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Stance detection holds great potential to improve online political discussions through its deployment in discussion platforms for purposes such as content moderation, topic summarization or to facilitate more balanced discussions.
Query by committee
H. S. Seung, M. Opper, and H. Sompolinsky · 1992
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Entropy-based active learning for object recognition
Alex Holub, Pietro Perona, and Michael C Burl · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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What Creates Interactivity in Online News Discussions? An Exploratory Analysis of Discussion Factors in User Comments on News Items
Marc Ziegele, Timo Breiner, and Oliver Quiring · 2014
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Automatic debate text summarization in online debate forum
Alan Darmasaputra Chowanda, Albert Richard Sanyoto, Derwin Suhartono, and Criscentia Jessica Setiadi · 2017
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Active learning and visual analytics for stance classification with alva
Kostiantyn Kucher, Carita Paradis, Magnus Sahlgren, and Andreas Kerren · 2017
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Stance and sentiment in tweets
Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Encoding social information with graph convolutional networks forPolitical perspective detection in news media
Chang Li and Dan Goldwasser · 2019
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Stance detection: A survey
Dilek Küçük and Fazli Can · 2020
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Active learning approaches for labeling text: Review and assessment of the performance of active learning approaches
Blake Miller, Fridolin Linder, and Walter R. Mebane · 2020
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X-Stance: A multilingual multi-target dataset for stance detection
Jannis Vamvas and Rico Sennrich · 2020
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Stance detection on social media: State of the art and trends
Abeer ALDayel and Walid Magdy · 2021
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LoRA: Low-Rank Adaptation of Large Language Models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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P-stance: A large dataset for stance detection in political domain
Yingjie Li, Tiberiu Sosea, Aditya Sawant, Ajith Jayaraman Nair, Diana Inkpen, and Cornelia Caragea · 2021
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Active Learning by Acquiring Contrastive Examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras · 2021
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A survey on stance detection for mis- and disinformation identification
Momchil Hardalov, Arnav Arora, Preslav Nakov, and Isabelle Augenstein · 2022
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Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz · 2023
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Synthetic data generation with large language models for text classification: Potential and limitations
Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, and Ming Yin · 2023
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Anders Giovanni Møller, Jacob Aarup Dalsgaard, Arianna Pera, and Luca Maria Aiello · 2023
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Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nick Beauchamp, and Lu Wang · 2022
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On the importance of data size in probing fine-tuned models
Houman Mehrafarin, Sara Rajaee, and Mohammad Taher Pilehvar · 2022
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No Language Left Behind: Scaling Human-Centered Machine Translation
NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang · 2022
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Automated topic categorisation of citizens’ contributions: Reducing manual labelling efforts through active learning
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KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
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Stance Detection: A Practical Guide to Classifying Political Beliefs in Text
Michael Burnham · 2023
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Prompting and Fine-Tuning Open-Sourced Large Language Models for Stance Classification
Iain J. Cruickshank and Lynnette Hui Xian Ng · 2023
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Making sense of citizens’ input through artificial intelligence: A review of methods for computational text analysis to support the evaluation of contributions in public participation
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Classifying speech acts in political communication: A transformer-based approach with weak supervision and active learning
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Can Large Language Models Transform Computational Social Science?
Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang · 2023
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Maike Behrendt, Stefan Sylvius Wagner, and Stefan Harmeling · 2024
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Stance Detection on Social Media with Fine-Tuned Large Language Models
İlker Gül, Rémi Lebret, and Karl Aberer · 2024
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Zero-shot stance detection using contextual data generation with LLMs
Ghazaleh Mahmoudi, Babak Behkamkia, and Sauleh Eetemadi · 2024
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