Fake News Detection Techniques on Social Media: A Survey

Ali, Ihsan; Ayub, Mohamad Nizam Bin; Shivakumara, Palaiahnakote; Noor, Nurul Fazmidar Mohd

doi:10.1155/2022/6072084

Cited by 17 publications

(5 citation statements)

References 60 publications

(126 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A systematic literature review on spam content detection and classification 22 [73] How do Canadian public health agencies respond to the COVID-19 emergency using social media: A protocol for a case study using content and sentiment analysis 23 [74] A retrospective analysis of social media posts pertaining to COVID-19 vaccination side effects [116] Fake News Detection Techniques on Social Media: A Survey 66 [117] Inclusive Study of Fake News Detection for COVID-19 with New Dataset using Supervised Learning Algorithms 67 [118] On Politics and Pandemic: How Do Chilean Media Talk about Disinformation and Fake News in Their Social Networks?…”

Section: [72]mentioning

confidence: 99%

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

Tomassi,

Falegnami,

Romano

2024

PLoS ONE

View full text Add to dashboard Cite

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

show abstract

Section: [72]mentioning

confidence: 99%

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

Tomassi,

Falegnami,

Romano

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…These technologies can be classified based on their purpose: Content-Based, Social context-based, Temporal-Based, and Credibility-Based [Ali et al 2022. Content-based technologies assess the text and images, either with or without consulting external knowledge.…”

Section: Short Background and Related Workmentioning

confidence: 99%

“…In-social context techniques, the environment reaction, such as comments, replies, and reposts, makes a network architecture which can be analyzed, such as blockchain and graphs. The temporal considerate that rumors resurface, and credibility based analyzed the credibility of the news source [Ali et al 2022.…”

Section: Short Background and Related Workmentioning

confidence: 99%

A Rapid Tertiary Review at the Fake News Domain

Gomes,

Neto,

Barbosa

et al. 2023

Anais Da XI Escola Regional De Informática De Goiás (ERI-GO 2023)

View full text Add to dashboard Cite

Context: The spread of fake news on social media platforms has emerged as a pressing concern in recent years. Between 2018 and 2023, numerous secondary studies (SS) have explored this issue, employing diverse methodologies and approaches. Surprisingly, no tertiary study exists to summarize the state of the research. Objective: The aim of this paper is to provide a rapid overview of the SS on fake news research topics for researchers and practitioners. Method: We defined and conducted a rapid tertiary review to find SS published from 2013 to August 2023. 50 most relevant studies in a Google Scholar search were retrieved, from which 15 secondary studies were included and analyzed. Results: A diversity of definitions for fake news exist, often associated with the technology and content in which they are being analyzed. Various stages of fake news processing are covered in the literature. A predominance in the use of deep learning (DL) was observed and challenges still remain, including the urgent need for real-time learning and early detection of fake news.

show abstract

“…Fake news or false stories are circulated online or through social media [12] or other media in a way that appears to be legitimate [13] and presents real information in a fake context to support untrue assertions [14] or mislead the audience [15]. Identifying fake news on social media sites can be challenging due to the abundance of information [16].…”

Section: Introductionmentioning

confidence: 99%

Text Mining for News Forecasting on The Turnback Hoax Website

Wirawan,

Krisnanik,

Arista

2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

News has been disseminated swiftly via the internet due to the rapid growth of information technology. The rapid spreading of news often confuses because the truth cannot be ascertained. Additionally, online social media is becoming increasingly popular, making it an excellent environment for propagating false information, including misinformation, phony reviews, advertising, rumors, political remarks, innuendo, etc. This study's specific goal is to classify data using a data mining approach model called text mining so that a system can automatically do the classification. As a result, the study will produce a dataset, which can then be used to create an application using data mining's ability to predict breaking news. An application was produced by employing data mining to forecast recent news. This study was able to classify data using a naive Bayes data mining approach model so that a system can automatically do the classification. The study produced an accuracy of 77% obtained with training data of 82%. From 994 contents, misleading reached 33.9%, false reached 24.85%, imitation reached 13.48%, fake reached 11.07%, manipulated was 9.86%, parody was 3.22%, satire was 2.31%, and connection content as many as 1.31%. This study then visualizes the results using bar charts and word clouds. This work also produced datasets with the naïve Bayes method of news data and news that has been valid. Afterward, the dataset will be used in making applications to produce prototypes of computer program applications.

show abstract

Fake News Detection Techniques on Social Media: A Survey

Cited by 17 publications

References 60 publications

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

A Rapid Tertiary Review at the Fake News Domain

Text Mining for News Forecasting on The Turnback Hoax Website

Contact Info

Product

Resources

About