Fake News Research Articles

Enhanced Fake News Detection with the Aid of Improved Spider Monkey Optimization-Based Optimal Feature Selection and Deep Neural Network

Fake news has become a significant problem in recent years, leading to widespread misinformation and public manipulation. This research focuses on developing an effective fake news detection model using advanced machine learning and deep learning techniques. Existing methodologies face challenges such as poor performance with large datasets, noise, and limited generalization. The proposed solution integrates pre-processing, feature extraction, optimal feature selection via Spider Monkey Optimization (SMO), and a deep neural network (OAF-DNN) with optimized activation functions. The model's performance will be validated using publicly available datasets and analyzed through various evaluation metrics. This study aims to enhance the accuracy, precision, and detection of fake news.

The empowering role of health literacy in combatting fake news, misinformation and infodemics

Abstract Background In times of the rapid digital transformation, people need to acquire specific knowledge, skills and attitudes to deal with data, digital information and technology. Such skills are particularly important in times of crisis, as the COVID-19 pandemic has shown. Alongside the pandemic, a so called “infodemic” has emerged, i.e., an overabundance of information, accompanied by misinformation, which impedes sound decision-making processes, affects health literacy (HL) and impacts public health. However, while misinformation poses a well-known threat to our health and well-being, we still lack viable concepts and approaches to satisfactorily solving this issue. Thus, we aimed at reviewing the concept of HL in view of the infodemic and health-related challenges. Methods In a 12-month participatory process in Switzerland in 2023, we investigated the empowering role of HL in light of the polycrisis. On behalf of the Swiss Federal Office of Public Health, we conducted a literature review on existing HL definitions. Then, we conducted 6 expert interviews and 2 focus group interviews with other 10 experts in HL and associated domains, which guided and informed the review process. Results As a result, HL can be understood as a bundle of competencies to proactively deal with health-related information, services, and challenges and thereby empowers people to manage their and other’s health and well-being. Thus, HL empowers people to better manage digital information and services and promotes critical thinking. This in turn is necessary to assess information quality, uncover misinformation and to adequately manage health data and information. Conclusions HL represents a crucial prerequisite for individuals, professionals, and decision-makers to find trustworthy health information, to reflect upon the quality and credibility of sources and content, and to understand the complex interrelations of the determinants of health and is therefore crucial for public health. Key messages • Health literacy empowers people to adequately deal with fake news, misinformation and infodemics. • Strengthening health literacy offers great potential for public health to promote critical thinking and to rebuild trust.

Public views on open data health research in Switzerland: Implications for research and policy

Abstract Background The increasing volume of publicly available data offers health researchers opportunities to explore public sentiment through open data research on issues such as vaccination hesitancy and fake news propagation. To ensure ethical conduct and maintain public trust, assessing public perceptions of open data research in healthcare is crucial. Methods We conducted 10 focus groups across the German, French, and Italian-speaking regions of Switzerland. We presented participants with real-world examples of open data research in healthcare and guided discussions using a semi-structured format. The topic areas were: (1) using Twitter data to assess public opinion on gene-editing technology, (2) assessment of health policy documents, (3) using Twitter data to predict individual health status and (4) using YouTube data to assess public sentiment on health policies. We assessed our findings using a thematic analysis approach. Results Gathering insights from 75 citizens, four major themes emerged: (1) Expectations towards actors and open data research, such as using research findings for societal benefit, and ensuring transparency in data use through informed consent; (2) Supportive measures including communication strategies to raise awareness and inform the public about the use of publicly available data for health research; (3) Concerns about data reuse for purposes beyond the study’s objectives, especially for financial gain, and about data validity and privacy, particularly for sensitive health data; and (4) Mitigative measures to minimize harm, such as through the involvement of external oversight committees. Conclusions Our findings indicate public support for open data research in healthcare, provided that specific expectations are met. Developing a guiding framework for legitimate open data research in healthcare is a valuable next step, with a focus on enhancing public awareness through communication campaigns and providing oversight measures to foster public trust. Key messages • Open data research in healthcare relies on public data use without explicit consent, which can disconnect the research community from society. • We found that public support for open data research in healthcare is generally present, however it is also conditional on communication and policy efforts addressing their concerns.

