Misinformation Research Articles

Calibrations without raw data—A response to “Seasonal calibration of the end-cretaceous Chicxulub impact event”

A recent article by DePalma et al. reported that the season of the End-Cretaceous mass extinction was confined to spring/summer on the basis of stable isotope analyses and supplementary observations. An independent study that was concurrently under review reached a similar conclusion using osteohistology and stable isotope analyses. We identified anomalies surrounding the stable isotope analyses reported by DePalma et al. Primary data are not provided, the laboratory where the analyses were performed is not identified, and the methods are insufficiently specified to enable accurate replication. Furthermore, isotopic graphs for carbon and oxygen contain irregularities such as missing data points, duplicate data points, and identical-length error bars for both elements despite different scales, that appear inconsistent with laboratory instrument outputs. A close examination of such methodological omissions and data irregularities can help to raise the standards for future studies of seasonality and prevent inaccurate claims or confirmation bias.

ChatGPT for parents' education about early childhood caries: A friend or foe?

With the increasing popularity of online sources for health information, parents may seek information related to early childhood caries (ECC) from artificial intelligence-based chatbots. The aim of this article was to evaluate the usefulness, quality, reliability, and readability of ChatGPT answers to parents' questions about ECC. Eighty questions commonly asked about ECC were compiled from experts and keyword research tools. ChatGPT 3.5 was asked these questions independently. The answers were evaluated by experts in paediatric dentistry. ChatGPT provided "very useful" and "useful" responses to 82.5% of the questions. The mean global quality score was 4.3 ± 1 (good quality). The mean reliability score was 18.5 ± 8.9 (average to very good). The mean understandability score was 59.5% ± 13.8 (not highly understandable), and the mean actionability score was 40.5% ± 12.8 (low actionability). The mean Flesch-Kincaid reading ease score was 32% ± 25.7, and the mean Simple Measure of Gobbledygook index readability score was 15.3 ± 9.1(indicating poor readability for the lay person). Misleading and false information were detected in some answers. ChatGPT has significant potential as a tool for answering parent's questions about ECC. Concerns, however, do exist about the readability and actionability of the answers. The presence of false information should not be overlooked.

Medical Board Discipline of Physicians for Spreading Medical Misinformation.

False medical information disseminated dangerously during the COVID-19 pandemic, with certain physicians playing a surprisingly prominent role. Medical boards engendered widespread criticism for not imposing forceful sanctions, but considerable uncertainty remains about how the professional licensure system regulates physician-spread misinformation. To compare the level of professional discipline of physicians for spreading medical misinformation relative to discipline for other offenses. This cross-sectional study analyzed and coded publicly reported medical board disciplinary actions in the 5 most populous US states. The analysis included data from January 1, 2020, through May 30, 2023, for California, Florida, New York, and Pennsylvania and from January 1, 2020, through March 30, 2022, for Texas. Medical board disciplinary proceedings that resulted in some form of sanction were analyzed. Codes were assigned for the different types of offenses relied on by medical boards for imposing physician discipline. Among 3128 medical board disciplinary proceedings in the 5 most populous states, spreading misinformation to the community was the least common reason for medical board discipline of physicians (6 [0.1%] of all identified offenses). Two reasons tied for third least common: patient-directed misinformation (21 [0.3%]) and inappropriate advertising or patient solicitation (21 [0.3%]). The frequency of misinformation conduct was exponentially lower than more common reasons for discipline, such as physician negligence (1911 [28.7%]), problematic record-keeping (990 [14.9%]), and inappropriate prescribing (901 [13.5%]). Patient-directed misinformation provided a basis for discipline 3 times as often as spreading misinformation to the community. The frequency of disciplinary actions for any reasons related to COVID-19 care, even if not about misinformation, was also quite low (10 [0.2%]). Sanctions in misinformation actions tended to be relatively light. The frequency of discipline for physician-spread misinformation observed in this cross-sectional study was quite low despite increased salience and medical board warnings since the start of the COVID-19 pandemic about the dangers of physicians spreading falsehoods. These findings suggest that there is a serious disconnect between regulatory guidance and enforcement and that medical boards relied on spreading misinformation to patients as a reason for discipline 3 times more frequently than disseminating falsehoods to the public. These results shed light on important policy concerns about professional licensure, including why, under current patient-centered frameworks, this form of regulation may be particularly ill-suited to address medical misinformation.

