A Content Management Perspective on Fact-Checking

Abstract

Fact checking has captured the attention of the media and the public alike; it has also recently received strong attention from the computer science community, in particular from data and knowledge management, natural language processing and information retrieval; we denote these together under the term "content management". In this paper, we identify the fact checking tasks which can be performed with the help of content management technologies, and survey the recent research works in this area, before laying out some perspectives for the future. We hope our work will provide interested researchers, journalists and fact checkers with an entry point in the existing literature as well as help develop a roadmap for future research and development work.

Understanding the Role of Human Values in the Spread of Misinformation

“…One area in which computer science has had a fruitful partnership with human experts is with the process of ranking 'claims' for fact-checking. A claim is a statement that can be extracted from a document and fact-checked (Cazalens et al, 2018). Machine learning algorithms can now identify and sort claims within information, so that they can be prioritised for human review (Wiebe and Riloff, 2005;Cazalens et al, 2018).…”

“…Computer science approaches address a number of tasks that are important for fighting misinformation at scale, such as identifying deep fakes (Yang et al, 2018), analysing provenance and spread patterns (Wu et al, 2016), and assisting humans (e.g. fact-checkers and journalists) with ranking and prioritisation (Wiebe and Riloff, 2005;Cazalens et al, 2018). As a companion to technological and fact-checking approaches, information literacy is promoted as a mitigating force against misinformation and disinformation online (DiFranzo and Gloria-Garcia, 2017).…”

CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media

“…turns its attention to social media -in particular to Twitter -as information posted on that platform is not checked by an authoritative entity before publication and such information tends to disseminate very quickly. Moreover, social media posts lack context due to their short length and conversational nature; thus, identifying a claim's context is sometimes key for enabling effective fact-checking [7].…”

Extracting Statistical Mentions from Textual Claims to Provide Trusted Content

“…With the increase of disinformation in online media, social networks and the Web in general, we witness a strong interest in computational fact-checking, defined as a set of computer-assisted techniques capable of assessing the truthfulness of a given statement [4]. In this context, computational fact-checking is a manystage pipeline, whereas (i) claims are extracted from text, (ii) possible sources of reference are identified, (iii) a check is made combining automated and manual means; (iv) an interpretation is produced.…”

“…We use data from French national institute for statistics and economic studies (INSEE) as an example as highquality, trustful reference database. In previous work, we have extracted tens of thousands of RDF graphs out of INSEE statistic tables [2] 4 . We also developed a novel keyword search algorithm which, given a set of search terms, e.g.…”