Ingenuidade e Inércia Cognitiva como Atributos para a Viralização de Fake News nas Redes Sociais Online

The dissemination of fake news on online social networks has created a scenario of disinformation in which manipulation and control are used to influence behavior and gain advantages. This phenomenon has motivated joint efforts to inhibit its spread. However, despite these initiatives, the problem persists. The assumption is that users of these channels, who are key agents in confronting this phenomenon, become contributors to disinformation when affected by naivety or cognitive inertia. The aim is to analyze the role of these users in the spread of fake news and identify factors that influence their uncritical stance. Conducted through a narrative review of the literature for theoretical purposes, this analysis adopts a reflective, exploratory and essayistic approach to the condition of co-participants of online social network users in the face of the problem of fake news, based on key themes such as post-truth and critical information competence, and illustrated by an empirical analysis of three specific cases of viral fake news. It is concluded that users of online social networks, when naive and inert, are part of the problem and, therefore, need to be more reactive, instead of, consciously or unconsciously, becoming agents of misinformation.

ChatGPT as a Source of Information about Russian Military Involvement in Ukraine (2014–Present)

The current online environment has seen an explosion of false news and conspiracy theories, and questions have been raised as to whether Large Language Models or Large Generative Artificial Intelligence Models such as ChatGPT could be used to generate false information. Against this background, the current study investigated whether ChatGPT could be used as a reliable source of information about Russian military involvement in Ukraine (2014– present). Based on previous research, seven Russian narratives that have justified Russian military involvement in Ukraine (from a Russian perspective) were identified and ChatGPT was tasked with generating 10 responses to 10 questions posed around these narratives. Responses were scored for truthfulness by two annotators. Overall, the study found that ChatGPT does not generate misinformation, and on average, truthfulness was scored at 3.19/4. It is also shown how ChatGPT's responses differed between questions, with those relating to Russian claims about Ukrainian atrocities in the Donbas and the reliability of Russian media channels scoring lower on truthfulness, and those relating to the reliability of the Western mainstream media scoring the highest. Generally, ChatGPT's responses pointed to reliable and trustworthy sources of information about this conflict, suggesting that this and similar technologies could be employed to combat misinformation.

Narrow Margins and Misinformation: The Impact of Sharing Fake News in Close Contests

This study investigates the impact of candidates disseminating fake news on voter behavior and electoral outcomes in highly competitive, partisan races. While the effects of fake news on electoral outcomes have been studied, research has yet to examine the impact of candidates’ strategic use of fake news in elections where it may have the greatest impact—close races. This research explores whether the use of fake news influences voter support, particularly among independent voters, in tightly contested elections. Through a conjoint survey experiment involving participants from Amazon MTurk, this study analyzes how variables such as race competitiveness, perceived risk of alienating independents, and the presence of partisan labels affect voter responses to candidates who spread misinformation. The findings indicate that while the competitiveness of a race does not significantly enhance support for candidates sharing fake news, the presence of partisan labels does. These results suggest that voter behavior in response to fake news is more closely tied to partisan identity than to strategic electoral considerations. This study highlights the complex dynamics of misinformation in electoral contexts and its implications for democratic processes.

Modality deep-learning frameworks for fake news detection on social networks: a systematic literature review

Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.

NEWS PRODUCTION MANAGEMENT ”JABAR SABER HOAKS” IN OVERCOMING THE HOAX OUTBREAK ON SOCIAL MEDIA

This research emerged due to the increasing number of fake news on social media which has negative impacts such as misinformation and propaganda. This problem is getting worse because widespread access to social media is exacerbating the situation, even triggering social conflict. To overcome this, the West Java Diskominfo established Jabar Saber Hoaks on Instagram and websites to verify fake news and reduce its spread. The aim of this research is to understand how West Java Saber Hoaks manages news production in dealing with the spread of fake news on social media. This research uses a qualitative descriptive method with data collection techniques through in-depth interviews and observation. News production management is observed based on George R. Terry's management principles which include Planning, Organizing, Actuating, and Controlling or POAC. Research subjects included division heads, coordinators, dissemination heads, and West Java Saber Hoaks social media managers. The research results show that West Java Saber Hoax implements planning steps by setting priorities and analyzing hoax trends. Organizing is carried out by teams responsible for fact checking and social media. The clarification process is carried out by checking facts first, then distributing verified information through social media and websites. Control is carried out by monitoring the reach and response to clarifying news. In conclusion, Jabar Saber Hoaks has carried out careful planning, efficient organization, active implementation and careful control in fighting fake news on social media.