Understanding BNS Sections 238 and 61(2): Evidence Tampering, Supplying False Information to Protect a Criminal, and Police Accountability

This paper provides a detailed analysis of Sections 238 and 61(2) of the Bharatiya Nyaya Sanhita, 2023 (BNS), focusing on the implications of tampering with evidence or providing false information to protect a criminal, especially concerning the responsibilities of police officers. Those who deliberately obliterate, remove, destroy or conceal evidence related to a crime or offer false information to shield an offender face penalty under Section 238 BNS, where the severity varies based on the nature of the offence, potentially resulting in a prison sentence of up to seven years and a fine. In addition, Section 61(2) BNS in this regard addresses issues related to criminal conspiracy between two or more persons in order to destroy or temper with evidence or furnish false information to shield a criminal from arrest or criminal liability and stipulates punishment commensurate with the seriousness of the offence. It also underscores the importance of investigating police officers' safety and the need to adhere to ethical and investigative standards. The study highlights the crucial role of honesty and transparency within law enforcement to promote accountability and uphold the principles of justice.

ChatGPT-Supported Education in Primary Schools: The Potential of ChatGPT for Sustainable Practices

This study aims to evaluate the potential of using ChatGPT at the primary school level from the teachers’ perspective within a sustainability framework. The research was conducted as a qualitative case study involving 40 primary school teachers in Turkey during the 2023–2024 academic year, all of whom had no prior experience with ChatGPT. Data collection tools included semi-structured interview forms and researcher diaries developed by the researchers. The data obtained were analysed using content analysis. The findings indicate that most primary school teachers believe ChatGPT is suitable for primary education and can contribute to Sustainable Development Goal (SDG) 4. Additionally, teachers noted that ChatGPT enriches the teaching process and is user-friendly. These findings suggest potential contributions to SDG 4.1 and SDG 4.2. However, concerns were raised regarding ChatGPT’s potential to provide false information, which may negatively impact SDG 4.7. The study also identified that ChatGPT is particularly suitable for mathematics, Turkish, and English courses. This study’s main contribution is that it shows how ChatGPT can help sustainable practices in primary education by getting teachers more involved and meeting specific curriculum needs. This gives us useful information for incorporating AI tools into education that is in line with SDG 4. It is recommended that training programs about ChatGPT and similar AI-supported tools be organised for teachers and parents.

MANIPULATING PERCEPTIONS AND BEHAVIOR THROUGH COGNITIVE WARFARE TECHNIQUES IN THE DIGITAL AGE

Dominated by technology and information in our digital era, cognitive warfare has become a ubiquitous occurrence impacting views and human behavior by leveraging cognitive weaknesses. Rapid spread of propaganda and false information made possible by the Internet and social media seriously influences democracies, economics, and social stability by means of their effects on Emphasizing the need of preserving cognitive integrity, this book investigates how psychology and technology interact to control minds and behaviors. Propaganda, contrived stories, behavioral psychology, and the application of personal data for tailored influence efforts are among the strategies applied. Media education, digital literacy, and the development of psychological resilience are therefore absolutely crucial in order to offset these consequences.

