Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10674
Title: Stance Detection of Turkish Twitter Accounts
Authors: Türkmen, Mehmet Deniz
Doğan, Dilara
Abul, Osman
Kutlu, Mucahid
Keywords: Social media
author profiling
Users
Issue Date: 2023
Publisher: IEEE
Abstract: Twitter offers users an environment where they can express their opinions on any subject. As a result, user activities have become an important resource for various sociological studies. In this study, we proposed machine learning methods that utilize text-based and interaction-based features to predict the opinions of users who post messages in Turkish. We also performed zero-shot classification using ChatGPT. We labeled 4,502 accounts that posted during the Turkey Presidential election in 2018 according to the candidates they supported. In our experiments with the dataset we created, we achieved a classification accuracy of 0.831 for users.
Description: 31st IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUL 05-08, 2023 -- Istanbul Tech Univ, Ayazaga Campus, Istanbul, TURKEY
URI: https://doi.org/10.1109/SIU59756.2023.10223816
https://hdl.handle.net/20.500.11851/10674
ISBN: 979-8-3503-4355-7
ISSN: 2165-0608
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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