StanceDetection-Papers

Literature Review

Stance Detection: A Survey DILEK KÜÇÜK,FAZLI CANACM19[pdf]

Dataset

1. SemEval-2016 Task 6: Detecting Stance in Tweets Saif M. Mohammad,Svetlana Kiritchenko,Parinaz Sobhani,Xiaodan Zhu,Colin Cherry [pdf]
2. A Dataset for Multi-Target Stance Detection(pair-target) [pdf]
3. Overview of NLPCC Shared Task 4: Stance Detection in Chinese MicroblogsRuifeng Xu,Jiachen Du,Lin Gui[pdf]
4. FNC-1(https://github.com/FakeNewsChallenge/fnc-1)
5. RumourEval(https://alt.qcri.org/semeval2017/task8/)
6. VAST(2020) Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations[pdf]

Stance Detection work

ID	Name	Description	Dataset	Paper	Conference
1	数据集	放出一个twitter上的新冠数据集	-	Stance Detection in COVID-19 Tweets	ACL21
2	[X]	数据增强	-	Target-Aware Data Augmentation for Stance Detection	NAACL21
3	数据集	数据集，减少立场-情感之间潜在的相关性。bias	-	tWT–WT: A Dataset to Assert the Role of Target Entities for Detecting Stance of Tweets	NAACL21
4	[KEMLM]	让MLM任务mask重要的词，重要的词用weighted log-odds-ratio technique计算方式来选出对不同立场重要的词，用这种方式，注入知识	所在数据集：2020数据集（拜登，川普）	Knowledge Enhanced Masked Language Model for Stance Detection	NAACL21
5	[ALM]	跨领域，对抗学习	数据集：SemEval 2016	Adversarial Learning for Zero-Shot Stance Detection on Social Media	NAACL21
6	[X]	增加对两个target的立场是否相同的辅任务	数据集：VAST	A Multi-Task Learning Framework for Multi-Target Stance Detection	ACL Findings 21
7	[CKM]	跨领域，应用conceptNet，对话题和文本分开建模	数据集：VAST	Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph	ACL Findings 21
8	[X]	跨领域，使用混合专家和领域适应方法。	-	Adversarial Learning for Zero-Shot Stance Detection on Social Media	EMNLP21
9	[X]	在stancy模型基础上，加入unstancy，进行训练；	-	Adversarial Learning for Zero-Shot Stance Detection on Social Media	EMNLP21
10	[X]	-	-	Adversarial Learning for Zero-Shot Stance Detection on Social Media	EMNLP21
11	[X]	-	-	Adversarial Learning for Zero-Shot Stance Detection on Social Media	EMNLP21
12	[X]	改造MLM任务，实现立场检测	数据集：SemEval	MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection	EMNLP21
13	[s/e-GRU]	引入sentiment、emotion信息。bert拿到结果之后，不直接进行分类，又利用GRU和max-pool和avg-pool进行表示	从procon.org整理数据。	Stance Prediction for Contemporary Issues: Data and Experiments	ACL20
14	[X]	-	-	Stance Prediction and Claim Verification: An Arabic Perspective	ACL20
15	[X]	-	-	Agreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity	ACL20
16	[X]	-	-	Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge	ACL20
17	[X]	在本文中，我们提出了一种级联方法，该方法使用无监督学习来利用Twitter用户的转发行为来确定其对两极分化话题的态度。 然后，它使用基于用户标签的监督学习来表征在线媒体和受欢迎的Twitter用户的一般政治倾向，以及他们对目标两极分化话题的立场。	构建twitter数据集	Will-They-Won’t-They: A Very Large Dataset for Stance Detection on Twitter	ACL20
18	[X]		自己构造的数据集	Predicting the Topical Stance and Political Leaning of Media using Tweets	ACL20
19	[SCN]	立场并不是互相独立的，需要单独学习每个立场，考虑立场之间的相关性	SemEval taskA	Stance Detection with Stance-Wise Convolution Network	NLPCC20
20	[CKEMN]	引入ConceptNet知识，帮助丰富text和target的上下文	SemEval16 taskA	Commonsense Knowledge Enhanced Memory Network for Stance Classification	IEEE20
21	[PNEM]	使用densely connected Bi-LSTM和 nested LSTMs architectures和注意力机制	SemEval taskA,Multi-target	Tweet Stance Detection Using an Attention based Neural Ensemble Model	NAACL19
22	[stancy]	bert,加入cos约束	Perspectrum dataset	STANCY: Stance Classification Based on Consistency Cues	EMNLP19
23	[MTransSAN]	多任务，Sentiment Analysis,QA,Textual Entailment,Paraphrase Detection。基本思想：在四个任务上用任务语料进行预训练，再用FNC-1语料进行fine-tuning	FNC-1	Neural Multi-Task Learning for Stance Prediction	EMNLP19
24	[X]	-	BBC,ETC(tweet),MFTC(tweet)	Incorporating Label Dependencies in Multilabel Stance Detection	EMNLP19
25	[X]	多任务(待看)	SemEval taskA	Multi-Task Stance Detection with Sentiment and Stance Lexicons	EMNLP19
26	[CLMN]	跨语言	FNC-1	Contrastive Language Adaptation for Cross-Lingual Stance Detection	EMNLP19
27	[X]	-	RumourEval,FNC-1	Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection	ACL19
28	[X]	-	数据集	Data Set for Stance and Sentiment Analysis from User Comments on Croatian News	ACL19
29	[X]	-	Pheme 5 events dataset	Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations	ACL19
30	[X]	-	-	Recognising Agreement and Disagreement between Stances with ReasonComparing Networks	ACL19
31	[AEKFW]	在立场检测模型中加入Wikipedia信息，设定关系为pro，sup，proby，supby	从twitter，wikipedia构建数据集	Stance Detection Attending External Knowledge from Wikipedia	IPSJ19
32	[RCA]	QA类型的立场检测	自己从Baidu Knows，Sogou Wenwen和Mingyi构建数据集	Exploring Answer Stance Detection with Recurrent Conditional Attention	AAAI19
33	[X]	-	-	Modeling Transferable Topics for Cross-Target Stance Detection	SIGIR19
34	[AE-RNN]	在GRU基础上加入tweet用户信息。引入作者的知识改善推文立场分类，由于作者信息嵌入通常不适用于带标签的，所以提出半监督式预训练方法来预测用户嵌入	SemEval taskA，Gun（论文自己构建）	Using Author Embeddings to Improve Tweet Stance Classification	EMNLP18
35	[X]	-	FNC-1	Stance Detection in Fake News: A Combined Feature Representation	EMNLP18
36	[X]	-	FNC-1	Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles	EMNLP18
37	[CrossNet]	跨target	SemEval2016 taskA	Cross-Target Stance Classification with Self-Attention Networks	ACL18
38	[X]	-	-	A Retrospective Analysis of the Fake News Challenge Stance-Detection Task	COLING18
39	[X]	-	-	Structured Representation Learning for Online Debate Stance Prediction	COLING18
40	[X]	-	《Other topics you may also agree or disagree: Modeling inter-topic preferences using tweets and matrix factorization》	Predicting Stances from Social Media Posts using Factorization Machines	COLING18
41	[HAN]	引入Sentiment，Dependency，Argument，用注意力机制共同做立场检测	SemEval16 taskA,H&N14	Stance Detection with Hierarchical Attention Network	COLING18
42	[X]	-	-	ConStance: Modeling Annotation Contexts to Improve Stance Classification	EMNLP17
43	[TAN]	在模型层面注意到target	SemEval16 taskA,NLPCC16 taskA	Stance Classification with Target-Specific Neural Attention Networks	IJCAI17
44	[BiCond]	经典	SemEval taskB	Stance Detection with Bidirectional Conditional Encoding	EMNLP16
45	[X]	-	SemEval taskB	Weakly Supervised Tweet Stance Classification by Relational Bootstrapping	EMNLP16
46	[X]	-	-	Modeling Stance in Student Essays	ACL16
47	[X]	-	-	Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter	ACL16
48	[STS]	不是深度学习方法，概率	SemEval taskA&B	A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets	COLING16
49	[X]	-	PHEME rumour	Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations	COLING16
50	[UTCNN]	-	FBFans(中文),CreateDebate	UTCNN: a Deep Learning Model of Stance Classification on Social Media Text	COLING16
51	[]	-	-	Stance Classification by Recognizing Related Events about Targets	IEEE16
52	[pkudblab]	SemEval 2016 taskA第二名，taskB第一名。利用CNN和投票策略。对五个target单独训练五个模型	SemEval taskA&B	pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection	SemEval16

