A comprehensive survey of fake news in social networks: Attributes, features, and detection approaches

Kondamudia, Medeswara Rao; Sahoob, Somya Ranjan; Chouhanc, Lokesh; Yadavd, Nandakishor

doi:10.1016/j.jksuci.2023.101571

Cited by 18 publications

(4 citation statements)

References 167 publications

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“…Additionally, we introduce a quantitative measure known as the percentage overlap (PO) to evaluate a feature's capacity to distinguish between different classes. Denoted as PO (x, y), this metric represents the sum of shared proportions between two classes or categories, as outlined in (3). By combining the IG-based ranking approach with the PO metric, Int J Artif Intell ISSN: 2252-8938  our goal is to select features that significantly contribute to the discrimination between fake accounts and authentic ones.…”

Section: Features Selectionmentioning

confidence: 99%

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Improved performance of fake account classifiers with percentage overlap features selection

Tjahyanto,

Pratama,

Shiddiqi

2024

IJ-AI

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Feature selection plays a crucial role in the development of high-performance classification models. We propose an innovative method for detecting fake accounts. This method leverages the percentage overlap technique to refine feature selection. We introduce our technique upon earlier work that showcased the enhanced efficacy of the Naïve Bayesian classifier through dataset normalization. Our study employs a dataset of account profiles sourced from Twitter, which we normalize using the Min-Max method. We analyze the results through a series of comprehensive experiments involving diverse classification algorithms—such as Naïve Bayes, decision tree, k-nearest neighbors (KNN), deep learning, and support vector machines (SVM). Our experimental results demonstrate a 100% accuracy achieved by the SVM and deep learning classifiers. The results are attributed to the percentage overlap technique, which facilitates the identification of four highly informative features. These findings outperform models with more extensive feature sets, underscoring the efficacy of our approach.

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Section: Features Selectionmentioning

confidence: 99%

“…The increasing number of internet users further highlights the urgency of addressing this issue. Unfortunately, these sources are frequently used to disseminate false information, rumors, politically biased comments, and more, potentially harming society [3]. Fake news creators can be categorized into genuine users and bots.…”

Section: Introductionmentioning

confidence: 99%

Improved performance of fake account classifiers with percentage overlap features selection

Tjahyanto,

Pratama,

Shiddiqi

2024

IJ-AI

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“…Based on data that can be found on social networks, the information is separated into four categories: hyperlinks, images, audio, and text (a subset of spoken language primarily produced with a text or string to examine the content) [11]. OSNs are receiving attention from users who are malicious or abnormal and engage in malicious activities such as harassing others, plotting attacks (in which terrorists may be involved), and disseminating false information [12].…”

Section: Related Workmentioning

confidence: 99%

A Multilayered Preprocessing Approach for Recognition and Classification of Malicious Social Network Messages

Čepulionytė,

Toldinas,

Lozinskis

2023

Electronics

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The primary methods of communication in the modern world are social networks, which are rife with harmful messages that can injure both psychologically and financially. Most websites do not offer services that automatically delete or send malicious communications back to the sender for correction, or notify the sender of inaccuracies in the content of the messages. The deployment of such systems could make use of techniques for identifying and categorizing harmful messages. This paper suggests a novel multilayered preprocessing approach for the recognition and classification of malicious social network messages to limit negative impact, resulting in fewer toxic messages, scams, and aggressive comments in social media messages and commenting areas. As a result, less technical knowledge would be required to investigate the effects of harmful messages. The dataset was created using the regional Lithuanian language with four classes: aggressive, insulting, toxic, and malicious. Three machine learning algorithms were examined, five use cases of a multilayered preprocessing approach were suggested, and experiments were conducted to identify and classify harmful messages in the Lithuanian language.

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“…It is followed by the word "lie", which means something that is not commensurate with the actual facts or circumstances. The spread of false news (hoax) is included in conventional crimes (Kondamudi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Controlling perpetrators of spreading fake news in the Southern Sumatra regional police

Fitrizal Auli,

Sobri,

Sriati

et al. 2024

Indonesian Journal of Communications Studies

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The continuous development of information technology brings significant changes in various fields of community life. Society and the internet have a very complex and significant relationship, especially in terms of using the internet as a means of socialisation and interaction. In the context of social media abuse, people are often trapped in complex issues that lead to hoaxes and hate speech. This study examines policies, control measures, and punishment for hoax spreaders in the South Sumatra Regional Police jurisdiction. This research employs qualitative research using empirical studies and a legal, sociological framework. This research aims to contribute to the corpus of literature and information regarding the perpetrators of spreading false news (hoax) at the South Sumatra Regional Police. The findings of this study reveal that criminal law enforcement against fake news spreaders (hoaxes) in the South Sumatra Regional Police have procedures or efforts to resolve cases of fake news (hoax); the procedure includes receiving reports/complaints, investigations, investigations, sending SPDP (Notice of Commencement of Investigation), sending case files, and transferring suspects and evidence. Law enforcement entities involved in combating fake news are the Police, Prosecutors, and Judges. This research provides the latest data in 2022 regarding cases of spreading fake news (Hoax) in the South Sumatra Regional Police.

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A comprehensive survey of fake news in social networks: Attributes, features, and detection approaches

Cited by 18 publications

References 167 publications

Improved performance of fake account classifiers with percentage overlap features selection

Improved performance of fake account classifiers with percentage overlap features selection

A Multilayered Preprocessing Approach for Recognition and Classification of Malicious Social Network Messages

Controlling perpetrators of spreading fake news in the Southern Sumatra regional police

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