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Dataset results
7 results for “Stance Classification”
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper
<p>Corpora used in the publication:</p> <ul> <li>Cristina España-Bonet. 2023. <strong>Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper. </strong>In <em>Findings of the Association for Computational Linguistics: EMNLP 2023</em>, Singapore. Pages 11757–11777. Association for Computational Linguistics.</li> </ul> <p>Three corpora are included:</p> <ol> <li>Newspaper articles extracted from the OSCAR corpus in English, German, Spanish and Catalan automatically annotated for political stance (left vs right) and topic</li> <li>Newspaper-like article generations by different versions of ChatGPT for 101 topics in the 4 languages</li> <li>Newspaper-like article generations by Bard for 101 topics in the 4 languages</li> </ol> <p>See the README file and the original article for further details.</p>
Irish Rumour Stance Classification Dataset
<p>We create a new public test set for rumour stance classification with substantial differences from the <a href="https://aclanthology.org/S19-2147/">RumourEval dataset</a> over both vocabulary and stance distribution, making it well-suited for studies on evaluation or adaptation under domain shift.</p>
Same Side Stance Classification Resampled Datasets
<p>The resampled datasets for the <a href="http://sameside.webis.de/">Same Side Stance Classification</a> problem used in the EMNLP'21 paper "On Classifying whether Two Texts are on the Same Side of an Argument".</p> <p>The data is based on the publicly available <a href="https://zenodo.org/record/4382353">S3C training datasets</a>.</p> <p>The data format is <em>JSONlines</em>.<br> Python Load Example: (for every single task split)</p> <pre><code class="language-python">import pandas as pd df_cross_dev = pd.read_json("cross_dev.jsonl", lines=True)</code></pre> <p>For details on how the data was compiled, please refer to our <a href="https://github.com/webis-de/emnlp21-same-stance">code</a>.</p>
DIVERSE: Deciphering Internet Views on the U.S. Military Through Video Comment Stance Analysis: A Novel Benchmark Dataset for Stance Classification
<p>Paper citation: Cruickshank, Iain J., and Lynnette Hui Xian Ng. "DIVERSE: Deciphering Internet Views on the US Military Through Video Comment Stance Analysis, A Novel Benchmark Dataset for Stance Classification." <em>arXiv preprint arXiv:2403.03334</em> (2024).</p> <p>Link to paper: https://arxiv.org/abs/2403.03334</p>
Same Side Stance Classification Adversarial Test Cases
<p>This dataset contains 175 cases. It is manually created to reveal the ability of models to solve different types of <strong>adversarial</strong> cases for same side stance predictions more systematically. The examples selected here are derived from the dataset used in the <a href="https://sameside.webis.de/">Same Side Stance Classification</a> shared task.</p> <p>We have selected <strong>25</strong> distinct arguments from the <em>"gay marriage"</em> topic that are short and express their stance clearly. For each selected argument, we construct new arguments of four distinct types to obtain two pairs, one with the same stance, and one with an opposing stance:</p> <ul> <li><strong>Negation</strong>: a simple negation of the argument.</li> <li><strong>Paraphrase</strong>: alters important words from the argument to synonymous expressions with the same stance.</li> <li><strong>Argument</strong>: uses an argument from the same topic and stance, but semantically completely different regarding the first one.</li> <li><strong>Citation</strong>: repeats or summarizes the first argument and then expresses agreement or rejection (a case frequently occurring in the dataset).</li> </ul> <p>The types <em>Paraphrase</em>, <em>Argument</em>, and <em>Citation</em> are also formulated in a negated version to create additional test instances for the opposite stance.</p>
Same Side Stance Classification Challenge
<p>The dataset contains argument pairs which are sampled from <a href="https://zenodo.org/record/4139439#.X-C6nOG-q6M">args.me dataset</a> and cover two topics: abortion and gay marriage. The dataset is used in the <a href="http://sameside.webis.de/">same side stance classification challenge</a> which consists of two experiments (cross-topics and within topics).</p> <p>Cite the dataset as <a href="https://webis.de/publications.html#stein_2021">Benno Stein, Yamen Ajjour, Roxanne El Baff, Khalid Al-Khatib, Philipp Cimiano, and Henning Wachsmuth. Same Side Stance Classification. In Yamen Ajjour et al, editors, Same Side Stance Classification Shared Task 2019, volume 2921, July 2021</a></p>
Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification
<p>Code and dataset for paper "Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification". ICWSM 2025</p> <p>Preprint: https://arxiv.org/abs/2407.17688</p> <p>Citation: </p> <p>@misc{ng2024examininginfluencepoliticalbias,<br> title={Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification}, <br> author={Lynnette Hui Xian Ng and Iain Cruickshank and Roy Ka-Wei Lee},<br> year={2024},<br> eprint={2407.17688},<br> archivePrefix={arXiv},<br> primaryClass={cs.CL},<br> url={https://arxiv.org/abs/2407.17688}, <br>}</p>
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