SOCIAL MEDIA FAKE ACCOUNT IDENTIFICATION USING ML

The proliferation of social media platforms has led to an increase in the creation of fake accounts. These accounts are used for various malicious activities, such as spreading false information, phishing, and identity theft. As a result, there is a growing need for effective methods to identify and eliminate fake accounts. This paper proposes a machine learning-based approach for social media fake account identification. This paper proposes a machine learning-based approach to identify fake accounts on social media platforms. Our method leverages a combination of feature extraction techniques and supervised learning algorithms to classify accounts as genuine or fake. We collect a large dataset of labeled social media accounts, including both genuine and fake profiles, and extract features from various sources such as profile information, network behavior, and content analysis. We experiment with multiple machine learning models, including Support vector machines(SVM),K-Nearest Neighbors Algorithm(KNN),Random forest, Logistic Regression & Artificial Neural Network(ANN) to evaluate their performance in identifying fake accounts. Our proposed method has significant implications for social media platform operators, policymakers, and researchers seeking to combat fake accounts and maintain online trust. The approach can be integrated with existing social media moderation tools to enhance the accuracy and efficiency of fake account identification. Key Words: Support vector machines(SVM),K-Nearest Neighbors Algorithm(KNN),Random forest, Logistic Regression & Artificial Neural Network(ANN),Python.

DENOTATIVE AND CONNOTATIVE MEANING IN VIRGIN VS. CHAD MEME IN KNOW YOUR MEME SITE

This research aims to find out how hidden meanings are conveyed in memes and the type of meme studied is a meme with the Virgin vs. Chad template and other similar template memes namely Swole Doge vs. Cheems on the official meme site and Know Your Meme's official Instagram account. By using descriptive qualitative methods, this research aims to analyze the meaning of denotation and connotation attached to several data. Researchers found 10 memes that use the template Virgin vs. Chad and 10 memes that use the Swole Doge vs. Cheems. The results of the analysis show that the denotational meaning in these memes shows figures that have been arranged and edited as creatively as possible by the creator without describing any further meaning, just figures and text written without context, while the connotative meaning in these memes describe the hidden meanings of the meme, such as how the meme template functions, the role of the figures displayed, and the written text has an implied meaning, whether it is the opinion of the meme maker, public opinion, facts in the field, even there are exaggerated opinions and even false information.

Conflict and Context: Jadidism, Pan-Islamism, and the Muslims of Akmolinsk at the Beginning of the 20th Century

Abstract This article analyzes a conflict between the imam of Akmolinsk’s Second Congregational Mosque Hujjat Mahmudov and the Tatar merchant Nur-Muhammad Zabirov. The conflict took place in 1912–1913, and attracted the attention of the public at large, editorial boards of Muslim newspapers, and the Russian colonial administration. The depictions of the conflict were contradictory and depended on who was telling the story: the immediate participants, residents of Akmolinsk, or external observers. When the imam accused the Tatar merchant of ignorance and a grossly over-extravagant lifestyle, the merchant’s defenders immediately accused the imam of spreading forbidden innovations and selling banned literature, i.e., the works by the Kazakh writer Mir-Zhaqïp Dulatov and the editor of the newspaper Waqït, Fatih Karimi. These works were regarded by imperial officials as politically dangerous on account of containing false information about the Romanov Dynasty, as well as Pan-Turkic and Pan-Islamic ideas. At first glance, the Akmolinsk story appears to illustrate how Muslims themselves made use of the imperial discourse about the threat of jadidism and Pan-Islamism to eliminate their enemies; but it also exhibits a series of distinct peculiarities. My aim is to show that it is not so much the conflict as such that is important, but rather the context in which it arose. A careful study of this context allows us to understand the principles by which the local society functioned. Focusing on specific representations of the conflict (newspaper accounts, the point of view of imperial bureaucrats, the petitions of the immediate participants), we ignore the epistemic value of other sources – for example, the opinions of local Muslims who do not necessarily describe what happened through the prism of fundamental contradictions and imply, among other things, the confrontation between so-called reformers and traditionalists. The Muslims of Akmolinsk, like those of other regions of the Russian Empire, were aware of the nature of external threats and challenges; however, they could also perceive them based on their own rationality as expressed in their prevailing cultural and everyday life. In some cases, that rationality was draped in pragmatism; in others, it stemmed from an attempt to protect cultural self-sufficiency – that is, a specific world of ideas and values; in yet others, it was justified by the need to carry out those small reforms and changes that could fit organically into the customary course of things.

