Knowledge Enhanced Target-Aware Stance Detection on Tweets

Zhang, Xin; Yuan, Jianhua; Zhao, Yanyan; Qin, Bing

doi:10.1007/978-981-16-6471-7_13

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…• CKEMN ( [4]): A commonsense knowledge enhanced memory network for stance detection using LSTM as embedding. • RelNet ( [46]): A multiple knowledge enhanced framework for stance detection using BERT. • BERT 𝑆𝐸𝑃 ( [2]): A pre-trained language model that predicted the stance by appending a linear classification layer to the hidden representation of [CLS] token.…”

Section: Baseline Methodsmentioning

confidence: 99%

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Few-Shot Stance Detection via Target-Aware Prompt Distillation

Yan

Gao

Shen

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Stance detection aims to identify whether the author of a text is in favor of, against, or neutral to a given target. The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets. Existing works mainly focus on solving the second issue by designing attention-based models or introducing noisy external knowledge, while the first issue remains under-explored. In this paper, inspired by the potential capability of pre-trained language models (PLMs) serving as knowledge bases and few-shot learners, we propose to introduce prompt-based fine-tuning for stance detection. PLMs can provide essential contextual information for the targets and enable few-shot learning via prompts. Considering the crucial role of the target in stance detection task, we design targetaware prompts and propose a novel verbalizer. Instead of mapping each label to a concrete word, our verbalizer maps each label to a vector and picks the label that best captures the correlation between the stance and the target. Moreover, to alleviate the possible defect of dealing with varying targets with a single hand-crafted prompt, we propose to distill the information learned from multiple prompts. Experimental results show the superior performance of our proposed model in both full-data and few-shot scenarios.

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Section: Baseline Methodsmentioning

confidence: 99%

“…Liu et al [19] introduced a commonsense knowledge enhanced model to exploit both the structural-level and semantic-level information of the relational knowledge. Besides, Zhang et al [46] leveraged multiple external knowledge bases as bridges to explicitly link potentially opinioned terms in texts to targets of interest.…”

Section: Stance Detectionmentioning

confidence: 99%

Few-Shot Stance Detection via Target-Aware Prompt Distillation

Yan

Gao

Shen

et al. 2022

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

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show abstract

“…[31]- [33] built a political knowledge graph to infer political polarities in news media, but they cannot infer topic-specific stances. [34], [35], [37] used a commonsense knowledge graph built from ConceptNet or WikiData to infer the topic-specific stances on Tweets. Jiang et al [38] used a prompt-based method to distill knowledge from pretrained LM and to infer the topic-specific stances on text.…”

Section: B Graph-based Workmentioning

confidence: 99%

Topic-Specific Political Stance Inference in Social Networks With Case Studies

Wu,

Zhou,

et al. 2024

IEEE Access

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Topic-specific political stance inference in social networks (SNs) aims at inferring target users' attitudes toward different target topics. Traditional methods mainly used a language model to classify sentiments from the postings of the SN users. However, people's stances are not always equal to their sentiments. Some others tried to build separate models toward different target topics. In many cases, though SN users talked about the target topics, the information given was limited; or they only expressed attitudes toward some other issues except the target topics. When information is incomplete, the methods that treat the topics independently fail to work, let alone for users who didn't post any of the topics. To solve the above problems, we introduced a political knowledge graph (PKG) to supplement side information for users and topics and proposed a united Knowledge Graph-aware and Social Network-enhanced framework (KGSN) to capture not only the knowledge connections between topics but also the social connections between users. KGSN utilized two levels of graph convolutional networks, the one at the knowledge graph level generating knowledge-aware representations merging knowledge entities for the users and topics respectively, and the one at the social graph level generating social-enhanced representations merging social neighbors for the target users. Beyond that, the respective topic-specific attention mechanisms were leveraged to emphasize special knowledge entities in the knowledge graph and special neighboring users in the social graph. The advantages of KGSN are that: first, it can infer users' attitudes toward more than one topic in one model; second, it can infer users' implicit attitudes toward the target topics through users' explicit attitudes toward the other issues; last but not least, even for users without any postings, KGSN can infer users' implicit attitudes through their social neighbors. Finally, extensive experiments were conducted to demonstrate the superiority of KGSN over state-of-the-art models and case studies were investigated to testify the effectiveness of the model.

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“…Furthermore, Li et al [6] proposed the BERTtoCNN model combining the classic distillation loss with similaritypreserving loss to improve the performance of stance detection. In order to enrich semantics of the user text, some work tried to introduce external knowledge [5], [17], [18]. Zhang et al [5] used external knowledge such as semantic and emotion lexicons to supplement the target information, and put forward a knowledge transfer model that can be used for cross-domain stance detection.…”

Section: Stance Detectionmentioning

confidence: 99%

Heterogeneous Graph Contrastive Learning for Stance Prediction

Yang

2022

IEICE Trans. Inf. & Syst.

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Stance prediction on social media aims to infer the stances of users towards a specific topic or event, which are not expressed explicitly. It is of great significance for public opinion analysis to extract and determine users' stances using user-generated content on social media. Existing research makes use of various signals, ranging from text content to online network connections of users on these platforms. However, it lacks joint modeling of the heterogeneous information for stance prediction. In this paper, we propose a self-supervised heterogeneous graph contrastive learning framework for stance prediction in online debate forums. Firstly, we perform data augmentation on the original heterogeneous information network to generate an augmented view. The original view and augmented view are learned from a meta-path based graph encoder respectively. Then, the contrastive learning among the two views is conducted to obtain highquality representations of users and issues. Finally, the stance prediction is accomplished by matrix factorization between users and issues. The experimental results on an online debate forum dataset show that our model outperforms other competitive baseline methods significantly.

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Knowledge Enhanced Target-Aware Stance Detection on Tweets

Cited by 4 publications

References 17 publications

Few-Shot Stance Detection via Target-Aware Prompt Distillation

Few-Shot Stance Detection via Target-Aware Prompt Distillation

Topic-Specific Political Stance Inference in Social Networks With Case Studies

Heterogeneous Graph Contrastive Learning for Stance Prediction

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