Cross-Target Stance Detection

1. Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge Bowen Zhang, Min Yang, Xutao Li, Yunming Ye, Xiaofei Xu, Kuai DaiACL20[pdf]
2. Cross-Target Stance Classification with Self-Attention Networks Chang Xu,Cecile Paris,Surya Nepal,Ross Sparks ACL18 [pdf]
3. Modeling Transferable Topics for Cross-Target Stance Detection Penghui Wei,Wenji Mao SIGIR19 [pdf]
4. Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge Bowen Zhang , Min Yang ACL2020 [pdf]

Cross-Lingual

1. Contrastive Language Adaptation for Cross-Lingual Stance Detection Mitra Mohtarami,James Glass,Preslav Nakov EMNLP19 [pdf]

Commonsense Konwledge

1. Commonsense Knowledge Enhanced Memory Network for Stance Classification Jiachen Du,Lin Gui,Ruifeng Xu,Yunqing Xia,Xuan Wang IEEE20[pdf]
2. Stance Detection Attending External Knowledge from Wikipedia Kazuaki Hanawa,Akira Sasaki,Naoaki Okazaki,Kentaro Inui IPSJ19[pdf]

Reference

[summarization.bib](https://github.com/zmsjf/StanceDetection-Papers/blob/main/summrization.bib)