Identifying Fake Experts A Conceptual Framework And Case Study Of

Bonisiwe Shabane

-Dec 5, 2025, 4:36 PM

identifying fake experts a conceptual framework and case study of

Corresponding author. p105401@siswa.ukm.edu.my Received 2023 Jun 13; Revised 2023 Sep 13; Accepted 2023 Sep 20; Collection date 2023 Oct. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Currently, social networks have become the main source to acquire news about current global affairs. However, fake news appears and spreads on social media daily.

This disinformation has a negative influence on several domains, such as politics, the economy, and health. In addition, it further generates detriments to societal stability. Several studies have provided effective models for detecting fake news in social networks through a variety of methods; however, there are limitations. Furthermore, since it is a critical field, the accuracy of the detection models was found to be notably insufficient. Although many review articles have addressed the repercussions of fake news, most have focused on specific and recurring aspects of fake news detection models. For example, the majority of reviews have primarily focused on dividing datasets, features, and classifiers used in this field by type.

The limitations of the datasets, their features, how these features are fused, and the impact of all these factors on detection models were not investigated, especially since most detection models were based on a... This review article analyzes relevant studies for the few last years and highlights the challenges faced by fake news detection models and their impact on their performance. The investigation of fake news detection studies relied on the following aspects and their impact on detection accuracy, namely datasets, overfitting/underfitting, image-based features, feature vector representation, machine learning models, and data fusion. Based on the analysis of relevant studies, the review showed that these issues significantly affect the performance and accuracy of detection models. This review aims to provide room for other researchers in the future to improve fake news detection models. Keywords: Fake news detection, Dataset, Overfitting/underfitting, Image feature, Feature vector, Data fusion

Harris, S.; Hadi, H.J.; Ahmad, N.; Alshara, M.A. Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas. Technologies 2024, 12, 222. https://doi.org/10.3390/technologies12110222 Harris S, Hadi HJ, Ahmad N, Alshara MA. Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas.

Technologies. 2024; 12(11):222. https://doi.org/10.3390/technologies12110222 Harris, Sheetal, Hassan Jalil Hadi, Naveed Ahmad, and Mohammed Ali Alshara. 2024. "Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas" Technologies 12, no.

11: 222. https://doi.org/10.3390/technologies12110222 Harris, S., Hadi, H. J., Ahmad, N., & Alshara, M. A. (2024).

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas. Technologies, 12(11), 222. https://doi.org/10.3390/technologies12110222 Humanities and Social Sciences Communications volume 12, Article number: 1040 (2025) Cite this article The past decade has seen a rapid and vast adoption of social media globally and over sixty percent of people were connected online through various social media platforms as of the start of 2024. Despite many advantages social media offers, one of the most significant challenges is the rapid rise of fake news and AI-generated deepfakes across these social networks.

The spread of fake news and deepfakes can lead to a series of negative impacts, such as social trust, economic consequences, public health and safety crises, as demonstrated during the COVID-19 pandemic. Hence, it is more important now than ever to develop solutions to identify such fake news and deepfakes, and curb their spread. This paper begins with a review of the literature on the definitions of fake news and deepfakes, their different types and major differences, and the ways they spread. Building on this literature research, this paper aims to analyse how fake news can be identified using machine learning models, and understand how data analytics can be leveraged to evaluate the impact of such... A fake news detection framework is developed, where TF-IDF vectorization and bag of n-grams methods are implemented to extract text features, and six typical machine learning models are used to detect fake news, with... Additionally, a convolutional neural network model is designed to detect deepfake images with two distinct architectures, namely, ResNet50 and DenseNet121.

To analyse the emotional impact of fake news on public behaviour and trust, a trained natural language toolkit called VADER lexicon is used to assign sentiment polarity and emotion strength to articles. The rampant rise of deepfake technology poses huge risks to social trust and privacy issues, which impacts both individuals and society at large, and leveraging the effective use of data analytics, machine learning and... Finally, the paper discusses some practical solutions to mitigate the negative impacts of fake news and deepfakes. Humans are inherently poor at differentiating between fact and fiction, especially when inundated with information from various sources. This notion has been confirmed by various previous research (Egelhofer and Lecheler, 2019; Rubin, 2010). The scenario worsens if the fiction is desirable to the public (desirability bias, Fisher, 1993) or if it endorses their pre-existing opinions (confirmation bias, Nickerson, 1998).

