Výsledky vyhledávání - "KARAGOZ, Pinar"

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Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative LLMs

Autor: Ozturk, Asli Umay, Cekinel, Recep Firat, Karagoz, Pinar

Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection perform

Externí odkaz: http://arxiv.org/abs/2412.09247

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Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies

Autor: Cekinel, Recep Firat, Karagoz, Pinar, Coltekin, Cagri

This study evaluates the effectiveness of Vision Language Models (VLMs) in representing and utilizing multimodal content for fact-checking. To be more specific, we investigate whether incorporating multimodal content improves performance compared to

Externí odkaz: http://arxiv.org/abs/2412.05155

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Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish

Autor: Cekinel, Recep Firat, Karagoz, Pinar, Coltekin, Cagri

The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English

Externí odkaz: http://arxiv.org/abs/2403.00411

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Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach

Autor: Cekinel, Recep Firat, Karagoz, Pinar

The rapid dissemination of misinformation through social media increased the importance of automated fact-checking. Furthermore, studies on what deep neural models pay attention to when making predictions have increased in recent years. While signifi

Externí odkaz: http://arxiv.org/abs/2402.06443

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Event Detection on Twitter

Autor: Sahin, Ozlem Ceren, Tatbul, Nesime, Karagoz, Pinar

Detecting events by using social media has been an active research problem. In this work, we investigate and compare the performance of two methods for event detection in Twitter by using Apache Storm as the stream processing infrastructure. The firs

Externí odkaz: http://arxiv.org/abs/2311.07586

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Investigating the Effect of Segmentation Methods on Neural Model based Sentiment Analysis on Informal Short Texts in Turkish

Autor: Kurt, Fatih, Kisa, Dilek, Karagoz, Pinar

This work investigates segmentation approaches for sentiment analysis on informal short texts in Turkish. The two building blocks of the proposed work are segmentation and deep neural network model. Segmentation focuses on preprocessing of text with

Externí odkaz: http://arxiv.org/abs/1902.06635

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