SocialPET: Few-Shot Stance Detection in Social Media

Alapfogalmak

Our work introduces SocialPET, a socially informed approach to few-shot stance detection in social media, outperforming competitive models by leveraging language models and social network structures.

Kivonat

SocialPET is a novel approach that enhances PET for stance detection by incorporating social network information. It shows superior performance, particularly in identifying instances of the 'against' class. The model's effectiveness is demonstrated across different targets and datasets, showcasing its potential for improving few-shot stance detection tasks.
The study addresses the challenges of limited labeled data for new targets in realistic scenarios. By infusing socially informed knowledge into the pattern generation process, SocialPET achieves competitive results compared to baseline models. The analysis highlights the importance of leveraging social network structures to enhance stance detection accuracy.
Key points:

Introduction of SocialPET for few-shot stance detection in social media.
Leveraging language models and social network structures to improve performance.
Competitive results across various targets and datasets.
Importance of incorporating socially informed knowledge for accurate stance detection.

Statisztikák

Our proposed approach builds on the Pattern Exploiting Training (PET) technique.
Outperforms competitive models on two stance datasets, Multi-target and P-Stance.
Achieves superior performance with as few as 100 labeled instances for a target under study.

Idézetek

"Our work advances research in few-shot stance detection by introducing SocialPET."
"We exploit the social network structure surrounding social media posts to enhance our approach."

Főbb Kivonatok

SocialPET

by Parisa Jamad... : arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05216.pdf

Mélyebb kérdések

How can the incorporation of community information from social networks impact other NLP tasks?

Incorporating community information from social networks can have a significant impact on various NLP tasks by providing additional context and insights. For tasks like sentiment analysis, named entity recognition, or even content recommendation, understanding the relationships between users in a social network can help in identifying patterns, trends, and preferences. This information can enhance the accuracy and relevance of predictions made by NLP models. By leveraging community data, NLP tasks can be tailored to specific user groups or communities, leading to more personalized and effective results.

What are potential limitations or biases introduced by relying on social network structures for model enhancement?

While incorporating social network structures into model enhancement has its benefits, there are also potential limitations and biases to consider. One limitation is the issue of homophily bias, where individuals tend to connect with others who share similar characteristics or opinions. This could lead to echo chambers within the data and limit diversity in perspectives captured by the model. Additionally, privacy concerns may arise when using personal data from social networks without explicit consent from users. Biases present in the underlying social network data could also be amplified in the model's predictions if not properly addressed.

How can insights from this study be applied to improve understanding and analysis of user behavior on social media platforms?

Insights from this study provide valuable lessons for improving understanding and analysis of user behavior on social media platforms. By integrating socially informed approaches like SocialPET into existing analytical frameworks, researchers can gain deeper insights into how users interact with content online based on their affiliations within communities. Understanding these dynamics can help identify influential users, detect emerging trends early on, and predict shifts in public opinion more accurately. Furthermore, applying techniques that leverage both text-based content analysis and network structure analysis can offer a holistic view of user behavior patterns on social media platforms.