IEEE BigData 2026 Cup: Explainable Suicide Risk Explainable on Social Media

The IEEE BigData 2026 Cup Challenge on Explainable Suicide Risk Assessment on Social Media is part of the annual Big Data Cup series held under the auspices of the IEEE International Conference on Big Data (https://bigdataieee.org/BigData2026/). This competition introduces a dual-objective design that advances both suicide risk detection and clinical interpretability. Participants are required to: (1) identify the suicide risk level of a social media post (40%), and extract from the original text phrases or short clauses that serve as evidence for the risk level judgment (30%); (2) identify suicide factors in the post (30%). The top 8 teams will be invited to submit papers describing their solutions. Accepted papers will be presented at the IEEE BigData 2026 conference (Phoenix, Arizona, USA, December 14–17, 2026).

The topic of this year's competition is explainable suicide risk assessment from Reddit posts, with a focus on both suicide risk level detection and clinical evidence extraction. The dataset contains Reddit posts collected from mental health communities ("r/SuicideWatch" in Reddit), annotated by trained annotators following established clinical risk assessment protocols grounded in the Columbia-Suicide Severity Rating Scale (C-SSRS).

During the competition, participants upload formatted predictions file to a live leaderboard for real‑time feedback. Near the competition end, participants upload a solution report (report format: IEEE conference template) and solution code via a provided Google Drive link. The organizers then perform a final evaluation on a separate dataset and assess report quality. Invitations are based on final performance of solution, and report quality (approach innovation, experiments, writing). The final ranking determines the top 8 teams, who will be invited to extend their work for submission and publication in the IEEE BigData 2026 proceedings (subject to Organizing Committee and PC members review). Authors of accepted papers will be invited to present at the conference.

This challenge consists of two subtasks. Participants may compete in both subtasks. The final composite score is weighted 70% on Subtask 1 and 30% on Subtask 2.

Subtask 1: Suicide Risk Detection (Weight: 70%)
Given a Reddit post, identify the author's suicide risk level into one of four categories:

Indicator: The post contains no explicit mentions of suicide.
Ideation: The post contains explicit suicidal expressions but no suicidal plan.
Behavior: The post contains explicit suicidal expressions and self-harm/suicidal plan.
Attempt: The post contains explicit mentions of recent/past suicidal attempts.

Evaluation on Subtask 1: 1) identify the suicide risk level of a social media post: Weighted F1. 2) The evidence extraction task is evaluated using Phrase F1. After case-insensitive normalization, a predicted phrase is considered correct if it either contains a ground-truth phrase or is contained by a ground-truth phrase. This allows partial but faithful extraction, such as matching "kill myself" with "I want to kill myself". Phrase precision measures how many predicted phrases match the ground truth, while phrase recall measures how many ground-truth phrases are covered by the prediction. The final Phrase F1 is averaged over all posts.

Subtask 2: Suicide Factors Identification (Weight: 30%)
Given the same post, identify all suicide-related factors that are present in the post. This subtask is formulated as a multi-label classification problem: each post may be assigned zero, one, or multiple factor labels. The factor taxonomy is adapted from Li, J., Wang, X., Li, H., Yan, Y., Leong, H. V., Feng, L., et al. (2025), Protective factor-aware dynamic influence learning for suicide risk prediction on social media, arXiv:2507.10008.

The factor taxonomy includes the following categories:

mental health issues
physical health/characteristic
substance use
hopelessness
emotion dysregulation
low self-esteem
poor school performance
low socio-economic status
interpersonal violence
prior self-harm or suicidal thought/attempt
poor social support
interpersonal difficulty
dysfunctional family
exposure to others' suicide
stressful life event
traumatic experience
cognitive deficits
suicide means (with access)
sexual orientation related issues
social support
coping strategy
psychological capital
sense of responsibility
meaning in life

Participants are expected to predict all applicable factor labels for each post according to the predefined factor taxonomy. This subtask will be evaluated using the Macro F1 score across all factor categories.

Composite Scoring: The leaderboard score is computed as:

Score = Subtask1 (0.4*suicide risk detection + 0.3*evidence support for detection) + Subtask2 (0.3*suicide factors identification)

Teams that participate in Subtask 1 only will be scored on Subtask 1 alone (no penalty for skipping Subtask 2).

