Can Machines Learn to Detect Fake News? A Survey Focused on Social Media

Silva, Fernando Cardoso Durier da; Vieira, Rafael José; García, Ana Cristina Bicharra

doi:10.24251/hicss.2019.332

Cited by 50 publications

(12 citation statements)

References 35 publications

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“…One solution can be offered by technology companies (e.g., Twitter, Facebook), using algorithms to filter fake news (Zhou et al, 2019). However, a solution that is solely dependent on technology might not be ideal, because algorithms have their limitations, such as the inability to define intentions, sarcasm, or metaphors (Cardoso Durier da Silva et al, 2019). Moreover, such a solution on the platform-side does not empower people to better recognize and cope with fake news that potentially reaches them through other channels.…”

mentioning

confidence: 99%

Stop and Think! Exploring the Role of News Truth Discernment, Information Literacy, and Impulsivity in the Effect of Critical Thinking Recommendations on Trust in Fake Covid-19 News

Kruijt

Meppelink

Vandeberg³

2022

EJHC

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Covid-19-related fake news widely circulates on social media. This is problematic as people commonly do not process information on social media in a very critical manner. Also, when people encounter particular online content several times this tends to increase the content’s trustworthiness, sometimes irrespective of the accuracy of the provided information. Our study aims to explore whether, how, and for whom a simple critical thinking recommendation added to a social media newsfeed can aid people to better discern true news from fake news and reduce their trust in fake news. In an online experiment, 220 participants were exposed to a Twitter newsfeed with true and fake Covid-19-related news messages, either with or without critical thinking recommendations. The findings showed that participants who were exposed to the recommendations showed less trust in fake news messages, which was mediated by an increased accuracy in news truth discernment. Results showed no significant moderating effects of information literacy and impulsivity characteristics. Overall, the findings of this study are promising as this scalable, low-cost intervention might potentially help combat the effects of fake news on social media.

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mentioning

confidence: 99%

Stop and Think! Exploring the Role of News Truth Discernment, Information Literacy, and Impulsivity in the Effect of Critical Thinking Recommendations on Trust in Fake Covid-19 News

Kruijt

Meppelink

Vandeberg³

2022

EJHC

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“…They claim that fake news detection using composite network analysis combined with current machine-learning techniques can create a new, more generic ways of defining fake news that will enable easier detection by algorithms. Conceptualizing fake news in a way that allows it to be identified in different contexts by algorithmic intelligences "would ease future metamodelling of the entry object and enable better generalistic misinformation detecting agents to be manufactured" (Cardoso Durier da Silva et al 2019Silva et al , 2768.…”

Section: Fake News and Deepfakes In The Digital Agementioning

confidence: 99%

Michael Polányi’s fiduciary program against fake news and deepfake in the digital age

Ziegler

2021

AI & Soc

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This paper argues that Michael Polányi’s account of how science, as an institution, establishes knowledge can provide a structure for a future institution capable of countering misinformation, or fake news, and deepfakes. I argue that only an institutional approach can adequately take up the challenge against the corresponding institution of fake news. The fact of filtering news and information may be bothering. It is the threat of censorship and free speech limitation. Instead, I propose that we should indicate reliable information with a trademark and news signing-approved information and brand equity. I offer a method of creating a standard for online news that people can rely on (similar to high-quality shopping products).

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“…Several datasets and challenges has been released during the last years, for example: The Fake news challenge 7 , FEVER 8 and the clickbait challenge 9 . Those datasets consists of a set of news or piece of text containing claims, and human labels assessing the credibility of such information.…”

Section: Opinionmentioning

confidence: 99%

“…Given the relevance and complexity of this problem, online disinformation has been studied from a wide spectrum of disciplines such as philosophy [23,21,19], communication [45], political [74] and computer science [9,92]. In this work we give an overview of these studies, trying to answer these three questions:…”

Section: Introductionmentioning

confidence: 99%

Online Disinformation and the Role of Wikipedia

Saez-Trumper

2019

Preprint

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The aim of this study is to find key areas of research that can be useful to fight against disinformation on Wikipedia. To address this problem we perform a literature review trying to answer three main questions: (i) What is disinformation? (ii)What are the most popular mechanisms to spread online disinformation? and (iii) Which are the mechanisms that are currently being used to fight against disinformation?. In all these three questions we take first a general approach, considering studies from different areas such as journalism and communications, sociology, philosophy, information and political sciences. And comparing those studies with the current situation on the Wikipedia ecosystem. We found that disinformation can be defined as non-accidentally misleading information that is likely to create false beliefs. While the exact definition of misinformation varies across different authors, they tend to agree that disinformation is different from other types of misinformation, because it requires the intention of deceiving the receiver. A more actionable way to scope disinformation is to define it as a problem of information quality. In Wikipedia quality of information is mainly controlled by the policies of neutral point of view and verifiability. The mechanisms used to spread online disinformation include the coordinated action of online brigades, the usage of bots, and other techniques to create fake content. Underresouced topics and communities are especially vulnerable to such attacks. The usage of sock-puppets is one of the most important problems for Wikipedia. The techniques used to fight against information on the internet, include manual fact checking done by agencies and communities, as well as automatic techniques to assess the quality and credibility of a given information. Machine learning approaches can be fully automatic or can be used as tools by human fact checkers. Wikipedia and especially Wikidata play double role here, because they are used by automatic methods as ground-truth to determine the credibility of an information, and at the same time (and for that reason) they are the target of

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Can Machines Learn to Detect Fake News? A Survey Focused on Social Media

Cited by 50 publications

References 35 publications

Stop and Think! Exploring the Role of News Truth Discernment, Information Literacy, and Impulsivity in the Effect of Critical Thinking Recommendations on Trust in Fake Covid-19 News

Stop and Think! Exploring the Role of News Truth Discernment, Information Literacy, and Impulsivity in the Effect of Critical Thinking Recommendations on Trust in Fake Covid-19 News

Michael Polányi’s fiduciary program against fake news and deepfake in the digital age

Online Disinformation and the Role of Wikipedia

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