The potential effects of deepfakes on news media and entertainment

AbstractDeepfakes are synthetic media, such as pictures, music and videos, created with generative artificial intelligence (GenAI) tools, a technique that builds upon machine learning and multi-layered neural networks trained on large datasets. Today, anyone can create deepfakes online, without any knowledge about the underpinning technology. This fact opens up creative opportunities at the same time as it creates individual and societal challenges. Some identified challenges are fake news, bullying, defamation, media manipulation and democracy damage. The aim of this study was to analyse and discuss how deepfakes could affect entertainment, education and digital news media in positive and negative ways. The chosen research strategy was a qualitative cross-sectional study to create a snapshot of current attitudes in blogs, online discussion fora and from a selected expert panel. Firstly, blogs and internet discussion fora on deepfakes and AI were analysed inductively. Secondly, results from the previous step were used to create a question scheme for semi-structured interviews with some selected experts that have relevant knowledge in the field of AI and deepfakes. Finally, interviews answered were analysed thematically using the Technology Affordances and Constraints Theory as an analytical lens. Findings show that deepfakes at the same time have a wide variety of affordances, but also some critical constraints to consider. In the affordance part, there are the potential use of deepfakes for speculative and provoking concepts, not only as entertainment, but also to illustrate educational scenarios that are hard or impossible to capture in traditional videos, images or sound recordings. Furthermore, there are many affordances for entertainment and the creation of environments where realism is not an important condition. Some examples are video game graphics, animated film background music in games and music videos. However, the conclusion is that the findings comprise more of constraints than of affordances, where the serious negative aspect of deepfakes can lead to psychological, financial and social harm. Many images and videos in news media have already been manipulated during the last decades, but deepfakes can take this to a new and more problematic level where the overall trust might disappear. The author’s recommendation is to further increase the focus on source criticism and that discussions on which sources to trust should be an early introduced part of educational programmes.

Methodology of Strengthening Students’ Media Literacy

In today’s digital age, media literacy has become an essential skill for students. The rise of social media, fake news, and information overload necessitates that students critically evaluate and understand media messages. Media literacy involves the ability to access, analyze, evaluate, create, and communicate using various forms of media. It is not merely about learning to use digital tools, but about understanding how media influences perceptions and behavior. The growing role of media in shaping political, social, and cultural realities makes it essential to adopt methodologies that strengthen students' media literacy skills. This article explores key methodologies that educators can implement to enhance media literacy in students.

CoFFEE: a codec-based forensic feature extraction and evaluation software for H.264 videos

The forensic analysis of digital videos is becoming increasingly relevant to deal with forensic cases, propaganda, and fake news. The research community has developed numerous forensic tools to address various challenges, such as integrity verification, manipulation detection, and source characterization. Each tool exploits characteristic traces to reconstruct the video life-cycle. Among these traces, a significant source of information is provided by the specific way in which the video has been encoded. While several tools are available to analyze codec-related information for images, a similar approach has been overlooked for videos, since video codecs are extremely complex and involve the analysis of a huge amount of data. In this paper, we present a new tool designed for extracting and parsing a plethora of video compression information from H.264 encoded files, including macroblocks structure, prediction residuals, and motion vectors. We demonstrate how the extracted features can be effectively exploited to address various forensic tasks, such as social network identification, source characterization, and double compression detection. We provide a detailed description of the developed software, which is released free of charge to enable its use by the research community to create new tools for forensic analysis of video files.

Partisanship and Older Americans’ Engagement with Dubious Political News

Abstract Studies based on digital trace data show that older Americans visit and share dubious news sources far more often than younger cohorts, tendencies often attributed to lower levels of digital literacy. At the same time, survey experiments show that older Americans are no worse, if not better, at discerning between false and accurate news. If older Americans can identify misleading news content equally well, why are they still more likely to engage with it in observational settings? In this article, we combine survey measures and digital trace data for three nationally representative samples (N = 9,944) to argue that the existing literature overemphasizes the importance of factors like digital literacy relative to standard political variables such as political interest and partisanship, factors known to increase across the lifespan. Calcified partisanship in particular makes older Americans vulnerable to hyperpartisan news—which is highly slanted but not verified as explicitly false. High rates of engagement with this category of content, which has been examined in survey studies of older citizens less regularly in the literature, may partially explain the high rates of engagement with dubious news domains in behavioral trace data. In all, our findings have important implications for how we understand—and might intervene to reduce—high engagement among this cohort with dubious news.

