A survey of social media stance detection using non-textual features
DOI:
https://doi.org/10.5753/jbcs.2026.5687Keywords:
Natural Language Processing, Stance, Social media, Network featuresAbstract
Stance detection is known as the computational task of estimating an individual's attitude towards a given target topic, which is often of a political or moral nature. In traditional NLP fashion, models of this kind have relied mainly on learning features extracted from social media text. However, social media may provide many other types of non-content information in conjunction with text, such as friends networks, interactions with other users, etc. These knowledge sources, despite being potentially useful for stance prediction, remain relatively little discussed in existing surveys of the field. To fill this gap in the literature, this article presents a survey of stance detection research focusing on the use of network-related features and on how these are combined with more standard text models.
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