of COVID-19 symptoms in Spanish language tweets. Note that the annotated set of tweets for this task is a brand new set of curated Spanish-native language tweets and not a translation of the previous English-only data. The proposed task is a three-way classification problem, requiring participants to distinguish personal symptom mentions from other mentions such as symptoms reported by others and references to news articles or other sources. The target classes are:

  1. self-reports,
  2. non-personal reports,
  3. literature/news mentions

The proposed training dataset consists of 1,654 tweets labeled as self-reports, 2,413 tweets labeled as non-personal reports, and 5,985 labeled as literature/news mentions. The systems submitted for this task will be evaluated on precision, recall and F1-score.

  • Training data: 10,052 tweets
  • Validation data: 3,578 tweets
  • Test data: 6,851 tweets
  • Evaluation Metric: precision, recall and F1-score 

Contact: Juan Banda, Georgia State University, USA (


Submission format

Submissions should contain two columns tweet_id and label separated by tab spaces.The labels required are: self-report, non-personal report, literature-news mentions. Any other different label will be ignored. All other columns will be ignored. Predictions for each task should be contained in a single .tsv (tab separated values) file. This file (and only this file) should be compressed into a .zip file. Please upload this zip file as a submission. 

Task 6 – Classification of tweets which indicate self-reported COVID-19 vaccination status (in English)

With the widespread rollout of COVID-19 vaccines, vaccine surveillance became a very pressing research issue. While some vaccinated people report adverse events via their healthcare providers to systems like Vaccine Adverse Event Reporting System (VAERS), or are found documented in their electronic health record (EHR), a more robust and convenient method could be devised using self-reports from social media. In this task we provide an annotated dataset of Twitter users personally reporting vaccination status and users discussing vaccination status but not revealing their own. This task is tricky in the sense that users discuss vaccination status of others or from news reports in similar ways than they discuss their own at a higher rate (1 to 8 on average). The dataset presents as the positive class, unambiguous tweets of users clearly stating that they have been vaccinated. All other tweets are of users discussing vaccination status. This task involves the identification of self-reported COVID-19 vaccination status in English tweets. As a two-way classification task, the two classes in the provided training dataset are:

  1. vaccination confirmation,
  2. vaccine related chatter

The class imbalance in this dataset is roughly 1 to 8, meaning that we will provide 1,496 tweets of vaccination confirmation, and 12,197 of vaccine chatter tweets. The systems submitted for this task will be evaluated on precision, recall and F1-score.

  • Training data: 13,693 tweets
  • Validation data: 2,784 tweets
  • Test data: 5,923 tweets
  • Evaluation Metric: precision, recall and F1-score

Contact: Juan Banda, Georgia State University, USA (


Submission format

Submissions should contain two columns tweet_id and label separated by tab spaces.The labels required are: 0 for vaccination confirmation and 1 for vaccine related chatter. Any other different label will be ignored. All other columns will be ignored. Predictions for each task should be contained in a single .tsv (tab separated values) file. This file (and only this file) should be compressed into a .zip file. Please upload this zip file as a submission.

Task 7 – Classification of self-reported intimate partner violence on Twitter (in English)

Intimate partner violence (IPV), which refers to abuse or aggression that occurs in a romantic relationship, is a serious health problem that can have a lifelong impact on health and well-being. Recently, social media platforms have been increasingly used by IPV victims to share experiences and seek for help. To provide early intervention and timely support, an effective automatic self-reported IPV classifier is needed to detect the potential IPV victims on social media platforms. This task presents two challenges. First, the annotated data is significantly imbalanced where only around 11% of the tweets are identified as self-reported IPV. Second, the negative tweets include non-IPV domestic violence and non-self-reported IPV, which can hardly be distinguished from self-reported IPV by an automatic system. The data include annotated collections of posts on Twitter. They will be shared as .csv files. The training data is already prepared and will be available to the teams registering to participate. The testing data will be released when the evaluation phase starts.