A Narrative Review of the Digital Equity Gap of Apps for Cigarette Smoking Cessation for Persons Living in the Hispanosphere

Abstract Purpose of Review The Hispanosphere is a vast region of the world that has received little attention in the digital health literature. No study to date has examined the availability and quality of publicly available mobile applications (apps) for cigarette smoking cessation in this region. Three coders utilized the American Psychiatry Association (APA)’s Brief App Evaluation Model Screener (Brief-AEM Screener) to evaluate the quality of the label and public-facing screens of smoking cessation apps in Spanish. Availability of apps in the Hispanosphere was compared to availability of apps in the Anglosphere. Recent Findings We identified and reviewed 19 apps in Spanish in Google Play. The median score using the Brief-AEM Screener was 63 out of 100 suggesting generally acceptable app quality and features according to the quality standards for digital health tools proposed by the APA. However, we found (1) notable inaccurate and misleading labelling claims, (2) poor grammar or incomplete translations, and (3) a lack of cultural and linguistic adaptation to countries in the Hispanosphere. Our comparison of smoking cessation apps between the Hispanosphere and the Anglosphere suggested that there is a large digital equity gap between these two regions, with a four to sevenfold gap in app availability. Summary There is a relative shortage of quality and quantity of digital health apps for smoking cessation in the Hispanosphere. To ensure the cultural appropriateness of those digital interventions, it is essential that developers of digital health tools establish community partners in the region prior to developing apps for smoking cessation.

Evaluating ChatGPT's Role in Assessing Turkey's Medical Tourism Trends

Background and Aims: The primary objective of this study is to evaluate the performance of the ChatGPT as an expert in the field of medical tourism. Another objective of this study is to assess its evaluations regarding the current state and future prospects of medical tourism in Turkey from an artificial intelligence perspective. Methods: Within the scope of the study's objectives, questions were created for ChatGPT regarding the current state and future prospects of medical tourism in Turkey. First, the draft questions were prepared by the authors of this article using the literature. All questions were evaluated by experts through online interviews. Totally 8 experts have been evaluated the questionnaires. These experts consisted of two tourism professors who had research on medical tourism, three major medical tourism hospital managers, one reconstructive aesthetician clinic owner, and the Turkish Healthcare Travel Council Founding Chairman. The second step of the research questionnaire was applied to the ChatGPT Model 4, which was trained as a medical tourism expert. than test the answers of the AI used the tools “Completeness of content, Lack of false information in the content, Evidence supporting the content, Appropriateness of the content, and Relevance,” referred as CLEAR. All answers were evaluated by 41 experts who had articles and research on medical tourism in Turkey. To explain the methodology appropriately, reporting was conducted using the METRICS checklist, which was prepared for artificial intelligence studies. Results: TThe general answers of ChatGPT 4-o were accurate, informative, and helpful, providing a good overview of medical tourism in Turkey. However, the responses lacked detail, did not provide evidence-based information, and did not always address the nuances and cultural/social impacts of medical tourism. ChatGPT 4-o views the future of Turkey’s medical tourism as shiny and will force the lead in general. Conclusions: This study is important for revealing data on the current and future state of medical tourism through artificial intelligence. However, only a few studies have been conducted on artificial intelligence and medical tourism. To the best of our knowledge, there is no existing literature that systematically evaluates the ChatGPT responses of medical tourism experts. In this regard, it is believed that the study will benefit both academics and practitioners.

