Automatic Cross-Target Stance Detection With Fine-Tuned BERT (PDF)

Master's Thesis from the year 2023 in the subject Computer Sciences - Computational linguistics, grade: 1,3, University of Trier (Computerlinguistik und Digital Humanities), language: English, abstract: In 2022, various socio-political debates regarding the Russian-Ukrainian war took place between German-speaking users on Twitter. Due to the large amount of daily user- generated tweets, the main goal of this master thesis is the development of an automatic cross-target stance detection model to enable an examination of German Twitter data on the Russian-Ukrainian conflict 2022.

In the scope of this thesis, the BERT model is used and trained jointly on multiple-related targets of interest by encoding both tweet and target. Within this work, an auto-labeled dataset, a small manual-labeled test set and an unlabeled dataset with German tweets on four targets of interest are presented. A number of experiments with different BERT models are conducted studying cross-target generalization as well as the influence of class balance and case sensitivity. The best performing fine-tuned model is applied for automatic stance prediction on 2022 Twitter data. The stance prediction results are examined to detect potential reasons within a stance category.

The results of this work show that with the applied cross-target approach reasonable performance on known targets can be achieved, but does not suffice for a successful cross-target transfer on unknown targets. In addition, it is observed that a balanced class distribution can counteract a bias towards an overrepresented class and results suggest that case-sensitivity is detrimental in stance detection. The classified data show a number of potential reasons for a favorable and opposing stance towards a respective target within the Russo-Ukrainian conflict. Overall, the stance prediction results show that in 2022 there were consistently more German-speaking Twitter users in favor of supporting Ukraine in the conflict than those opposed to it.