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  <title>Important Dates | POLAR </title>

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    <a class="nav-link" href="index.html#about">About</a>
    <a class="nav-link" href="tasks.html">Tasks</a>
    <a class="nav-link" href="participation.html">Participation</a>
    <a class="nav-link" href="organizers.html">Organizers</a>
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    <h2>🧠 Task Overview</h2>
    <p>
      Polarization refers to the division of opinions into two sharply contrasting groups, especially when marked by hostility, intolerance, or exclusion.
      In the digital era, polarization is intensifying across platforms and geographies, affecting public discourse, exacerbating conflicts, and contributing to societal fragmentation.
    </p>
    <p>
      This shared task is the first SemEval task on polarization, and it seeks to advance the computational understanding of how polarization manifests in text across multiple languages, cultures, and event types.
      Participants will develop models that can detect and interpret polarization in a variety of online contexts.
    </p>
    <p>
      The task focuses on textual data collected from real-world events including elections, international conflicts, social protests, and ideological debates. The goal is to evaluate systems' abilities to identify polarized content, classify its target, and determine how polarization is expressed linguistically.
    </p>

    <h3>🌍 Multilingual, Multicultural, and Multievent Scope</h3>
    <p>
      This task emphasizes global inclusivity and cross-cultural representation. We include data from 15 languages, many of which are low-resource and underrepresented in mainstream NLP tasks.
    </p>
    <p><strong>Languages include:</strong></p>
    <ul>
      <li><strong>High-resource:</strong> English, German, Spanish, Arabic</li>
      <li><strong>Mid-resource:</strong> Urdu, Mozambican Portuguese, Amharic</li>
      <li><strong>Low-resource:</strong> Kinyarwanda, Hausa, Igbo, Twi, Swahili, isiXhosa, Zulu, Emakhuwa</li>
    </ul>

    <h3>🧪 Task Format and Subtasks</h3>
    <p>Participants may choose to compete in one or more of the following three subtasks:</p>
    <h5>Subtask 1: Polarization Detection</h5>
    <p>Binary classification: Identify whether a post contains polarized content.</p>
    <ul>
      <li>Labels: Polarized, Not Polarized</li>
    </ul>

    <h5>Subtask 2: Polarization Type Classification</h5>
    <p>Classify the target of polarization.</p>
    <ul>
      <li>Political groups or ideologies</li>
      <li>Religious groups or beliefs</li>
      <li>Racial or ethnic communities</li>
      <li>Gender identities</li>
      <li>Sexual orientations</li>
      <li>Other/domain-specific targets</li>
    </ul>

    <h5>Subtask 3: Manifestation Identification</h5>
    <p>Classify how polarization is expressed. Multiple labels possible.</p>
    <ul>
      <li>Stereotyping</li>
      <li>Vilification</li>
      <li>Dehumanization</li>
      <li>Deindividuation</li>
      <li>Use of Extreme Language</li>
      <li>Lack of Empathy</li>
      <li>Invalidation</li>
    </ul>

    <h3>📁 Data Description</h3>
    <p>
      Dataset sources: News websites, Reddit, blogs, Bluesky, regional forums. Event types include elections, conflicts, gender rights, migration, and more.
    </p>
    <p>Each language has 3,000–5,000 annotated instances. Tools used: Label Studio, Prolific, Potato, Mechanical Turk.</p>
    <p><strong>Sample Inter-Annotator Agreement (IAA):</strong></p>
    <ul>
      <li>Amharic: Kappa = 0.49</li>
      <li>Urdu: Kappa = 0.83</li>
      <li>English: Kappa = 0.52</li>
    </ul>
    <p>Datasets split into training, development, and test sets.</p>

    <h3>🎯 Research Contributions</h3>
    <ul>
      <li>Advancing socially responsible AI</li>
      <li>Supporting low-resource language NLP</li>
      <li>Fostering explainable and inclusive NLP systems</li>
      <li>Creating multilingual benchmarks for polarization detection</li>
    </ul>

    <h3>📅 Timeline</h3>
    <table class="table table-bordered">
      <thead>
        <tr><th>Phase</th><th>Date (Tentative)</th></tr>
      </thead>
      <tbody>
        <tr><td>Task Announcement</td><td>June 2025</td></tr>
        <tr><td>Data Release</td><td>July 2025</td></tr>
        <tr><td>Evaluation Phase Begins</td><td>September 2025</td></tr>
        <tr><td>Submission Deadline</td><td>October 2025</td></tr>
        <tr><td>Paper Submission to SemEval</td><td>November 2025</td></tr>
        <tr><td>SemEval Workshop @ ACL</td><td>March or April 2026</td></tr>
      </tbody>
    </table>

    <h3>🧑‍🤝‍🧑 Who Should Participate?</h3>
    <ul>
      <li>NLP researchers and developers</li>
      <li>Computational social science teams</li>
      <li>Practitioners in hate speech/misinformation</li>
      <li>Peacebuilding and civil society organizations</li>
      <li>Students and cross-disciplinary academics</li>
    </ul>

    <h3>👥 Organizing Team</h3>
    <p>
      Researchers from: University of Hamburg, Bahir Dar University, Macquarie University, Imperial College London, University of Pretoria, Zayed University, Bayero University Kano, Northeastern University
    </p>

    <h3>📬 Contact and Community</h3>
    <ul>
      <li>Email: your-email@domain.com</li>
      <li>Slack Channel: Link TBA</li>
      <li>Mailing List: Link TBA</li>
      <li>GitHub: Link TBA</li>
    </ul>
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