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 …

The prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of
stance detection data on social media sites, as users turn to social media to share their …

Detecting stance on Twitter is especially challenging because of the short length of each
tweet, the continuous coinage of new terminology and hashtags, and the deviation of …

Users rely heavily on social media to consume and share news, facilitating the mass dis-
semination of genuine and fake stories. The proliferation of misinformation on various social …

Identifying users' stances regarding specific targets/topics is a significant route to learning
public opinion from social media platforms. Most existing studies of stance detection strive to …

We extract a large-scale stance detection dataset from comments written by candidates of
elections in Switzerland. The dataset consists of German, French and Italian text, allowing …

Automated stance detection and related machine learning methods can provide useful
insights for media monitoring and academic research. Many of these approaches require …

The development of social media user stance detection and bot detection methods rely
heavily on large-scale and high-quality benchmarks. However, in addition to low annotation …

Social media platforms are becoming inherent parts of people's daily life to express opinions
and stances toward topics of varying polarities. Stance detection determines the viewpoint …