Misinformation Research Articles

Progress In Deep Fake And Tampering : An In-Depth Analysis

The rise of deep fake technology has ignited widespread societal apprehensions about potential security risks and the dissemination of false information. Despite extensive research into deepfake detection, effectively discerning low-quality deepfakes and simultaneously identifying variations in their quality remains a significant and formidable challenge. This investigation explores the dynamic field of deep fake detection, focusing specifically on video analysis targeting facial manipulations. The study introduces Celeb-DF, a substantial dataset comprising high- quality deep fake videos of celebrities, challenging prevailing detection methods. Additionally, a revolutionary Quality-Agnostic Deep Fake Detection (QAD) framework addresses the intricate task of simultaneously recognizing diverse qualities of deep fakes, surpassing established benchmarks across multiple datasets. The paper highlights ongoing efforts to enhance deep fake detection strategies, incorporating advanced models such as Stable Diffusion, and employing interpretability-through-prototypesn by merging fine-tuned Vision Transformers with Support Vector Machines. Keywords -- Deep fake technology, security risks, deep fake detection, Celeb-DF dataset, video analysis, Quality-Agnostic Deep Fake Detection (QAD), Stable Diffusion, Vision Transformers, Support Vector Machines.

Exercise expectation changes psychophysiological responses to sprint exercise regardless of changes in pacing and performance

ABSTRACT Deception of exercise-related may influence pacing, performance and psychophysiological responses to continuous exercise, but little is known about such effects on sprint exercises. Most sports modalities are intermittent in nature, so the present study verified if sprint exercise expectation deceived by false information influenced pacing strategy, performance and psychophysiological responses to sprint exercise bouts. We hypothesised that pacing and performance would be based on affective valence in this deception study. Eleven road cyclists performed two sprint interval training (SIT) sessions in counterbalanced order. In one session, we deceived participants by informing them they would perform a more physiologically challenging exercise bout (MDDE) when compared with the control session (CON). Psychophysiological variables such as oxygen uptake, lactate, heart rate (HR), feeling scale, felt arousal scale and ratings of perceived exertion (RPE) were sampled during or after each sprint in SIT protocols. We observed no effect on pacing and performance assessed as power output, regardless of the worsened affective valence in the MDEE session. Furthermore, null effects were detected in most psychophysiological variables, with the exception of HR and affective valence. Performance in both SIT conditions was strongly correlated with affective valence and RPE. These results challenged the role of affective valence on pacing regulation and performance in sprint exercises.

The Impact of Use Social Media in Communication Flow from State House to Traditional Media in Tanzania

The media has historically obtained and disseminated government information to the public through newspapers, radio, and television. Before the advent of social media, Journalists obtained information from the State House through telegraph, telephone, fax, and in-person encounters between the President's communication officers and journalists. Over twenty years, the State House Communications Department has enhanced its communication methods and effectively utilized social media platforms to engage with journalists and the public. However, little focus on scientific research has been done to assess the impact of social media on communication flow from Tanzania’s state house to traditional media. Therefore, the study evaluated the application of new media in the Presidential Communication Directorate in disseminating news to the mainstream media. The study used a descriptive design and mixed-methods approach, combining qualitative and quantitative methodologies. The qualitative delved into individual viewpoints on news collection using new media, while the quantitative evaluated respondents' perspectives. The State House Communication Unit has shifted its use of social media platforms enhancing networking among journalists, providing quick information access, and being cost-effective. However, the shift has led to decreased personal communication, a decline in exclusive news, an increase in false information propagation, and the emergence of citizen journalism. Journalists should improve their social media skills to maintain high-quality media reporting. The Statehouse should enhance its 24/7 media unit operations. The study highlighted the challenges and significance of using social media for news dissemination and gathering. The study involved media professional practitioner’s and President's communication officers who identified significance and challenges and suggested how to improve media production and contents.

Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing Models

In an era where fake news detection has become a pressing issue due to its profound impacts on public opinion, democracy, and social trust, accurately identifying and classifying false information is a critical challenge. In this study, the effectiveness is investigated of advanced machine learning models—convolutional neural networks (CNNs), bidirectional encoder representations from transformers (BERT), and generative pre-trained transformers (GPTs)—for robust fake news classification. Each model brings unique strengths to the task, from CNNs’ pattern recognition capabilities to BERT and GPTs’ contextual understanding in the embedding space. Our results demonstrate that the fine-tuned GPT-4 Omni models achieve 98.6% accuracy, significantly outperforming traditional models like CNNs, which achieved only 58.6%. Notably, the smaller GPT-4o mini model performed comparably to its larger counterpart, highlighting the cost-effectiveness of smaller models for specialized tasks. These findings emphasize the importance of fine-tuning large language models (LLMs) to optimize the performance for complex tasks such as fake news classifier development, where capturing subtle contextual relationships in text is crucial. However, challenges such as computational costs and suboptimal outcomes in zero-shot classification persist, particularly when distinguishing fake content from legitimate information. By highlighting the practical application of fine-tuned LLMs and exploring the potential of few-shot learning for fake news detection, this research provides valuable insights for news organizations seeking to implement scalable and accurate solutions. Ultimately, this work contributes to fostering transparency and integrity in journalism through innovative AI-driven methods for fake news classification and automated fake news classifier systems.

ALIENATION SYNDROME AND FALSE MEMORIES IN THE CONTEXT OF PSYCHOLOGY AND LAW: A literature review

The aim of this paper is to present the relationship between false memories and Parental Alienation Syndrome, using psychology and law as relevant premises to support the context of the subject. To this end, the methodology used was a literature review, as a way of gathering information on the subject. The theme was developed by selecting papers from electronic journal platforms, as well as complementary research sources such as books and laws. After reading the material, content analysis was undertaken, resulting in the creation of four categories as a way of facilitating understanding and development of the theme. The categories created were: custody disputes, false accusations of sexual abuse, psychological damage and psychological assessment. The results found show us the false memories have a direct influence on Parental Alienation Syndrome, as the alienator uses false information and distortions of untrue facts as a way of demoralizing the alienated person, such as accusations of sexual abuse. In this way, we can see how necessary the intervention of the judiciary and the expert psychologist is, who will carry out an accurate psychological assessment, with the aim of supporting the judge in making a decision that will safeguard the rights of the child or adolescent, especially the right to family life and avoid damage to their healthy development.

Manipulation of statistics in information: a worrying practice

Statistics is a very versatile and indispensable tool for analyzing and making decisions in a wide range of areas, such as science, politics and communication. However, it can be manipulated for questionable purposes and generate serious consequences in society as a result of its use. The manipulation of statistics in information produces a distortion of the truth, affecting people's decision-making, individually and in groups, producing: making erroneous decisions, based on false information; economic losses due to investments or purchases based on unfounded information; and, health risks, caused by the lack of important information on safety or effectiveness. To illustrate the case, a methodology based on critical analysis was applied; evaluating and contrasting sources, identifying biases and inconsistencies and selecting validated information, conducting a context search, examining the environment and the variables that influence the statistics and the techniques used to display the data at the convenience of the interested parties. Finally, the ethical and social implications of this manipulation were discussed, as well as possible solutions to prevent and combat it.

Unmasking deception: a topic-oriented multimodal approach to uncover false information on social media

In the digital landscape, social media has emerged as a prevalent channel for global communication, connecting like-minded individuals worldwide. However, while facilitating information exchange, it is also susceptible to the dissemination of false information, posing a constant challenge to the reliability of online content. To address this issue, this paper introduces a novel methodology called TM-FID (Topic-oriented Multimodal False Information Detection), which combines false information detection and neural topic modeling within a semi-supervised multimodal approach. By jointly leveraging textual and visual information contained in online news, our approach provides insights into how false information influences specific discussion topics, thus enabling a comprehensive and fine-grained understanding of its spread and impact on social media conversation. Experimental evaluation carried out on a set of multimodal gossip-related news demonstrates the quality of the identified topics, assessed through a novel centroid-based metric, as well as the efficacy of the cross-attention mechanism used within TM-FID to accurately identify false information in multimodal news. Overall, the proposed methodology can enable effective strategies to counter the spread of false information, thereby fostering trust and confidence in the information shared on social media platforms.

Pandemic or "Plandemic"?: The Mediating Role of Epistemic Justification Strategies in the Relationship Between COVID-19 Conspiracy Beliefs and COVID-19 Vaccine Conspiracy Beliefs.

