Veracity assessment of online data
作者：
Highlights：
• Three main veracity assessment research directions found, i.e., utilizing implicit features, employing explicit fact checking, and the appeal to authority method.
• The studied papers in general tend to be narrow as they focus on solving a small task with only one type of data from one main source.
• The most common approach to veracity assessment is to perform text analysis using supervised learning.
• Important identified research gaps include reproducibility challenges, low use of recent advancements made in machine learning, and a lack of efforts targeting online data streams.
• The veracity assessment domain is still relatively immature.
摘要
Fake news, malicious rumors, fabricated reviews, generated images and videos, are today spread at an unprecedented rate, making the task of manually assessing data veracity for decision-making purposes a daunting task. Hence, it is urgent to explore possibilities to perform automatic veracity assessment. In this work we review the literature in search for methods and techniques representing state of the art with regard to computerized veracity assessment. We study what others have done within the area of veracity assessment, especially targeted towards social media and open source data, to understand research trends and determine needs for future research.
论文关键词：Veracity assessment,Credibility,Data quality,Online data,Social media,Fake news
论文评审过程：Received 6 May 2019, Revised 16 July 2019, Accepted 16 August 2019, Available online 18 October 2019, Version of Record 3 January 2020.
论文官网地址：https://doi.org/10.1016/j.dss.2019.113132
