This article presents the latest developments to ClaimBuster’s claim-spotting model, which tackles the critical task of identifying check-worthy claims from large streams of information. We introduce the first adversarially regularized, transformer-based claim-spotting model, which achieves state-of-the-art results on several benchmark datasets. In addition to analyzing model performance metrics, we also quantitatively and qualitatively analyze the impact of ClaimBuster’s real-world deployment. Moreover, to help facilitate reproducibility and community engagement, we publicly release our codebase, dataset, data curation platform, API, Google Colab notebooks, and various ClaimBuster-based demo systems, at claimbuster.org .

In this paper, we introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. Modeling is a robust tool that can be leveraged in many different tasks such as matching claims to existing fact-checks and translating claims to structured queries. Our work introduces 11 new manually crafted frames along with 9 existing FrameNet frames, all of which have been selected with fact-checking in mind. Along with these frames, we are also providing 2,540 fully annotated sentences, which can be used to understand how these frames are intended to work and to train machine learning models. Finally, we are also releasing our annotation tool to facilitate other researchers to make their own local extensions to FrameNet.

This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses. The claim spotting model is built using a human-labeled dataset of check-worthy factual claims from the U.S. general election debate transcripts. The paper explains the architecture and the components of the system and the evaluation of the model. It presents a case study of how ClaimBuster live covers the 2016 U.S. presidential election debates and monitors social media and Australian Hansard for factual claims. It also describes the current status and the long-term goals of ClaimBuster as we keep developing and expanding it.

Our society is struggling with an unprecedented amount of falsehoods, hyperboles, and half-truths. Politicians and organizations repeatedly make the same false claims. Fake news floods the cyberspace and even allegedly influenced the 2016 election. In fighting false information, the number of active fact-checking organizations has grown from 44 in 2014 to 114 in early 2017. 1 Fact-checkers vet claims by investigating relevant data and documents and publish their verdicts. For instance, PolitiFact.com, one of the earliest and most popular fact-checking projects, gives factual claims truthfulness ratings such as True, Mostly True, Half true, Mostly False, False, and even "Pants on Fire". In the U.S., the election year made fact-checking a part of household terminology. For example, during the first presidential debate on September 26, 2016, NPR.org's live fact-checking website drew 7.4 million page views and delivered its biggest traffic day ever.

In this paper, we discuss the application of the two-dimensional paired representation for processing medical images. This representation leads to the effective solution of the discrete as well as continuous model of image reconstruction from their projections, and to the image enhancement. These two applications can be combined in order to receive high quality images. The method of paired representation of two-dimensional (2D) images is considered with respect to the 2D discrete Fourier transform (DFT). Basis functions of the paired transformation are defined completely by parallel projections. At the same time, the paired representation describes the image as a set of short 1D real signals (splitting-signals) which completely determine the 2D DFT of the image at disjoint subsets of frequency-points. The image enhancement procedure is thus can be reduced to processing splitting-signals and such process requires only a few spectral components of the image. For instance, the traditional alpha-rooting method of image enhancement can be fulfilled through processing the splitting-signals defined for only 3N - 2 frequency-points, when the image has the size N times N, and N is a power of two. It is shown, that processing one or a few splitting signals leads to a high image enhancement.

In the tensor representation, a two-dimensional (2-D) image is represented uniquely by a set of one-dimensional (1-D) signals, so-called splitting-signals, that carry the spectral information of the image at frequency-points of specific sets that cover the whole domain of frequencies. The image enhancement is thus reduced to processing splitting-signals and such process requires a modification of only a few spectral components of the image, for each signal. For instance, the alpha-rooting method of image enhancement can be fulfilled through processing separately a maximum of 3N/2 splitting-signals of an image (N x N), where N is a power of two. In this paper, we propose a fast implementation of the a-rooting method by using one splitting-signal of the tensor representation with respect to the discrete Fourier transform (DFT). The implementation is described in the frequency and spatial domains. As a result, the proposed algorithms for image enhancement use two 1-D N-point DFTs instead of two 2-D N x N-point DFTs in the traditional method of alpha-rooting.

The method of tensor representation of an image with respect to the Fourier transform and its application for image enhancement is described. The method is based on the fact that a two-dimensional (2D) image can be represented by a set of 1D signals that split the 2D Fourier transform of the image into different groups of frequencies. Each splitting-signal carries information of the spectrum in a specific group. The processing of the image is reduced to processing splitting-signals. The effectiveness of such an approach is illustrated through processing the image by the /spl alpha/-rooting method of enhancement. We propose to enhance the image by processing only one or a few splitting-signals, to achieve image enhancement which, in many cases, can exceed enhancement by the /spl alpha/-rooting method. The selection of such splitting-signals is described.