Feb. 12, 2024, 5:46 a.m. | Recep Firat Cekinel Pinar Karagoz

cs.CL updates on arXiv.org arxiv.org

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 significant progress has been made in this field, it has not yet reached a level of reasoning comparable to human reasoning. To address these gaps, we propose a multi-task explainable neural model for misinformation detection. Specifically, this work formulates an explanation generation process of the model's veracity …

attention automated cs.cl evidence fact-checking importance making media misinformation predictions progress reasoning social social media studies summarization through

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Business Intelligence Analyst Lead

@ Zillow | Mexico City

Lead Data Engineer

@ Bristol Myers Squibb | Hyderabad

Big Data Solutions Architect

@ Databricks | Munich, Germany

Senior Data Scientist - Trendyol Seller

@ Trendyol | Istanbul (All)