A Survey on Automated Fact-Checking
- URL: http://arxiv.org/abs/2108.11896v1
- Date: Thu, 26 Aug 2021 16:34:51 GMT
- Title: A Survey on Automated Fact-Checking
- Authors: Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos
- Abstract summary: We survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines.
We present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts.
- Score: 18.255327608480165
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fact-checking has become increasingly important due to the speed with which
both information and misinformation can spread in the modern media ecosystem.
Therefore, researchers have been exploring how fact-checking can be automated,
using techniques based on natural language processing, machine learning,
knowledge representation, and databases to automatically predict the veracity
of claims. In this paper, we survey automated fact-checking stemming from
natural language processing, and discuss its connections to related tasks and
disciplines. In this process, we present an overview of existing datasets and
models, aiming to unify the various definitions given and identify common
concepts. Finally, we highlight challenges for future research.
Related papers
- Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches [2.0140898354987353]
Automated Fact-Checking (AFC) is the automated verification of claim accuracy.
AFC is crucial in discerning truth from misinformation, especially given the huge amounts of content are generated online daily.
Current research focuses on predicting claim veracity through metadata analysis and language scrutiny.
arXiv Detail & Related papers (2024-07-09T01:54:13Z) - Synthetic Disinformation Attacks on Automated Fact Verification Systems [53.011635547834025]
We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings.
We show that these systems suffer significant performance drops against these attacks.
We discuss the growing threat of modern NLG systems as generators of disinformation.
arXiv Detail & Related papers (2022-02-18T19:01:01Z) - Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or
Something Else? [93.91375268580806]
Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms.
Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications.
By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
arXiv Detail & Related papers (2021-11-09T13:30:34Z) - FacTeR-Check: Semi-automated fact-checking through Semantic Similarity
and Natural Language Inference [61.068947982746224]
FacTeR-Check enables retrieving fact-checked information, unchecked claims verification and tracking dangerous information over social media.
The architecture is validated using a new dataset called NLI19-SP that is publicly released with COVID-19 related hoaxes and tweets from Spanish social media.
Our results show state-of-the-art performance on the individual benchmarks, as well as producing useful analysis of the evolution over time of 61 different hoaxes.
arXiv Detail & Related papers (2021-10-27T15:44:54Z) - Automated Fact-Checking: A Survey [5.729426778193398]
Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets.
This paper reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.
arXiv Detail & Related papers (2021-09-23T15:13:48Z) - Towards Explainable Fact Checking [22.91475787277623]
This thesis presents my research on automatic fact checking.
It includes claim check-worthiness detection, stance detection and veracity prediction.
Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions.
arXiv Detail & Related papers (2021-08-23T16:22:50Z) - FaVIQ: FAct Verification from Information-seeking Questions [77.7067957445298]
We construct a large-scale fact verification dataset called FaVIQ using information-seeking questions posed by real users.
Our claims are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.
arXiv Detail & Related papers (2021-07-05T17:31:44Z) - Knowledge as Invariance -- History and Perspectives of
Knowledge-augmented Machine Learning [69.99522650448213]
Research in machine learning is at a turning point.
Research interests are shifting away from increasing the performance of highly parameterized models to exceedingly specific tasks.
This white paper provides an introduction and discussion of this emerging field in machine learning research.
arXiv Detail & Related papers (2020-12-21T15:07:19Z) - Generating Fact Checking Explanations [52.879658637466605]
A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process.
This paper provides the first study of how these explanations can be generated automatically based on available claim context.
Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.
arXiv Detail & Related papers (2020-04-13T05:23:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.