Ensemble based high performance deep learning models for fake news detection

Social media has emerged as a dominant platform where individuals freely share opinions and communicate globally. Its role in disseminating news worldwide is significant due to its easy accessibility. However, the increase in the use of these platforms presents severe risks for potentially misleading people. Our research aims to investigate different techniques within machine learning, deep learning, and ensemble learning frameworks in Arabic fake news detection. We integrated FastText word embeddings with various machine learning and deep learning methods. We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. The research methodology involves utilizing two Arabic news article datasets, AFND and ARABICFAKETWEETS datasets, categorized into fake and real subsets and applying comprehensive preprocessing techniques to the text data. Four hybrid deep learning models are presented: CNN-LSTM, RNN-CNN, RNN-LSTM, and Bi-GRU-Bi-LSTM. The Bi-GRU-Bi-LSTM model demonstrated superior performance regarding the F1 score, accuracy, and loss metrics. The precision, recall, F1 score, and accuracy of the hybrid Bi-GRU-Bi-LSTM model on the AFND Dataset are 0.97, 0.97, 0.98, and 0.98, and on the ARABICFAKETWEETS dataset are 0.98, 0.98, 0.99, and 0.99 respectively. The study’s primary conclusion is that when spotting fake news in Arabic, the Bi-GRU-Bi-LSTM model outperforms other models by a significant margin. It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.

Quantum Cryptography and Blockchain-Based Social Media Platforms as a Dual Approach to Securing Financial Transactions in CBDCs and Combating Misinformation in U.S. Elections

This paper explores the integration of quantum cryptography and blockchain technology to address two pressing challenges: securing financial transactions in Central Bank Digital Currencies (CBDCs) and combating the spread of misinformation during U.S. elections through decentralized social media platforms. As quantum computing advances, traditional encryption methods may become obsolete, posing significant risks to digital financial systems. Quantum cryptography, with its quantum-resistant algorithms, offers enhanced protection for CBDC transactions, ensuring long-term security and privacy. Simultaneously, blockchain-based social media platforms provide a decentralized structure that can prevent the dissemination of false information by ensuring transparency and authenticity through cryptographic verification and consensus mechanisms. These platforms also facilitate decentralized identity management, empowering users to verify content without relying on centralized authorities. By combining quantum cryptography’s secure framework with blockchain’s decentralized transparency, this dual approach creates a more secure digital ecosystem that not only safeguards financial transactions but also strengthens democratic processes. The paper further addresses the regulatory and technical challenges associated with implementing these technologies and their potential to shape a more secure, transparent, and accountable future.

Sentiment analysis in infodemic management: leveraging the eppm Risk communication model

Infodemics are the rapid spread of false or misleading information related to public health emergencies, often through digital platforms. They can cause confusion, fear, and even harm to public health. This study investigates the application of sentiment analysis for infodemic management during disease outbreaks. Leveraging the Extended Parallel Process Model (EPPM) of risk communication, the research aims to categorize rumors based on their perceived threat level (high, medium, or low). Machine learning is employed to analyze infodemic text data collected from two Nigerian states (Oyo and Bauchi) to assess threat appraisal according to the EPPM model. The findings can inform targeted interventions for effective infodemic management before, during, and after outbreak of diseases.

Secure multi-synchronization of heterogeneous dynamical networks with deception attacks via event-triggered impulsive control

This article investigates the secure multi-synchronization control problem of the heterogeneous dynamical networks (DNs) subject to deception attacks (DAs). First of all, a heterogeneous DNs model under DAs is built. The deception attackers are assumed to inject false information into the sensor-to-controller channels and the controller-to-actuator channels. Then, the centralized and distributed event-triggered impulsive controllers (ETIC) are designed respectively for DNs subject to DAs to achieve secure multi-synchronization. The synchronization error converges to a bounded set dependent on the heterogeneity of networks and the total attack energy. In addition, the heterogeneous DNs without DAs under ETIC can achieve multi-synchronization. Finally, a numerical example is given to verify the correctness of the theoretical results.

