Stance detection on social media is an emerging opinion mining paradigm for various social
and political applications in which sentiment analysis may be sub-optimal. There has been a …

Stance detection is an evolving opinion mining research area motivated by the vast increase
in the variety and volume of user-generated content. In this regard, considerable research …

Word embeddings provide quantitative representations of word semantics and the
associations between word meanings in text data, including in large repositories in media …

Text classification in natural language processing (NLP) is evolving rapidly, particularly with
the surge in transformer-based models, including large language models (LLM). This paper …

Expressed opinions on social media frequently cause a controversy. Controversial content
refers to content that attracts different opinions and interrogations, implying interaction …

As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a
computational way to examine political polarization. We conducted a methodological …

Influencers are key to the nature and networks of information propagation on social media.
Influencers are particularly important in political discourse through their engagement with …

This paper develops a novel unsupervised algorithm for belief representation learning in
polarized networks that (i) uncovers the latent dimensions of the underlying belief space and …

Stance detection, which focuses on users' deep attitudes, is an important way to understand
the online public opinion. This paper presents an overview of stance detection. First, we …

As the impact of social media gradually escalates, people are more likely to be exposed to
indistinguishable fake news. Therefore, numerous studies have attempted to detect rumors …