In today's post-truth times, where personal feelings and beliefs have become increasingly important, determining what is accurate knowledge has become an important skill. This is especially important during uncertainty crises (e.g., epidemics and pandemics) because alternative explanations other than scientific knowledge may be disseminated vigorously. Epistemic justification concerns how and in what way the truth of knowledge claims is justified and the criteria for knowledge to be true and/or a fact. Given this backdrop, the present study examined how individuals reacted to conspiracies in an uncertainty crisis (using the COVID-19 pandemic as an example). The mediating role of epistemic justification was investigated regarding its relationship between COVID-19 conspiracy beliefs and COVID-19 vaccine conspiracy beliefs. A cross-sectional study was conducted incorporating a multifactorial correlational design. Using convenience sampling, 690 participants (55.7% females, Mage=32.24years, SD=9.75) from different regions of Türkiye completed an online survey via Google Forms. The results demonstrated a strong and statistically significant correlation between beliefs in COVID-19 conspiracy theories and beliefs in COVID-19 vaccination conspiracy theories. The mediating effects of justification by authority and personal justification were statistically significant between COVID-19 conspiracy beliefs and COVID-19 vaccine conspiracy theories. Using the COVID-19 pandemic as an example, the present results indicated the complex relationships between conspiracy beliefs and epistemic justification. The present results indicate the importance of authorities intaking early action to provide scientific evidence and information to the public to avoid individuals believing false information.

How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?

In this study, we address the growing issue of misleading charts, a prevalent problem that undermines the integrity of information dissemination. Misleading charts can distort the viewer's perception of data, leading to misinterpretations and decisions based on false information. The development of effective automatic detection methods for misleading charts is an urgent field of research. The recent advancement of multimodal Large Language Models (LLMs) has introduced a promising direction for addressing this challenge. We explored the capabilities of these models in analyzing complex charts and assessing the impact of different prompting strategies on the models' analyses. We utilized a dataset of misleading charts collected from the internet by prior research and crafted nine distinct prompts, ranging from simple to complex, to test the ability of four different multimodal LLMs in detecting over 21 different chart issues. Through three experiments-from initial exploration to detailed analysis-we progressively gained insights into how to effectively prompt LLMs to identify misleading charts and developed strategies to address the scalability challenges encountered as we expanded our detection range from the initial five issues to 21 issues in the final experiment. Our findings reveal that multimodal LLMs possess a strong capability for chart comprehension and critical thinking in data interpretation. There is significant potential in employing multimodal LLMs to counter misleading information by supporting critical thinking and enhancing visualization literacy. This study demonstrates the applicability of LLMs in addressing the pressing concern of misleading charts.

AI Hallucination — Tips for Preventing Digital Delusions in Healthcare

The AI lexicon uses the term “hallucinations” to describe generative AI responses that provide inaccurate, false, and misleading information in AI chatbots. We must identify these hallucinations, whether mild deviations from facts or outright fabrications, and remove them from any content generated by chatbots. This underscores the necessity of fact-checking chatbot responses to ensure the accuracy and reliability of your content. This article explains how AI hallucinations occur and suggests ways to avoid and remove them.

The Effect of Fake News on Memory for True Events

ABSTRACTExposure to false information has the potential to impact how people encode subsequent, factual information that is related to the false information. In the current study, we propose an experiment in which participants read a true or false article about the relationship between the strictness of gun laws and gun violence in each state of the U.S. Afterward, stories of actual shootings are shown to participants. Half of the stories have details that are consistent with the false news article, and half of the stories have details that are consistent with the true news article. Following a brief distractor task, participants are asked to recall as much as they can about each story. We hypothesize that details that are consistent with the conclusion of the previously‐read article will be remembered better than inconsistent details.

False information recognition of social media platforms based on multi-modal feature fusion

False information recognition of social media platforms based on multi-modal feature fusion

Digital Navigation and Fact-Checking Practices Among First-Time Voters: A Digital Ethnographic Study of Social Science Students in Bengkulu, Indonesia

The participation of first-time voters in Indonesia's 2024 General Election presents new challenges in the digital era, particularly regarding digital literacy to counter misinformation and hoaxes. This study aims to identify the digital literacy skills of students from the Faculty of Social and Political Sciences (FISIP), University of Bengkulu, in addressing political misinformation. Utilizing a digital ethnographic method with a qualitative approach, data were collected through in-depth interviews, observations, and document analysis involving eight first-time voters aged 17–19 years. The findings reveal that social media platforms such as Instagram, TikTok, and Twitter are the primary sources of political information, despite their vulnerability to hoaxes. Respondents demonstrated basic skills in identifying false information, such as fact-checking through applications and engaging in group discussions. However, they were also exposed to algorithmic echo chambers that amplify information biases. The study concludes that strong digital literacy skills can enhance responsible political participation. Strengthening digital literacy programs is essential to support the integrity of democracy.