Submission format: Please submit your detections as a .csv file with the following structure. The file name must be YourTeamName.csv.

Field	Type	Description
row_id	string	The post identifier from the dataset
risk_level	string	One of: Indicator, Ideation, Behavior, Attempt
evidence	string	Semicolon-separated evidence text spans that support the predicted suicide risk level (e.g., want to kill myself; feel hopeless). Each span should be copied verbatim from the original post.
factors	list	List of factor categories

A sample submission file will be provided with the dataset release.

Final evaluation: The final evaluation will be conducted after the submission deadline using a held-out test set. Only teams that submit both their source code and a report by the deadline will qualify for the final evaluation. Code and reports will be submitted via Google Drive; instructions will be sent by email in advance.

Please submit the prediction file created by your team. Multiple submissions are permitted during the evaluation phase (up to 3 per day). The file format should be .csv, and the file name must be: YourTeamName.csv. Scores of uploaded prediction results will be updated on the leaderboard in real time. For a detailed explanation of the submission format, please refer to the 'Task Description' section above.

Based on the submitted final solution, teams will be evaluated according to the following selection criteria:

Model performance (30%)
Approach innovation (40%)
Report quality (30%)

The top 8 teams will be invited to submit a paper describing their solution (up to 10 page IEEE 2-column conference format, reference pages counted in the 10 pages) for the IEEE BigData 2026 proceedings and to present at the IEEE BigData 2026 conference (Phoenix, Arizona, December 14–17, 2026). Certificates of achievement will be issued to all teams.

Paper submission system: https://bigdataieee.org/BigData2026/ (link to be updated when the chairs open the proceedings submission portal).

Review policy: single-blind review.

The leaderboard will be updated in real time during the evaluation phase (starting June 1, 2026). Results below show the composite score (70% Subtask 1 + 30% Subtask 2).

Rank	Team Name	Subtask 1	Subtask 2	Composite Score
1	HelloWorld	0.4346	0.2292	0.3730

Start of the competition, task revealed and registration opens — May 10, 2026
Deadline for teams to submit email of intent — June 20, 2026
Deadline for submitting the source code & the detailed report of the solutions, End of the competition — August 31, 2026
Announcement of final results and winning teams — September 15, 2026
Sending invitations for submitting papers at IEEE BigData 2026 — September 15, 2026
Deadline for submitting invited papers — October 15, 2026
Notification of paper acceptance — October 30, 2026
Camera-ready of accepted papers due — November 15, 2026
IEEE BigData 2026 conference, Phoenix, Arizona, USA — December 14–17, 2026

Once you have read and accepted the Data Usage Agreement below, please send your team's information to the registration email address in the following format. We will respond with the dataset download link.

Team name
List of team members with affiliations
Contact person and email address

We accept the Competition Data Usage Agreement

Prof. Li Qing, The Hong Kong Polytechnic University
Dr. Hong Va Leong, The Hong Kong Polytechnic University

For registration and general inquiries, contact Alex at hialex.li@connect.polyu.hk

Q: Do I need to submit a formal letter of intent?

Participants do not need to submit a formal letter of intent. To register, simply send us an email by June 1, 2026 with your team information. Once you receive a reply with the dataset link, your registration is confirmed.

Q: Can I participate in only one subtask?

Yes. Subtask 1 is the primary task. You may choose to participate in Subtask 1 only. Teams that complete both subtasks will be ranked on the 70/30 composite leaderboard;

Q: How many submissions are allowed per day?

During the evaluation phase, teams may submit up to 3 prediction files per day.

Q: Are pre-trained language models and LLMs allowed?

Yes. Participants may use any publicly available pre-trained model, including large language models (LLMs). Use of external labeled data beyond what is provided in the dataset must be declared in the report.

Q: I uploaded predictions but the leaderboard score has not updated yet.

A: The server may occasionally respond slowly. Please refresh the webpage first. If your score is still not updated after 10 minutes, please email us and we will check it as soon as possible.

Q: What is the maximum team size?

There is no strict maximum team size. All team members must be listed at registration and in the submitted report.

Q: Will the test set labels be released after the competition?

Yes. After the final evaluation and announcement of results, the complete annotated test set will be released to all registered participants for research purposes, subject to the Data Usage Agreement.

Registration

Data Usage Agreement