2017/10/01 by Georgi Karadzhov, Preslav Nakov, Karadzhov, Georgi +7
Computer Science · Social Sciences · #68T50 #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #Misinformation and Its Impacts #Software Engineering Research #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1710.00341
openalex publication_date 2017/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.