Enhancing rumor detection with data augmentation and generative pre-trained transformer

The advent of social networks has facilitated the rapid dissemination of false information, including rumors, leading to significant societal and individual damages. Extensive research has been dedicated to rumor detection, ranging from machine learning techniques to neural networks. However, the existing methods could not learn the deep concepts of the rumor text to detect the rumor. In addition, imbalanced datasets in the rumor domain reduce the effectiveness of these algorithms. This study addresses this challenge by leveraging the Generative Pre-trained Transformer 2 (GPT-2) model to generate rumor-like texts, thus creating a balanced dataset. Subsequently, a novel approach for classifying rumor texts is proposed by modifying the GPT-2 model. We compare our results with state-of-art machine learning and deep learning methods as well as pre-trained models on the PHEME, Twitter15, and Twitter16 datasets. Our findings demonstrate that the proposed model, implementing advanced artificial intelligence techniques, has improved accuracy and F-measure in the application of detecting rumors compared to previous methods.

A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators

This study explores the global problem of misinformation dissemination on social media, particularly Twitter, due to the COVID-19 pandemic. It identifies prominent disseminators, investigates the spread of false information and the ecosystem of disinformation spreaders, and assesses their online personalities. We track the interaction among fake news spreaders using the User-User Interaction Graph. The study reveals a rapidly growing population of disseminators, including professional spreaders, with over 3% dominating the others. The collaboration among fake news spreaders is high, highlighting the need for further research using publicly available online data to understand the community spreading malicious misinformation about COVID-19.

Fake News Detection Using Machine Learning

Abstract: The identification of fake news has grown in importance as false information circulates quickly via digital channels. The goal of this project is to combine deep learning and machine learning to create an automated system that can identify news stories as legitimate or fraudulent. The preprocessed text data in the dataset, which comprises of tagged news items, was done using methods such tokenization, stop word removal, and padding. A Convolutional Neural Network (Conv1D), which is based on deep learning, was combined with more conventional machine learning models, such as Naive Bayes, in a hybrid approach. After converting the text input into a series of tokens, semantic meanings were captured by using word embeddings. Important patterns from the word sequences were intended to be captured by the Conv1D model with global max pooling, whereas the Naive Bayes also used in the project.

Continued influence of false accusations in forming impressions of political candidates

Abstract Previous work has shown that false information affects decision-making even after being corrected, a phenomenon known as “continued influence effects” (CIEs). Using mock social media posts about fictional political candidates, we observe robust within-participant CIEs: candidates targeted by corrected accusations are rated more poorly than candidates not targeted by allegations. These effects occur both immediately and after as much as a 2-day delay. We further demonstrate that vulnerability to CIEs in a political context varies systematically between individuals. We found that certain groups are more susceptible to CIEs on immediate candidate ratings (i) those who rely more on intuitive feelings, (ii) those with lower digital literacy knowledge, and (iii) younger individuals. These individuals’ judgments appear to be relatively more influenced by the refuted accusations and/or less influenced by the factual refutations. Interestingly, political orientation did not affect CIEs, despite its influence on explicitly identifying misinformation. Moreover, people recalled accusation stimuli better than refutations at a delay, suggesting that emotions may drive the prioritized processing of accusations. Our results indicate that analytic thinking could be protective when people judge political candidates targeted by refuted false information.

Challenges and Techniques in Multilingual Fake News Detection

The spread of false information across language and cultural barriers poses a significant threat to global societies. This study investigates the obstacles in detecting misinformation in various languages, including linguistic diversity, limited datasets, cultural nuances, and the ever- changing strategies of disinformation propagators. These complex issues require advanced methods to effectively identify and combat false information across multiple languages. The research evaluates several prominent machine learning approaches, such as Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), Logistic Regression, Decision Trees, and Natural Language Processing (NLP) techniques. By examining their effectiveness in multilingual contexts, this study sheds light on the advantages and drawbacks of each method. It underscores the need for continuous innovation and collaborative efforts in developing robust detection systems that can adapt to the ever-changing landscape of misinformation. These initiatives aim to promote informed public discourse and improve information integrity across diverse cultural contexts. Key Words: Multilingual Fake News Detection, Machine Learning, Machine Learning algorithms, Natural Language Processing, Misinformation.