Early risk prediction on the Internet

Participate

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This is the ninth year of eRisk and the lab plans to organize three tasks:

Task 1: Search for Symptoms of Depression

This is a continuation of eRisk 2024's Task 1.

The task consists of ranking sentences from a collection of user writings according to their relevance to a depression symptom. The participants will have to provide rankings for the 21 symptoms of depression from the BDI Questionnaire. A sentence is deemed relevant if it provides information about the user's condition regarding a particular symptom. That is, it may be relevant even when it indicates that the user is okay with the symptom.

We will release a TREC-formatted sentence-tagged dataset (based on eRisk past data) together with the BDI questionnaire. Participants are free to decide on the best strategy to derive queries from describing the BDI symptoms in the questionnaire.

After receiving the runs from the participating teams, we will create the relevance judgments with the help of human assessors using pooling. The resulting qrels will be used to evaluate the systems with classical ranking metrics (e.g., MAP, nDCG, etc.). This new corpus with annotated sentences would be a valuable resource with multiple applications beyond eRisk.

The task is organized into two different stages:

• Submission stage. After the release of the datasets, the participants will have time to produce and upload to our FTP server their TREC-formatted runs. Each participant may upload up to 5 files corresponding to 5 systems to the FTP.
  The required submission TREC format is as follows:

              symptom_number   Q0   sentence-id   position_in_ranking   score   system_name
                  

  An example of the format of your runs should be as follows:

              1   Q0   sentence-id-121   0001   10    myGroupNameMyMethodName
              1   Q0   sentence-id-234   0002   9.5   myGroupNameMyMethodName
              1   Q0   sentence-id-345   0003   9     myGroupNameMyMethodName
              ...
              21  Q0   sentence-id-456   0998   1.25  myGroupNameMyMethodName
              21  Q0   sentence-id-242   0999   1     myGroupNameMyMethodName
              21  Q0   sentence-id-347   1000   0.9   myGroupNameMyMethodName
                  

  Participants should submit up to 1000 results sorted by estimated relevance for each of the 21 symptoms of the BDI-II questionnaire. Each line contains: symptom_number, Q0, sentence-id, position_in_ranking, score, system_name.

• Evaluation stage. Once the submission stage is closed, the submitted runs will be used for obtaining the relevance judgments using classical pooling strategies with human assessors. With those judgments, systems will be evaluated.

The proceedings of the lab will be published in the online CEUR-WS Proceedings and on the conference website.

To have access to the collection, all participants have to fill, sign, and send a user agreement form (follow the instructions provided here). Once you have submitted the signed copyright form, you can proceed to register for the lab at CLEF 2025 Labs Registration site.

Important Dates

🆕 Task 2: Contextualized Early Detection of Depression

New task introduced in eRisk 2025.

This new task focuses on detecting early signs of depression by analyzing full conversational contexts. Unlike previous tasks that focused on isolated user posts, this challenge considers the broader dynamics of interactions by incorporating writings from all individuals involved in the conversation. Participants must process user interactions sequentially, analyze natural dialogues, and detect signs of depression within these rich contexts. Texts will be processed chronologically to simulate real-world conditions, making the task applicable to monitoring user interactions in blogs, social networks, or other types of online media.

The test collection for this task follows the format described in Losada & Crestani, 2016 and is derived from the same data sources as previous eRisk tasks. The dataset includes:

• 1. Writing history from the specific user: Posts from specific target social media users for depression estimation.
• 2. Full conversational contexts:
  • The discussion title and comment.
  • Comments from all users participating in the conversation, in chronological order.
  • Messages exchanged between the specific target user to classify and others in the discussion.

There are two categories of users: individuals suffering depression and control users. For each user, the collection contains a sequence of writings from that specific user along with the rest of the users that participated in the conversation (in chronological order). This approach allows systems to monitor ongoing interactions and make timely decisions based on the evolution of the conversation.

The task is organized into two different stages:

• Training Stage. Participants will be provided with a dataset containing isolated writings from a set of training users. For each user, the dataset will indicate whether they have explicitly mentioned a depression diagnosis.
  
  
  • Participants will only have access to the user’s writings, not the full conversation context.
  • Training data will include users from prior early depression detection tasks, allowing teams to train their systems effectively.
• Test Stage. During the test phase, participants will connect to our server that provides user writings iteratively, including full conversational contexts (e.g., the discussion title and other users’ comments).
  
  

  Participants have to:

  • Analyze the data in real time and send their predictions after processing each writing.
  • Use the provided full conversational contexts for each user interaction, simulating real-world scenarios.

  More information on connecting to the eRisk server is provided here.

Evaluation: The evaluation will consider not only the correctness of the system's output (i.e., whether or not the user is depressed) but also the delay taken to emit its decision. To meet this aim, we will consider the ERDE metric proposed in Losada & Crestani, 2016 and other alternative evaluation measures. A full description of the evaluation metrics can be found in 2021's eRisk overview.

The proceedings of the lab will be published in the online CEUR-WS Proceedings and on the conference website.

To have access to the collection, all participants must fill, sign, and send a user agreement form (follow the instructions provided here). Once you have submitted the signed copyright form, you can proceed to register for the lab at CLEF 2025 Labs Registration site.

Important Dates

Pilot Task: Conversational Depression Detection via LLMs

This pilot task introduces a unique challenge: detecting depression through conversational agents. Participants will interact with a large language model (LLM) persona that has been fine-tuned using user writings, simulating real-world conversational exchanges. The challenge lies in determining whether the LLM persona exhibits signs of depression, accompanied by an explanation of the main symptoms that informed their decision. This task pushes participants to develop more interactive, dynamic models that can engage with users and assess their mental state through dialogue.

• Training Stage. Since this is a pilot and new task in eRisk, there will be no training data. The participants need to obtain their own data or make unsupervised methods for the task.
• Test Stage. During the test stage, participants will connect to a server where they can interact with the LLMs, requiring them to identify depressive symptoms within a conversational window.
  
  

  Key Details:

  • Participants will be provided a form to select time slots for interacting with the LLM.
  • The number of messages participants can exchange with the LLM will be limited. This limit will be decided based on initial pilot experiments involving health professionals.

Evaluation: **Coming soon**