ANTI-CORRUPTION AUDIT OF COMPANIES AND FORESIGHT ANALYSIS IN TERMS OF THE STATE'S EUROPEAN INTEGRATION ECONOMIC POLICY

The article examines the current problems of spreading corruption in Ukraine. It has been established that corruption is a negative socio-economic phenomenon that restrains the state's economic development, reduces trust in the authorities and complicates the fight against Russia's military aggression. The coming years will be indicative for Ukraine's international partners in the context of assessing the effectiveness of national anti-corruption policy, and the degree of implementation of planned anti-corruption reforms, which will indicate the level of integration of European values into society and Ukraine's readiness to become a full member of the EU. The perception of the main corruption problems by the public and business was analyzed, and it was established that the general attitude of Ukraine towards corruption is shifting towards intolerance of this negative phenomenon. Based on the analysis of the dynamics of detected financial violations by the State Audit Service of Ukraine in the region for 2019-2023, the TOP-3 conditional regions that are prone to violations, including corruption, have been determined. A trend analysis of the volume of financial violations was carried out, which proved the average annual changes in the volume of the number of accrued violations for the years 2019-2023 and allowed us to make a forecast for 2026. The results of the foresight analysis show that the volume of financial violations in 2026 may reach the level of UAH 68,956.5 thousand. Accounting criminal and administrative violations by types of corruption during 2019-2023 were studied. The structural analysis of violations for 2019-2022 shows that the top three corruption methods included: an offer, a promise or the granting of an undue advantage to an official (21,9%); failure to take appropriate measures to combat corruption crimes (15,7%); declaring false information (12,8%). The analysis confirmed the significance of anti-corruption audits of companies and the formation of an anti-corruption independent audit system.

CURRENT REGULATIONS AND ANTICIPATIONS ON CRIMINALIZING BLACK CAMPAIGNS FOR THE 2024 REGIONAL HEAD ELECTIONS

Campaigning plays a critical role in elections, enabling candidates to present their vision, mission, and programs to the public. However, Law Number 8 of 2015, lacks explicit regulations on black campaigning, especially when it occurs via electronic media. This research highlight the Regional Head Election Law only indirectly addresses defamation in Article 69 (b) and (c), which prohibits insults targeting individuals, religions, ethnicities, races, groups, and candidates. Other relevant regulations, such as the Criminal Code and the ITE Law, contain provisions on defamation, hate speech, and incitement, but they are not spesifically regulate in the context of election. This paper examines the urgency of reformulating explicit prohibitions and criminalization of black campaigning within the Regional Head Election Law. Using a normative juridical approach to examines the existing regulations, this research suggests that an amendment to the Regional Head Election Law is necessary to explicitly define and prohibit black campaigning, establishing it as a formal offense and identifying relevant legal subjects. By setting clear prohibitions, this reform would strengthen fairness and integrity in elections, prevent the spread of false information and incitement, and align with the principle of legality, ultimately enhancing the democratic process.

Research Paper on AI-Driven Detection of Fake News in Non-Textual Content

In today's information ecosystems, there are serious issues due to the growing frequency of fake news in multimedia content, which includes images, videos, and audio. Non-textual content is still largely unexplored, despite the encouraging outcomes AI-driven algorithms have shown in identifying fake news in textual content. This work offers an extensive analysis of existing AI techniques for identifying false information in non-textual media, such as audio forgeries, modified photos, and deep fake movies. We hope to highlight the opportunities and constraints that currently exist in the field by examining cutting-edge methods like convolutional neural networks (CNNs), deep learning, and natural language processing (NLP) when paired with visual data. Additionally, we suggest that collaborative learning and multi-modal AI techniques could lead to advances. In the final section of the paper, ethical issues and potential research avenues for AI-driven false news detection in non-textual content are discussed.

Exploring social media strategies use during the presidential campaign of the 2023 general elections in Nigeria

Social networking during Nigeria's presidential campaign 2023 represented a significant era of heightened political engagement as social media became a crucial tool. This study critically evaluates the effectiveness and shortcomings of the social media strategies adopted by candidates, highlighting issues such as misinformation, cyberbullying, and the polarisation of the electorate. The research was grounded in conspiracy theory, employing quantitative and qualitative methods. A cluster sampling technique was employed to ensure a representative sample, dividing the population into six distinct clusters based on the six Area Councils of the Federal Capital Territory, Abuja, Nigeria. The Cochran formula was used to calculate a sample size of 385 respondents for the quantitative component of the study, and 18 participants were selected through purposeful and snowball sampling techniques. The study was directed by research questions that aligned with its objectives. Data collection was conducted using interview guides and questionnaires. The results were presented in tables, charts, and narrative formats. The findings reveal that social media hurt Nigeria's 2023 presidential election campaign. Studies reveal several challenges, including unreliable internet services, poor power supply, polarisation, and spreading false information. The study recommends the ethical use of social media platforms by candidates, their supporters, and all stakeholders to foster ethical practices and curb the dissemination of false and misleading information.