DEVELOPING A WEB BROWSER EXTENSION TO PREVENT THE SPREAD OF FAKE NEWS

Fake news causes huge social problems such as misdirecting and provoking the masses, creating an atmosphere of chaos by spreading fear. Detecting and stopping the dissemination of fake news has been an important and priority issue due to its rapid spread, difficulty to detect, and negative effects. In our study, a new Chrome extension that detects fake news has been developed in order to detect and prevent the spread of fake news. In the study; natural language processing, data mining methods, and various machine learning techniques for instance Passive Aggressive, Random Forest, Support Vector Machine, AdaBoost, XGBoost, and Long-Short-Term Memory (LSTM) algorithms are used. The accuracy rates of the algorithms, confusion matrices, AUC rates, and ROC curves were compared. The created machine learning model has been implemented in the online internet environment by using Flask and Rest API in the Python program. Finally, the Chrome extension interface was built using Javascript, HTML, and CSS. The LSTM algorithm gave the highest prediction result with a rate of 90.72% compared to other algorithms.

The Effectiveness of an Educational Intervention on Countering Disinformation Moderated by Intellectual Humility

<span lang="EN-US">There is an ongoing debate among scholars on how to tackle disinformation. Media education initiatives to increase literacy are effective ways to counter disinformation. Hence, the European Commission (2022) published <em>Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy Through Education and Training</em>. The present research looked at the role of social media literacy in increasing awareness of the role of social media in spreading disinformation. We developed an educational intervention based on the European Commission guidelines. We investigated its impact on perceived social media literacy, the intention to share fake news on social media, and general conspiracy beliefs. We conducted a within-subject (two times measurement: before the educational intervention and one week after) +1 experiment with <em>N</em> = 127 young adults (aged 18 to 23). After filling in an initial survey, the experimental group received a 15-minute educational intervention on the role of social media for disinformation dissemination in complex digital information environments. One week later, all participants completed the second survey to assess perceived social media literacy and general conspiracy beliefs. In both surveys, participants saw three Instagram posts from a fictitious media outlet to express potential intentions to share on social media. Among the three posts, two showed false information. Findings showed that educational intervention produces a significant increase in perceived social media literacy and a decrease in general conspiracy beliefs. Intellectual humility moderates the impact of educational intervention on algorithmic awareness.</span>

DESAFIOS E OPORTUNIDADES DA COMUNICAÇÃO NA SOCIEDADE DIGITAL

Communication in a digital society presents challenges and opportunities that transform the way we connect and share information. With the advancement of information technologies, communication becomes more agile, accessible and democratic. However, this transformation also brings challenges such as the spread of fake news, user privacy and excessive dependence on social networks. New digital platforms offer unique opportunities for business, education and social relationships, enabling global reach. However, the speed of information often compromises the depth of dialogues and the discernment between what is true or manipulation. The exponential growth of online content requires reflection on how to use digital tools ethically and effectively. Furthermore, digital communication creates new forms of interaction, which challenge traditional models and require new communication skills. Digital education and media literacy emerge as solutions to prepare individuals to deal critically with this new environment. Therefore, the balance between the opportunities for innovation and the challenges of maintaining authentic and secure communication is essential for the future of communication in the digital society.

Sentence Embeddings and Semantic Entity Extraction for Identification of Topics of Short Fact-Checked Claims

The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semantic vocabulary. Classification of topics should go beyond simple connotations like instance-class and rather reflect broader phenomena that are recognized by fact checkers. The assignment of semantic entities is also crucial for the automatic verification of facts using the underlying knowledge graphs. Our method is based on sentence embeddings, various clustering methods (HDBSCAN, UMAP, K-means), semantic entity matching, and terms importance assessment based on TF-IDF. We represent our topics in semantic space using Wikidata Q-ids, DBpedia, Wikipedia topics, YAGO, and other relevant ontologies. Such an approach based on semantic entities also supports hierarchical navigation within topics. For evaluation, we compare topic modeling results with claims already tagged by fact checkers. The work presented in this paper is useful for researchers and practitioners interested in semantic topic modeling of fake news narratives.

An Efficient Fusion Network for Fake News Classification

Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in authorities and institutions. Therefore, the identification of fake news has emerged as a major field of research that seeks to validate content. The proposed model operates in two stages: First, TF-IDF is applied to an entire document to obtain its global features, and its spatial and temporal features are simultaneously obtained by employing Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory with a Gated Recurrent Unit. The Fast Learning Network efficiently classifies the extracted features. Comparative experiments were conducted on three easily and publicly obtainable large-scale datasets for the purposes of analyzing the efficiency of the approach proposed. The results also show how well the model performs compared with past methods of classification.

