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Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information).
Multi-task learning of pairwise sequence classification tasks over disparate label spaces
Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. 2018 · 1906
Earlier work this paper cites.
The argument reasoning comprehension task: Identification and reconstruction of implicit warrants
Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, and Benno Stein. 2018 · 1940
Earlier work this paper cites.
Towards few-shot fact-checking via perplexity
Nayeon Lee, Yejin Bang, Andrea Madotto, and Pascale Fung. 2021 · 1981
Earlier work this paper cites.
Adverbial stance types in English
Douglas Biber and Edward Finegan. 1988 · 1988
Earlier work this paper cites.
Multisensor fusion and integration: approaches, applications, and future research directions
R.C. Luo, Chih-Chen Yih, and Kuo Lan Su. 2002 · 2002
Earlier work this paper cites.
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
Earlier work this paper cites.
The stance triangle
John W Du Bois. 2007 · 2007
Earlier work this paper cites.
Twitter under crisis: Can we trust what we RT?
Marcelo Mendoza, Barbara Poblete, and Carlos Castillo. 2010 · 2010
Earlier work this paper cites.
Rumor has it: Identifying misinformation in microblogs
Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, and Qiaozhu Mei. 2011 · 2011
Earlier work this paper cites.
Battling the internet water army: Detection of hidden paid posters
Cheng Chen, Kui Wu, Venkatesh Srinivasan, and Xudong Zhang. 2013 · 2013
Earlier work this paper cites.
Why are you taking this stance? Identifying and classifying reasons in ideological debates
Kazi Saidul Hasan and Vincent Ng. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Knowledge-based trust: Estimating the trustworthiness of web sources
Xin Luna Dong, Evgeniy Gabrilovich, Kevin Murphy, Van Dang, Wilko Horn, Camillo Lugaresi, Shaohua Sun, and Wei Zhang. 2015 · 2015
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor S. Lempitsky. 2015 · 2015
Earlier work this paper cites.
Leveraging joint interactions for credibility analysis in news communities
Subhabrata Mukherjee and Gerhard Weikum. 2015 · 2015
Earlier work this paper cites.
Stance detection with bidirectional conditional encoding
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. 2016 · 2016
Earlier work this paper cites.
Emergent: a novel data-set for stance classification
William Ferreira and Andreas Vlachos. 2016 · 2016
Earlier work this paper cites.
Hawkes processes for continuous time sequence classification: an application to rumour stance classification in Twitter
Michal Lukasik, P. K. Srijith, Duy Vu, Kalina Bontcheva, Arkaitz Zubiaga, and Trevor Cohn. 2016 · 2016
Earlier work this paper cites.
SemEval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016 · 2016
Earlier work this paper cites.
Simple open stance classification for rumour analysis
Ahmet Aker, Leon Derczynski, and Kalina Bontcheva. 2017 · 2017
Earlier work this paper cites.
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Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017 · 2017
Earlier work this paper cites.
Seminar users in the Arabic Twitter sphere
Kareem Darwish, Dimitar Alexandrov, Preslav Nakov, and Yelena Mejova. 2017 · 2017
Earlier work this paper cites.
SemEval-2017 task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017 · 2017
Earlier work this paper cites.
Turing at SemEval-2017 task 8: Sequential approach to rumour stance classification with branch-LSTM
Elena Kochkina, Maria Liakata, and Isabelle Augenstein. 2017 · 2017
Earlier work this paper cites.
An army of me: Sockpuppets in online discussion communities
Srijan Kumar, Justin Cheng, Jure Leskovec, and V. S. Subrahmanian. 2017 · 2017
Earlier work this paper cites.
Detection of sockpuppets in social media
Suman Kalyan Maity, Aishik Chakraborty, Pawan Goyal, and Animesh Mukherjee. 2017 · 2017
Earlier work this paper cites.
Fake news challenge stage 1 (FNC-I): Stance detection
Dean Pomerleau and Delip Rao. 2017 · 2017
Earlier work this paper cites.