E-cigarette usage, awareness and harmful effects among dental students in Riyadh, Saudi Arabia

Background: This study examines the rising prevalence of e-cigarette usage among dental students in Riyadh, Saudi Arabia. Initially designed for smoking cessation, e-cigarettes have become popular for their diverse flavors, believed health advantages compared to traditional smoking, and acceptance in social settings. Nonetheless, increasing worries surround their possible negative impacts, particularly on dental well-being. The research seeks to evaluate the understanding, utilization habits, and awareness of negative consequences among dental students, who will have a crucial role in guiding patients in the future. Methods: The online survey was conducted on 419 dental students and interns in Riyadh, Saudi Arabia. Data collection was done using self-administered questionnaires that included age, e-cigarette use, reasons for use, health risks knowledge, and perceptions of effects on dental health. For exploring the association among academic level, gender, and awareness of e-cigarettes, Chi-square and logistic regression statistical analysis were used. Results: Peer influence was the primary reason for using e-cigarettes, reported by 49% (n=206) of participants. The most common reason given-peer influence-was reported by 44% (206 of 419) participants who had tried e-cigarettes. The majority of respondents, 82% (343 of 419), believed that e-cigarettes could be damaging to oral health, although some, 10% (41 of 419), were not aware of this possibility. A significant association was found between the level of education and awareness of the harmful effects of e-cigarettes. Conclusions: Our study shows that e-cigarettes are highly consumed among dental students, and there is a great need for awareness campaigns. Despite the high consumption, many students are aware of the potential health hazards of e-cigarettes, especially to oral health. Academic institutions should use educational programs that eliminate false information about e-cigarettes being a safer product and help young adults make informed choices.

The Development of Journalism in the Face of Social Media - A Study on Social Media’s Impact on a Journalist’s Role, Method, and Relationship to the Audience

This article explores how social media has changed journalism, with a particular emphasis on how it has affected reporters' roles, techniques, and interactions with audiences. Social media sites like Facebook, Instagram, and Twitter have given journalists new opportunities as well as difficulties [1]. These platforms' introduction has sped up news cycles and moved the emphasis from in-depth reporting to quick, snappy content creation [2]. As a result of this change, journalists must learn to work in a faster-paced news environment and handle the challenges of information overload and source verification [vi]. Both qualitative and quantitative research methodologies are used in this study. Ten journalists participated in in-depth interviews to collect qualitative data, which showed how social media has drastically changed traditional reporting practices by requiring a balance between accuracy and speed [12]. The spread of false information and the pressure to give sensationalism top priority in order to achieve high engagement metrics have been identified as major obstacles [13]. Variations in the frequency of audience interaction, the number of platforms used, and the reliance on social media for news sourcing are highlighted by quantitative analysis based on surveys of 250 journalists. According to the research, social media is heavily relied upon by journalists, with 27.2% of them utilising it for 41–60% of their news sourcing and differing degrees of audience interaction. "The ethical ramifications of social media in journalism are also examined in the study.'' While some journalists discover that social media improves audience engagement and speeds up the distribution of news, others worry about the detrimental effects on journalistic ethics and integrity. According to the study's findings, social media can increase interaction and immediacy, but it can also make it difficult to uphold ethical standards and accuracy.

Legal considerations regarding the responsibility of the mass media on the veracity of medical information disseminated to the public

The responsibility of the mass media on the veracity of information in the medical field disseminated to the public is a subject of real interest both for practitioners working within the health system and for media trusts, considering the numerous situations that have occurred so far in Romania in regarding the image damage caused to the medical staff as a result of the transmission of false or erroneous information regarding the activity carried out. In order to carry out this observational-prospective study, a questionnaire was prepared consisting of 10 closed questions with yes or no answers, which had the role of evaluating the patients' opinion regarding the degree of influence of their opinion on the activity carried out by the medical staff, as a result of the information transmitted in the mass media. After completing the completion period, the questionnaires were centralized and analyzed statistically. Most of the patients included in the study were of the opinion that there is a very high risk that the image and professional prestige of the medical staff will be affected when the information transmitted through the mass media is not in agreement with reality, which indicates the need of existence of solid legal levers through which press trusts can be held accountable when they commit such slips. The main conclusion of this study focuses on the identification and sanctioning of those active in the mass media every time there is a violation of the rules of a legal and ethical nature regarding the transmission of false/erroneous information related to the professional activity of the medical staff.