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POLAR: Detecting Multilingual, Multicultural and Multievent Online Polarization

SemEval-2026 Task 9 (POLAR): detecting online polarization across 22 languages, many cultures, and multiple real-world events. Annotators judge whether a social-media post is polarized and, if so, identify the type of polarization and how it is rhetorically manifested (e.g. stereotyping, vilification, dehumanization, intolerance). Polarization encompasses stereotyping, vilification, dehumanization, and intolerance directed at social groups.

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Quick start:

bash
git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/semeval/2026/task09-polar-polarization
potato start config.yaml

Configuration File

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# POLAR: Detecting Multilingual, Multicultural and Multievent Online Polarization
# Based on Naseem et al., SemEval-2026 Task 9
# Paper: https://aclanthology.org/2026.semeval-1.453/
# Task page: https://polar-semeval.github.io/
#
# Given a social-media post (one of 22 languages), judge whether it is
# polarized. The shared task has three subtasks:
#   - Subtask 1: binary polarization detection (polarized / not)
#   - Subtask 2: polarization type (the social dimension involved)
#   - Subtask 3: manifestation categories (how polarization is expressed)
# The organizers characterize polarization as encompassing stereotyping,
# vilification, dehumanization, and intolerance toward social groups; those
# form the manifestation labels below. This showcase collects the binary
# label, the manifestation(s), and the polarization type as free text
# (the official type inventory is defined by the task).

annotation_task_name: "POLAR - Online Polarization"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
export_annotation_format: "jsonl"

port: 8000

annotation_schemes:
  - annotation_type: radio
    name: polarized
    description: "Is this post polarized?"
    labels:
      - name: "Polarized"
        key_value: "1"
      - name: "Not polarized"
        key_value: "2"

  - annotation_type: multiselect
    name: manifestation
    description: "If polarized, how is it manifested? (Select all that apply)"
    labels:
      - name: "Stereotyping"
        tooltip: "Attributing fixed, oversimplified traits to a group"
      - name: "Vilification"
        tooltip: "Portraying a group as evil, blameworthy, or threatening"
      - name: "Dehumanization"
        tooltip: "Denying a group's humanity (e.g. comparing to animals/objects)"
      - name: "Intolerance"
        tooltip: "Rejecting a group's right to exist, speak, or participate"

  - annotation_type: text
    name: polarization_type
    description: "If polarized, what is the target dimension/type (e.g. political, religious, ethnic, gender)? - optional"
    textarea: false
    required: false

annotation_instructions: |
  Read the social-media post (an English gloss is provided for reference).
  First decide whether it is polarized. If it is, select how the
  polarization is manifested (stereotyping, vilification, dehumanization,
  intolerance) and note the target dimension (e.g. political, religious,
  ethnic, gender).

user_config:
  allow_all_users: true
max_annotations_per_user: 50
num_annotators_per_item: 3

Sample Data

json
[
  {
    "id": "pol_001",
    "language": "English",
    "text": "The city council approved the new bike lanes after months of public consultation.",
    "gloss": "The city council approved the new bike lanes after months of public consultation."
  },
  {
    "id": "pol_002",
    "language": "English",
    "text": "Those people are all the same, lazy and dishonest, and they'll ruin this country if we let them.",
    "gloss": "Those people are all the same, lazy and dishonest, and they'll ruin this country if we let them."
  }
]

// ... and 8 more items

Dataset & paper

Naseem et al., SemEval 2026 (Task 9)

Citation (BibTeX)

bibtex
@inproceedings{naseem-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization",
    author = "Naseem, Usman  and Geislinger, Robert  and Ren, Ada  and Kohail, Sarah  and Garrido Veliz, Rudy  and Muhammad, Shamsuddeen Hassan  and Yimam, Seid Muhie  and others",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.semeval-1.453",
    pages = "3779--3796"
}

Details

Annotation Types

radiomultiselecttext

Domain

NLPComputational Social ScienceSemEval

Use Cases

Polarization DetectionContent ModerationMultilingual NLP

Tags

semevalsemeval-2026shared-taskpolarizationmultilingualsocial-media

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