Where the truth lies: Explaining the credibility of emerging claims on the Web and social media
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017 · 2017
Earlier work this paper cites.
Truth of varying shades: Analyzing language in fake news and political fact-checking
Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017 · 2017
Earlier work this paper cites.
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Benjamin Riedel, Isabelle Augenstein, Georgios P Spithourakis, and Sebastian Riedel. 2017 · 2017
Earlier work this paper cites.
SemEval-2017 task 4: Sentiment analysis in Twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017
Earlier work this paper cites.
A temporal attentional model for rumor stance classification
Amir Pouran Ben Veyseh, Javid Ebrahimi, Dejing Dou, and Daniel Lowd. 2017 · 2017
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“Liar, Liar Pants on Fire”: A new benchmark dataset for fake news detection
William Yang Wang. 2017 · 2017
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Predicting factuality of reporting and bias of news media sources
Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018a · 2018
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Integrating stance detection and fact checking in a unified corpus
Ramy Baly, Mitra Mohtarami, James Glass, Lluís Màrquez, Alessandro Moschitti, and Preslav Nakov. 2018b · 2018
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Can rumour stance alone predict veracity?
Sebastian Dungs, Ahmet Aker, Norbert Fuhr, and Kalina Bontcheva. 2018 · 2018
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Stance detection in fake news a combined feature representation
Bilal Ghanem, Paolo Rosso, and Francisco Rangel. 2018 · 2018
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Multi-source domain adaptation with mixture of experts
Jiang Guo, Darsh Shah, and Regina Barzilay. 2018 · 2018
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All-in-one: Multi-task learning for rumour verification
Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2018 · 2018
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An end-to-end multi-task learning model for fact checking
Sizhen Li, Shuai Zhao, Bo Cheng, and Hao Yang. 2018 · 2018
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Team Papelo: Transformer networks at FEVER
Christopher Malon. 2018 · 2018
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Automatic stance detection using end-to-end memory networks
Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, and Alessandro Moschitti. 2018 · 2018
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Overview of the CLEF-2018 CheckThat! lab on automatic identification and verification of political claims
Preslav Nakov, Alberto Barrón-Cedeño, Tamer Elsayed, Reem Suwaileh, Lluís Màrquez, Wajdi Zaghouani, Pepa Atanasova, Spas Kyuchukov, and Giovanni Da San Martino. 2018 · 2018
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An interpretable joint graphical model for fact-checking from crowds
An T. Nguyen, Aditya Kharosekar, Matthew Lease, and Byron C. Wallace. 2018 · 2018
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Deep contextualized word representations
Stance prediction and claim verification: An Arabic perspective
Jude Khouja. 2020 · 2020
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The hateful memes challenge: Detecting hate speech in multimodal memes
Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. 2020 · 2020
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Stance detection: A survey
Dilek Küçük and Fazli Can. 2020 · 2020
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Language models as fact checkers?
Nayeon Lee, Belinda Z. Li, Sinong Wang, Wen-tau Yih, Hao Ma, and Madian Khabsa. 2020 · 2020
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Exploiting microblog conversation structures to detect rumors
Jiawen Li, Yudianto Sujana, and Hung-Yu Kao. 2020 · 2020
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Debunking rumors on Twitter with tree transformer
Jing Ma and Wei Gao. 2020 · 2020
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Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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CredEye: A credibility lens for analyzing and explaining misinformation
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2018 · 2018
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A stylometric inquiry into hyperpartisan and fake news
Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2018 · 2018
Cited alongside, same era.
