Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning

Core Concepts

A novel collaborative stance detection framework (CoSD) that leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection.

Abstract

The paper presents a novel collaborative stance detection framework called CoSD that leverages contrastive heterogeneous topic graph learning to enhance stance detection.
Key highlights:

CoSD constructs a heterogeneous graph to structurally organize texts, targets, and stances through implicit topics extracted via latent Dirichlet allocation (LDA).
It performs contrastive graph learning to learn heterogeneous node representations, aggregating informative multi-hop collaborative signals via a Collaboration Propagation Aggregation (CPA) module.
During inference, CoSD introduces a hybrid similarity scoring module that integrates BERT and CPA to comprehensively incorporate topic-aware semantics and collaborative signals for stance detection.
Extensive experiments on two benchmark datasets demonstrate the state-of-the-art performance of CoSD, verifying the effectiveness and explainability of the collaborative framework.

Stats

The SemEval-2016 dataset consists of 4,163 English tweets on 5 different targets.
The UKP dataset consists of 25,492 argument sentences across 8 different topics.

Quotes

"Stance detection seeks to identify the viewpoints of individuals either in favor or against a given target or a controversial topic."
"When expressing their opinions, users often highlight their attitudes towards a specific topic or aspect of the target, while intentionally concealing or downplaying other perspectives or aspects that may be irrelevant."

Key Insights Distilled From

CoSD: Collaborative Stance Detection with Contrastive Heterogeneous Topic Graph Learning

by Yinghan Chen... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17609.pdf

Deeper Inquiries

How can the collaborative learning approach in CoSD be extended to other text classification tasks beyond stance detection

The collaborative learning approach in CoSD can be extended to other text classification tasks by leveraging the concept of collaborative signals among different elements in the data. This approach can be applied to tasks such as sentiment analysis, fake news detection, sarcasm detection, and more. By constructing a heterogeneous graph that captures relationships between texts, topics, and labels, the model can learn from the collaborative signals present in the data to enhance classification accuracy. Additionally, the Collaboration Propagation Aggregation (CPA) module can be adapted to capture informative multi-hop collaborative signals in various text classification tasks, improving the overall performance and explainability of the models.

What are the potential limitations of the heterogeneous topic graph construction and how can it be further improved

One potential limitation of the heterogeneous topic graph construction in CoSD is the reliance on latent Dirichlet allocation (LDA) for generating implicit topics. While LDA is a powerful tool for topic modeling, it may not always capture the full complexity and nuances of the underlying topics in the data. To improve this aspect, the model could benefit from incorporating more advanced topic modeling techniques or exploring alternative methods for topic extraction. Additionally, the construction of the graph could be further optimized by considering different weighting schemes for the edges between nodes, incorporating more sophisticated graph structures, or integrating additional features to enhance the representation of texts, topics, and labels within the graph.

How can the proposed framework be adapted to handle dynamic and evolving topics and stances over time

To adapt the proposed framework in CoSD to handle dynamic and evolving topics and stances over time, the model can be modified to incorporate mechanisms for continuous learning and adaptation. This can be achieved by implementing techniques such as online learning, where the model is updated incrementally as new data becomes available. Additionally, the model can be enhanced with mechanisms for topic drift detection and adaptation, allowing it to adjust to changes in topics and stances over time. By incorporating dynamic topic modeling approaches and real-time data processing capabilities, the framework can effectively handle evolving topics and stances in a dynamic environment.