The word ‘post-truth’ defines this behaviour succinctly, where personal beliefs and public emotions are far more influential than objective facts. In fact, the word ‘post-truth’ was used frequently in articles and social media, especially during events like the Brexit referendum in the United Kingdom and during the 2016 presidential elections in the United States. Due to its frequent usage, the word was named by Oxford Dictionaries in 2016 as its word of the year (Wang, 2016). The above points highlight just one of the many reasons why fake news is very quick to spread and hard to detect. Unlike traditional news outlets, social media has low barriers to entry, making it an ideal platform for rapidly sharing false information. This is further amplified by the echo chamber effect (Jamieson and Cappella, 2008), where like-minded users reinforce each other’s beliefs, increasing the reach of misinformation.

In addition, the economic benefits, such as from social media advertisements, and political benefits for big businesses and opposition parties also provide a strong motive to create, distribute, and spread fake news. However, the public today is more aware of the negative impacts of fake news than ever before, and there is a growing recognition of the need to take extra measures to verify the authenticity... This paper aims to investigate how data analytics and machine learning (ML) techniques can be leveraged to detect fake news and deepfakes accurately and the key limitations of such solutions. To achieve this aim, it is important to first define what constitutes fake news and deepfakes and review the relevant research surrounding the different types of fake news and deepfakes. This paper also explores ways of evaluating the impact of such fake news on public behaviour and trust using natural language processing (NLP) and sentiment analysis, especially during crises such as the COVID-19 pandemic... Specifically, this work aims to explore and address the following formulated research questions,

What data analytics and machine learning techniques can be used to detect fake news and deepfake images, and how effective are these techniques in terms of accuracy and other performance metrics? Edited by: Taner Edis, Truman State University, United States Reviewed by: Frank Zenker, Boğaziçi University, Turkey; Ohan Homins, Boğaziçi University, Turkey, in collaboration with reviewer FZ; Andrea Lavazza, Centro Universitario Internazionale, Italy *Correspondence: Sebastian Dieguez, sebastian.dieguez@unifr.ch This article was submitted to Theoretical and Philosophical Psychology, a section of the journal Frontiers in Psychology Received 2021 Jun 29; Accepted 2021 Oct 19; Collection date 2021.

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. With the widespread dissemination of information via digital media platforms, it is of utmost importance for individuals and societies to be able to judge the credibility of it. Fake news is not a recent concept, but it is a commonly occurring phenomenon in current times. The consequence of fake news can range from being merely annoying to influencing and misleading societies or even nations. A variety of approaches exist to identify fake news.

By conducting a systematic literature review, we identify the main approaches currently available to identify fake news and how these approaches can be applied in different situations. Some approaches are illustrated with a relevant example as well as the challenges and the appropriate context in which the specific approach can be used. Keywords: Fake news, Machine learning, Linguistics, Semantics, Syntax, Algorithms, Digital tools, Social media Paskin (2018: 254) defines fake news as “particular news articles that originate either on mainstream media (online or offline) or social media and have no factual basis, but are presented as facts and not... The importance of combatting fake news is starkly illustrated during the current COVID-19 pandemic. Social networks are stepping up in using digital fake news detection tools and educating the public towards spotting fake news.

At the time of writing, Facebook uses machine learning algorithms to identify false or sensational claims used in advertising for alternative cures, they place potential fake news articles lower in the news feed, and... Twitter ensures that searches on the virus result in credible articles and Instagram redirects anyone searching for information on the virus to a special message with credible information (Marr 2020). These measures are possible because different approaches exist that assist the detection of fake news. For example, platforms based on machine learning use fake news from the biggest media outlets, to refine algorithms for identifying fake news (Macaulay 2018). Some approaches detect fake news by using metadata such as a comparison of release time of the article and timelines of spreading the article as well where the story spread (Macaulay 2018).

Identifying Fake Experts A Conceptual Framework And Case Study Of

People Also Search

Corresponding Author. P105401@siswa.ukm.edu.my Received 2023 Jun 13; Revised 2023 Sep

This Disinformation Has A Negative Influence On Several Domains, Such

The Limitations Of The Datasets, Their Features, How These Features

Harris, S.; Hadi, H.J.; Ahmad, N.; Alshara, M.A. Fake News

Technologies. 2024; 12(11):222. Https://doi.org/10.3390/technologies12110222 Harris, Sheetal, Hassan Jalil Hadi, Naveed