BuzzFace: A news veracity dataset with Facebook user commentary and egos
Giovanni Santia and Jake Williams. 2018 · 2018
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Automated fact checking: Task formulations, methods and future directions
James Thorne and Andreas Vlachos. 2018 · 2018
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FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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Cross-lingual cross-platform rumor verification pivoting on multimedia content
Weiming Wen, Songwen Su, and Zhou Yu. 2018 · 2018
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Fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection
Kai Nakamura, Sharon Levy, and William Yang Wang. 2020 · 2020
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FANG: leveraging social context for fake news detection using graph representation
Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, and Min-Yen Kan. 2020 · 2020
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That is a known lie: Detecting previously fact-checked claims
Shaden Shaar, Nikolay Babulkov, Giovanni Da San Martino, and Preslav Nakov. 2020 · 2020
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Political framing: US COVID19 blame game
Chereen Shurafa, Kareem Darwish, and Wajdi Zaghouani. 2020 · 2020
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Transfer learning from transformers to fake news challenge stance detection (FNC-1) task
Valeriya Slovikovskaya and Giuseppe Attardi. 2020 · 2020
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Predicting the topical stance and political leaning of media using tweets
Peter Stefanov, Kareem Darwish, Atanas Atanasov, and Preslav Nakov. 2020 · 2020
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Early detection of rumours on Twitter via stance transfer learning
Lin Tian, Xiuzhen Zhang, Yan Wang, and Huan Liu. 2020 · 2020
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X-Stance: A multilingual multi-target dataset for stance detection
Jannis Vamvas and Rico Sennrich. 2020 · 2020
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Where are the facts? Searching for fact-checked information to alleviate the spread of fake news
Nguyen Vo and Kyumin Lee. 2020 · 2020
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Robust reasoning over heterogeneous textual information for fact verification
Yongyue Wang, Chunhe Xia, Chengxiang Si, Beitong Yao, and Tianbo Wang. 2020 · 2020
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Transformer based multi-source domain adaptation
Dustin Wright and Isabelle Augenstein. 2020 · 2020
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Coreferential reasoning learning for language representation
Deming Ye, Yankai Lin, Jiaju Du, Zhenghao Liu, Peng Li, Maosong Sun, and Zhiyuan Liu. 2020 · 2020
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Coupled hierarchical transformer for stance-aware rumor verification in social media conversations
Jianfei Yu, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu, and Rui Xia. 2020 · 2020
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Transformer-XH: Multi-evidence reasoning with extra hop attention
Chen Zhao, Chenyan Xiong, Corby Rosset, Xia Song, Paul N. Bennett, and Saurabh Tiwary. 2020 · 2020
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Reasoning over semantic-level graph for fact checking
Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2020 · 2020
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Fighting the COVID-19 infodemic in social media: A holistic perspective and a call to arms
Firoj Alam, Fahim Dalvi, Shaden Shaar, Nadir Durrani, Hamdy Mubarak, Alex Nikolov, Giovanni Da San Martino, Ahmed Abdelali, Hassan Sajjad, Kareem Darwish, and Preslav Nakov. 2021 · 2021
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Stance detection on social media: State of the art and trends
Abeer Aldayel and Walid Magdy. 2021 · 2021
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AraStance: A multi-country and multi-domain dataset of Arabic stance detection for fact checking
Tariq Alhindi, Amal Alabdulkarim, Ali Alshehri, Muhammad Abdul-Mageed, and Preslav Nakov. 2021 · 2021
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SemEval-2021 task 6: Detection of persuasion techniques in texts and images
Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, and Giovanni Da San Martino. 2021b · 2021
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Multi-hop fact checking of political claims
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COVID-19 vaccine discourse on Twitter: A content analysis of persuasion techniques, sentiment and mis/disinformation
Denise Scannell, Linda Desens, Marie Guadagno, Yolande Tra, Emily Acker, Kate Sheridan, Margo Rosner, Jennifer Mathieu, and Mike Fulk. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
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Stance detection benchmark: How robust is your stance detection?
Benjamin Schiller, Johannes Daxenberger, and Iryna Gurevych. 2021 · 2021
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Topic-aware evidence reasoning and stance-aware aggregation for fact verification
Jiasheng Si, Deyu Zhou, Tongzhe Li, Xingyu Shi, and Yulan He. 2021 · 2021
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DISARM: Detecting the victims targeted by harmful memes
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Fact-checking meets fauxtography: Verifying claims about images
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