  • Training data: 4,523 posts
  • Validation data: 534 posts
  • Testing data: 1,291 post
  • Evaluation metric: F1-score for the self-reported IPV class

Contact: Yuting Guo, Emory University, USA (


Data Examples:

tweet_id text label
12453 I didn’t get married because I wanted too.
I didn’t have much choice I  an abusive relationship. I was so scared to say no for what the repercussions would be. I didn’t want to be homeless. I loved him at the time but marriage is not something I dreamed about it aspired too.
13349 RT @Richkid_life: Domestic violence is a sensitive topic so I just stay away from it all together 0

Submission format

Submissions should contain tweet_id and label separated by tabspace in the same order as below.

tweet_id label
234 1
414 0
611 0
876 0
Task 8 – Classification of self-reported chronic stress on Twitter (in English)

Chronic stress is defined as the physiological or psychological response to a prolonged internal or external stressful event (i.e., a stressor), which can lead to poor mental health, including depression and anxiety, and can also take a toll on the body, resulting in the dysfunctions of cardiovascular, metabolic, endocrine, and immuno-inflammatory systems. Traditional methods of assessing stress, including interviews, questionnaires/surveys, etc., have some limitations in accurately measuring population-level stress. Thus, there is a critical need to develop innovative chronic stress assessment methods. Social media are potentially valuable resources for studying chronic stress and its characteristics, and the first step is to accurately detect the tweets that are self-disclosures of chronic stress. In this task, about 37% of the tweets are positive (self-disclosure of chronic stress, P) and 63% are negative (non-self-disclosure of chronic stress, N). Systems designed for this task need to automatically identify tweets in the self-disclosure category. Classifier evaluation will be based on the F1 score over the positive class.

  • Training data: 2,936 tweets
  • Validation data: 420 tweets
  • Testing data: 839 tweets
  • Evaluation metric: F1-score over the positive class

Contact: Yao Ge, Emory University, USA (


Data examples

text Tweet_id label
Depression and anxiety will probably end up killing me – I feel so stressed all the time and just feel awful. 1189514994452520000 1
@User Do you enjoy living constantly in this self-inflicted stress? 1206425604607870000 0

Submission Format

Submissions should contain tweet_id and label separated by tabspace in the same order as below.

Tweet_id label
1198338978165880000 0
1189199432115510000 0
1220488181935690000 1
1183824148260440000 1
Task 9 – Classification of Reddit posts self-reporting exact age (in English)

Because social media is used by patients in every aspect of their daily lives, its analysis presents a promising way to understand the patient’s perspective on their disease journey, their unmet medical needs and their disease burden. Social media listening (SML) can, therefore, potentially support the progress in our understanding of a disease and influence the development of new therapies.

The detection of demographic information on social media is essential to address the differences in demographic characteristics (age, gender, ethnicity, medical history) between patients on social media and patients in target clinical populations. In this task, we focus on the automatic classification of social media forum (Reddit) posts into posts that self-report the exact age of the social media user at the time of posting (annotated as “1”) from those that do not (annotated as “0”). The dataset is disease-specific and consists of posts collected via a series of keywords associated with dry eye disease.

  • Training data: 9000 posts
  • Validation data: 1000 posts
  • Test data: 2000 posts
  • Evaluation metric: F1-score for the positive class (i.e. posts annotated as “1”)

Contact: Ana Lucia Schmidt, Roche Pharmaceuticals, Switzerland (


The table below provides sample training data, which includes the Post ID, Post Text, and annotated binary label (Class). Posts were annotated as “1” if the user’s exact age could be determined, from the post, at the time the post was written. Posts 1 to 5, shown below, are annotated as “1” as they have the age explicitly stated in the text (1-3) or the age can be inferred from the information presented (4-5). Posts 2 and 3 are noteworthy examples of the forum’s convention to denote the age and gender of the users. Posts 4 and 5 exemplify two different cases in which the age can be inferred from an explicitly stated past age (40 years since the user was 7) or future age (one week until the user turns 40). Posts 6 to 13 are

annotated as “0” for a variety of reasons:

