[EMNLP 2024 Workshop NLP4PI]🌏 MultiClimate: Multimodal Stance Detection on Climate Change Videos 🌎

Fact-checking pipeline for climate change-related claims.

Example application for applying QLoRA-based Parameter-Efficient Fine-Tuning (PEFT) to a Stance Detection task using Gemma-2-9B-Instruct

Example application for applying QLoRA-based Parameter-Efficient Fine-Tuning (PEFT) to a sentence pair classification task using Mistral-7B and Llama3-8B

Improved stance prediction model

Towards a Programmable Humanizing AI through Scalable Stance-Directed Architecture

The code and data for the Paper 'Inferring Climate Change Stances from Multimodal Tweets' accepted by the Short Paper track of SIGIR 2024

Bachelor Thesis (HSE FCS AMI) "Analysis of Recent Topics in Social Networks Using Natural Language Processing"

SP24_PROJECT

This repository represents the attempt for the Touché Task 3: Image Retrival for Arguments by the group Aramis (Copy of the original repository)

A curated list of awesome sentiment analysis studies, in which attitude corresponds to the text position conveyed by Subject towards other Object mentioned in text such as: entities, events, etc.

Exploring Public Opinion Dynamics From 2020 U.S. Election Tweets, CSC440 Fall 2023

Identify the freedom of a local news outlet by comparing sentiment and stance of published news against international outlets.

Code for "Using Masked Language Model Probabilities of Connectives for Stance Detection in English Discourse"

The page lists recent research developments in the area of Stance Learning.

Implementation of the Kurybayashi et al. text span representation method using LLMs

Initial testing repository for the article hosted on UNic-AILab.github.io

Multi-dataset stance detection and robustness experiments

This repository contains the annotated dataset referenced in the paper "Unveiling Vaccine Hesitancy on Twitter: Analyzing Trends and Reasons during the Emergence of COVID-19 Delta and Omicron Variants".

Project for Human Language Technologies course, at the University of Pisa. Comparison of different classifier models for a stance detection task.