Stochastic bilevel interdiction for fake news control in online social networks

Social media platforms attempt to mitigate and control fake news, using interventions such as flagging posts or adjusting newsfeed algorithms, to protect vulnerable individuals. In this paper, we consider performing intervention actions on specific source nodes or user–user edges in social networks, under uncertain effectiveness of different intervention strategies. We model misinformation from malicious users to vulnerable communities using stochastic network interdiction formulations. Specifically, we minimize the expected number of reachable vulnerable users via stochastic maximum flow, and develop an alternative formulation for handling large-scale social networks based on their topological structures. We derive theoretical results for path-based networks and develop an approximate algorithm for single-edge removal on paths. We test instances of a social network with 23,505 nodes, based on the IMDb actors dataset, to demonstrate the scalability of the approach and its effectiveness. Via numerical studies, we find that characteristics of removed edges change when intervention effectiveness is stochastic. Our results suggest that intervention should target on (i) a smaller set of centrally located edges with nodes that represent communities where regulatory actions are more effective, and (ii) dispersed edges with nodes where intervention has a high chance of failure.

A knowledge-Aware NLP-Driven conversational model to detect deceptive contents on social media posts

The widespread dissemination of deceptive content on social media presents a substantial challenge to preserving authenticity and trust. The epidemic growth of false news is due to the greater use of social media to transmit news, rather than conventional mass media such as newspapers, magazines, radio, and television. Humans' incapacity to differentiate among true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and government credibility. Using combination of advanced methodologies, Deep learning (DL) methods, and Natural Language Processing (NLP) approaches, researchers and technology developers attempt to make robust systems proficient in discerning the subtle nuances that betray deceptive intent. Analysing conversational linguistic patterns of misleading data, these techniques’ purpose to progress the resilience of social platforms against the spread of deceptive content, eventually contributing to an additional informed and trustworthy online platform. This paper proposed a Knowledge-Aware NLP-Driven AlBiruni Earth Radius Optimization Algorithm with Deep Learning Tool for Enhanced Deceptive Content Detection (BER-DLEDCD) algorithm on Social Media. The purpose of the BER-DLEDCD system is to identify and classify the existence of deceptive content utilizing NLP with optimal DL model. In the BER-DLEDCD technique, data pre-processing takes place to change the input data into compatible format. Furthermore, the BER-DLEDCD approach applies hybrid DL technique encompassing Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) methodology for deceptive content detection. Moreover, the BER approach has been deployed to boost hyperparameter choice of the CNN-LSTM technique which leads to enhanced detection performance. The simulation outcome of the BER-DLEDCD system has been examined employing benchmark database. The extensive outcomes stated the BER-DLEDCD system achieved excellent performance with the accuracy of 94 %, 94.83 % precision, 94.30 % F-score with other recent approaches.

An Efficient Deep Learning Mechanism for Predicting Fake News/Reviews in Twitter Data

Recently, social media platforms have been widely utilized as information sources due to their effortless accessibility and reduced costs. However, online platforms like Instagram, Twitter and Facebook get influenced by their users via fake news/reviews. The main intention of spreading fake news is to mislead other network users, which highly affects businesses, political parties, etc. Thus, an effective methodology is needed to predict fake news from social media automatically. The major objective of this proposed study is to identify and classify the given Twitter input data as real or fake through deep learning mechanisms. The proposed study involves four stages: pre-processing, embedded word analysis, feature extraction, and fake news/reviews prediction. Initially, pre-processing is performed to enhance the quality of data with the help of tokenization, stemming and stop word removal. Embedded word analysis is done using Advanced Word2Vec and GloVe modeling to enhance the performance of a proposed prediction model. Then, the hybrid deep learning model named Dense Convolutional assisted Gannet Optimal Bi-directional Network (DC_GO_BiNet) is introduced for feature extraction and prediction. A Dense Convolutional Neural Network (DCNN) is hybridized with a bi-directional long-short-term memory (Bi-LSTM) model to extract the essential features and predict fake news from the given input text. Also, the proposed model’s parameters are fine-tuned by adopting a gannet optimization (GO) algorithm. The proposed study used three different datasets and obtained higher classification accuracy as 99.5% in Fake News Detection on Twitter EDA, 99.59% in FakeNewsNet and 99.51% in ISOT. The analysis proves that the proposed model attains higher prediction results for each dataset than others.