  • The posts lack enough information to infer the age of the users (6-7)
  • The age explicitly stated in the text refers to a third party and not the user writing the post (8-9)
  • The age given in the post is not exact (10-11)
  • The age reported refers to the past and thus there is no self-reported age at the time of the post was written (12)
  • There is no age self-reported in the post (13)
id Post Text Class
1 As I am young (25) I see no harm in adding a supplement to my diet which could potentially improve my eyesight. 1
2 I have a inferior cone (52diop) on my good eye which means it’s the bottom half of my eye. Am 25 M diagnosed at 13. Vision in my good eye is 1 line better than or known as 20/10 unaided. Concerned about the ghosting progressing considering maybe I need to get epi on? 1
3 I (27,f,US) just got diagnosed with Sjogren’s but they didn’t tell me anything about it. They said there’s nothing that needs to be done now but monitor every 6 months and take ibuprofen. Can anyone tell me more or anything about your experience with it? I don’t have dry eyes or mouth but I had a positive ANA test. 1
4 Hi all! I’ve been 4-eyed since I was 7, which, alas, was 40 years ago. This past year I noticed I’m no longer able to read small print with my glasses on. If I remove my glasses, I’m able to read it just fine. If I read books and read on my kindle, I always take my glasses off and read, so I don’t believe I need reading glasses. Also, I still need glasses for my laptop screen- it’s blurry without. My question is: should I get Varifocals or just take off my glasses when I read a book in bed (often) or examine small print (rare) in the shop? I really like my current, small-frame glasses, so if I can avoid large and expensive varifocals, that’s a plus. However, if getting Varifocals is healthier for my eyes, I will get them. Any input would be welcome! Thanks. 1
5 Glad to hear you’re doing better, and thanks for sharing some of the things to watch for! Both my parents had retinal tears in their early 50s. I turn 40 next week and have a prescription of -13.00 in both eyes, so can only assume I’m biding my time. I just had my annual eye exam with dilation yesterday; so far, so good. 1
6 I’ve been on antidepressants for 20+ years and never experienced this. Did you read the insert that comes with the medication to see if that’s a known side effect? 0
7 I was diagnosed at 34. Relatively mild, maybe not progressing or very mildly. 0
8 My 12 year old daughter doesn’t have a choice. She has to have a cornea transplant in her left eye. It’s the only way to repair all of damage that has been done. She has a cornea scar. So please make sure you don’t rub your eyes. 0
9 35 y.o. do not get **age-related** macular degeneration. To the extent your telling is an accurate characterization of what the first optom said, s/he is clueless, and you’ve spent a lot of money on AREDS2 MVIs for nothing. If you literally can’t sleep/function because of your anxiety over this, you have an anxiety condition that needs evaluation. 0
10 I was diagnosed about 2 months ago. Because I was going for new glasses, but the readings were off. I’m now in my 30s and wish I was diagnosed at your age, then a lot more could have been done. Got scleral lenses last Thursday, I was amazed by the difference. I agree with the other posters here. 0
11 I’m under 30, so the idea of needing any medication for life is scary. 0
12 It’s ok I was 20 when I had CXL and I hated that too lmao. 0
13 In 20 years or so you’re going to need *at least* reading glasses. 0
Task 10 – Detection of disease mentions in tweets – SocialDisNER (in Spanish)

This task will focus on the recognition of disease mentions in tweets written in Spanish after selecting primarily first-hand experience of diseases and other health-relevant content (from patient associations and professional healthcare institutions).

The aim is to use social media as a proxy to better understand societal perception of disease, from rare immunological and genetic diseases such as cystic fibrosis, highly prevalent conditions such as cancer and diabetes, to often controversial diagnoses such as fibromyalgia and even mental health disorders.

Automatic data selection actively retrieved posts with personal messages and from patient associations. Thus, the SocialDisNER shared task will enable training deep learning named entity recognition approaches to detect all kinds of disease mentions in social media, including both lay and professional language.

There will only be a single sub-track: NER offset detection and classification. Participants must find the beginning and end of disease mentions.

  • Training data: ~6000 tweets
  • Validation data: ~2000 tweets
  • Test data: ~2000 tweets
  • Evaluation metric: Strict Precision, Recall and F1-score


Luis Gasco, Barcelona Supercomputing Center, Spain,

Darryl Estrada, Barcelona Supercomputing Center, Spain,


Task Website:

Submission format: tab-separated file with headers, same format used in the validation set.

tweet_id begin end label span
25 131 139 ENFERMEDAD diabetes
25 198 201 ENFERMEDAD DM2