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Towards sentiment and Temporal Aided Stance Detection of climate change tweets

  • Published:2023-07 Issue:4 Volume:60 Page:103325
  • ISSN:0306-4573
  • Container-title:Information Processing & Management
  • language:en
  • Short-container-title:Information Processing & Management

Author:

Upadhyaya Apoorva,Fisichella MarcoORCID,Nejdl Wolfgang

Funder

Volkswagen Foundation

Niedersächsisches Ministerium für Wissenschaft und Kultur

Publisher

Elsevier BV

Subject

Library and Information Sciences,Management Science and Operations Research,Computer Science Applications,Media Technology,Information Systems

Reference75 articles.

1. A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments;Al-Ghadir;Information Fusion,2021

2. Assessing sentiment of the expressed stance on social media;Aldayel,2019

3. Stance detection on social media: State of the art and trends;AlDayel;Information Processing & Management,2021

4. Using machine learning to compare provaccine and antivaccine discourse among the public on social media: Algorithm development study;Argyris;JMIR Public Health and Surveillance,2021

5. Public attitudes towards climate change: A cross-country analysis;Arıkan;The British Journal of Politics and International Relations,2021

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1. Incorporating target-aware knowledge into prompt-tuning for few-shot stance detection;Information Processing & Management;2024-09

2. Inferring Climate Change Stances from Multimodal Tweets;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

3. Emotion detection for misinformation: A review;Information Fusion;2024-07

4. Punctuation and lexicon aid representation: A hybrid model for short text sentiment analysis on social media platform;Journal of King Saud University - Computer and Information Sciences;2024-03

5. Unraveling YouTube Stances on Global Warming: An In-Depth Analysis of Skeptics and Believers;Environmental Science and Engineering;2024
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