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Found 8 papers, 3 papers with code
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory
Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians’ nuanced stance on a particular topic.
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs
To study this, we propose the problem of faithful planning in TODs that needs to resolve user intents by following predefined flows and preserving API dependencies.
"A Tale of Two Movements": Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction
We convert the text to a graph by breaking it into structured elements and connect it with the social network of authors, then structured prediction is done over the elements for identifying perspectives.
Conversation Style Transfer using Few-Shot Learning
Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e. g., formality).
Towards Few-Shot Identification of Morality Frames using In-Context Learning
Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge.
Weakly Supervised Learning for Analyzing Political Campaigns on Facebook
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions.
Identifying Morality Frames in Political Tweets using Relational Learning
Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions.
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media
In this paper we suggest a minimally-supervised approach for identifying nuanced frames in news article coverage of politically divisive topics.
