False information detection in online content and its role in decision making: a systematic literature review

Habib, Ammara; Asghar, Muhammad Zubair; Khan, Adil; Habib, Anam; Khan, Amin

doi:10.1007/s13278-019-0595-5

Cited by 33 publications

(29 citation statements)

References 36 publications

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“…Intentionally, it is interchangeably used to define as a correct information. In social networks, some unscrupulous people exploit this for their interests by ensuring that the basic patterns of the original information are correct (Kumar and Shah 2018;Habib et al 2019). For example, we generally expect honest users to provide positive reviews to good products and negative reviews to bad products, whereas dishonest users may not follow this behavior.…”

Section: Types Of Misinformationmentioning

confidence: 99%

“…However, the spread of misinformation will be ineffective if people can identify the different types of misbehavior including fake reviews, false information, and rumors. And, identifying misinformation using SN data may provide early feedback on emerging issues, such as stock movement, political gossip, social issues, and business performance (Habib et al 2019). In this regard, various techniques have been applied to differentiate genuine and fraudulent information or users over the past years (Islam et al 2018a;De Choudhury et al 2013a;De Choudhury et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

“…And, identifying misinformation using SN data may provide early feedback on emerging issues, such as stock movement, political gossip, social issues, and business performance (Habib et al. 2019 ). In this regard, various techniques have been applied to differentiate genuine and fraudulent information or users over the past years (Islam et al.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

164

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Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension.

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Section: Types Of Misinformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning for misinformation detection on online social networks: a survey and new perspectives

Islam

Liu

Wang

et al. 2020

Soc. Netw. Anal. Min.

164

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“…Reliability in general, and rumor reliability in particular, is a vast research topic spanning multiple disciplines, and a central concept in theories of decision-making (Savage 1951;Simon 1960;Wedgwood 2002;Habib et al 2019), cooperation (Fehr and Fischbacher 2003), communication (Shannon 1948), viral marketing (Jurvetson 2000; Krider and Weinberg 1998), and markets (Bikhchandani et al 1992). The study of reliability commonly draws on network models, which often use the term reliability for the probability that the proportion of informed individuals exceeds a certain value.…”

Section: Related Workmentioning

confidence: 99%

“…Reliability in general, and rumor reliability in particular, is a vast research topic spanning multiple disciplines, and a central concept in theories of decision-making (Savage 1951 ; Simon 1960 ; Wedgwood 2002 ; Habib et al. 2019 ), cooperation (Fehr and Fischbacher 2003 ), communication (Shannon 1948 ), viral marketing (Jurvetson 2000 ; Krider and Weinberg 1998 ), and markets (Bikhchandani et al. 1992 ).…”

Section: Related Workmentioning

confidence: 99%

Bullshit in a network structure: the two-sided influence of self-generated signals

Tuchner

Gilboa-Freedman

2020

Soc. Netw. Anal. Min.

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In today's social network age, information flowing in networks does not derive solely from external sources; people in the network also independently generate signals. These self-generated signals may not be deliberate lies, but they may not bear any relationship with the truth, either. Following the philosopher Harry G. Frankfurt, we refer to such self-generated signals as bullshit. We present an information diffusion model that allows nodes which hold no value to spread information, capturing the diffusion of bullshit information. The presence of self-generated signals (i.e., bullshit) increases the amount of information available for transmission in the network. However, participants in the spread process respond to the existence of such self-generated information by receiving data from internal sources with caution. These two contradictory forces-the increase in information transmission on the one hand, and in suspicion on the other-result in a two-sided effect of bullshit on the total spread time. We first take a numerical approach, simulating our model on Watts-Strogatz networks and building a decision tree to characterize the effects of bullshit given different network structures. We find that increasing the rate of self-generated information may have either a monotonic or non-monotonic effect on the rumor spread time, depending on the network structure and rate of non-self-generated internal communications. Then, taking an analytical approach, we analyze the spread behavior for cliques, and identify the conditions for monotonic behavior in a 2-clique network.

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Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

Limam

Aqil

Yafooz

et al. 2021

Studies in Computational Intelligence

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This last decade, the amount of data exchanged on the Internet and more specifically on social media networks is growing exponentially. Fake News phenomenon has become a major problem threatening the credibility of these social networks. Machine Learning (ML) techniques represent a promising solution to deal with this issue. For that, several solutions and algorithms using Machine Learning have been proposed in literature in the recent time for detecting fake news generated by different digital media platforms. This chapter aims to conduct a systematic mapping study to analyze and synthesize studies concerning the utilization of machine learning techniques for detecting fake news. Therefore, a total number of 76 relevant papers published on this subject between 1 January 2010 and 30 June 2021 were carefully selected. The selected articles were classified and analyzed according to eight criteria: channel and year of publication, research type, study domain, study platform, study context, study category, feature, and machine learning techniques used to handle categorical data. The results showed that most of the selected papers use both features text/content and linguistic to design machine learning models. Furthermore, SVM technique, and Deep Neural Network (DNN) technique were the most binary classification algorithms used to combat fake news.

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False information detection in online content and its role in decision making: a systematic literature review

Cited by 33 publications

References 36 publications

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Deep learning for misinformation detection on online social networks: a survey and new perspectives

Bullshit in a network structure: the two-sided influence of self-generated signals

Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study

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