Misinformation Research Articles

Drug advertisements in daily newspapers: a case study with the Hannoversche Allgemeine Zeitung (Hannover General Newspaper).

The Hannoversche Allgemeine Zeitung (HAZ, The Hannover General Newspaper) was launched in 1949 and is the largest daily newspaper in Lower Saxony, Germany. Drug advertisements in daily newspapers are common but have not yet been the topic of scientific investigation. This study analyzed all 181 medical advertisements published in the HAZ issued in 2021, all of which promoted over-the-counter medicines. Information was extracted from each advertisement and checked for selected parameters relating to the general structure of an advertisement, the content of the advertisement and the medical products themselves. The Therapeutic Products Advertising Act (Medicines Advertising Law; Heilmittelwerbegesetz (HWG)) of 1965 regulates the handling of medical advertisements through mandatory information and prohibitions. All advertisements were checked for compliance with the Act. The Therapeutic Products Advertising Act is loosely worded and poorly implemented, i.e., in only 52% of the advertisements mandatory information was included and only 69% of the advertisements avoided the prohibitions of the law. Furthermore, there is a clear connection between the newspaper's readership, common diseases in the German population and the indications for the preparations. The most common indication was joint pain with 22.65%, this correlates with the prevalence of the disease in Germany and the age of the newspaper's readership. In most cases, scientific evidence of the effectiveness of the preparations was lacking and only 46 of the 181 advertisements mention clinical studies. All in all, this paper gives an insight into the world of medical advertisements in a German newspaper, where marketing mechanisms, the kind of the consumer and the prices of the advertisement play a far more bigger role than scientific evidence. The Medicines Advertising Law has many flaws that are exploitedby the drug companies to address the readers of the newspaper. Thelaw needs to be stricter and better controlled to protect the layman from false medical information.

CHALLENGES OF THE USE OF SOCIAL MEDIA IN ENHANCING NATIONAL SECURITY IN NIGERIA, 2015-2024

The sites used for interaction online are known as social networking sites and they include; Facebook, Twitter, YouTube, Yahoo messenger, Google talk, Google messenger, Instagram, Snapchat, among others. Social networking is a welcome development because of its numerous benefits such as the speed at which information is disseminated, enhancing learning opportunities and so on. On the other hand, the disadvantages of social media abound and they include lack of privacy and sharing false information about themselves and others. Such information could lead to insecurity in Nigeria. So, this study analyzed the challenges of the use of social media in enhancing national security in Nigeria. Premised on the structural-functionalism theory, the study employed questionnaire and in-depth interviews conducted among communications and security experts in Nigeria. Findings of the study revealed that Social Media are very difficult to control; rather they are controlling the users because of the freedom and absence of law guiding the cyberspace. The increasing use of Social Media has led to greater privacy challenges. Other challenges found are lack of coordination and strategic planning, political interference, lack of defined structures by security agencies, collapse of rule of law institutions and misinformation, disinformation and seditious messages. To curb the ongoing fake news, attack rumors, propaganda and so many others, the national security agents and others aligned agencies must be able to deploy different kinds of mechanisms to gather information, like through news gathering, online surveillance, information sharing, public diplomacy and many others. The Nigerian government and individuals should stop drawing conclusions from the information they see and read on SM.

Misinformation and Digital Inequalities: Comparing How Different Demographic Groups Get Exposed to and Engage with False Information

ABSTRACT This study examines the extent to which digital inequalities, often associated in the literature with certain social demographics (e.g. age or place of residence), shape media users’ exposure to and engagement with misinformation. Using data from 24 focus group discussions conducted in six African countries (Ghana, Kenya, Nigeria, South Africa, Zambia, and Zimbabwe), we describe media users’ diverse experiences with false information and discuss whether these experiences can be better understood if we take into account inequalities in access to digital technology and digital literacy skills. We find that, while some older media consumers living in rural areas say they feel less exposed to misinformation online, they still come across online falsehoods through other means. We also show that this group is often perceived by others as the most vulnerable to misinformation because of assumed lower digital literacy skills. Our data, however, call for a problematization of this view, given that critical consumption of the media is prevalent among older adults. We conclude by highlighting the need for nuanced and targeted interventions to combat the spread of misinformation that consider the diverse range of experiences of media consumers in the countries under study.

Unveiling temporal patterns in information for improved rumor detection

Rumor detection is a critical task for addressing the spread of misinformation and maintaining the credibility of information sources. Natural Language Processing (NLP) techniques have been employed to propose efficient and effective methods for rumor detection. In the wake of the widespread COVID-19 pandemic, the world has faced extensive strain on health, economics, and social structures. The dissemination of false or inaccurate information on social media, whether intentionally malicious or unintentional, has had detrimental consequences for individuals and society, particularly during critical situations like real world emergencies. In this study, we aim to explore the textual and temporal features present in social media posts (specifically tweets) related to COVID-19 to detect rumors as time is unique feature of text and any event can be mapped on timeline. Previous studies utilized the textual features and the temporal features are neglected at large for rumors detection. We utilize both temporal and textual features independently, as well as in combination, to train machine learning and neural network models. The evaluation of multiple algorithms (RNN, LSTM, CNN, DNN, BERT) across various feature sets reveals diverse performance. RNN and LSTM improve with combined textual and temporal features, highlighting temporal information’s importance. CNN performs well with textual features but declines with temporal features. DNN maintains consistent performance, while BERT demonstrates moderate effectiveness in classification tasks.

Navigating Crisis: The Dynamics of Global Communication Response in Indonesia

This study examines the complexities of crisis communication in the context of Indonesia, considering the nation’s vulnerability to a range of crises, such as health problems, political upheaval, and natural catastrophes. Using a mixed-methods approach, the study combined social network analysis, surveys, content analysis, and qualitative interviews to give a thorough knowledge of the dynamics of crisis communication.Important conclusions emphasize the important of social media and other international communication channels to coordinate crisis response and speedy information distribution. However, obstacles, e.g., the dissemination of false information, highlight the need for stronger approaches to information management. Furthermore, the practices of crisis communication in Indonesia are heavily influenced by contextual, social, and cultural aspects. Communication is influenced by language diversity, cultural standards, and religious sensitivity techniques, highlighting the significance of cultural sensitivity and comprehension of regional situations. The findings have implications for stakeholders, practitioners, and policymakers who are active in communication and crisis management. Enhancing public education and awareness campaigns, strengthening international cooperation to fortify crisis response skills, and boosting coordination among government institutions are among the recommendations. Although the study offers insightful information about crisis communication in Indonesia, it is important to recognize its limits and the dynamic character of crisis communication to guide future research efforts. The overall goal of this research is to contribute to the development of better crisis management and resilience building techniques in Indonesia and beyond.

Social Media and Plastic Surgery, the Good, the Bad, and Where are We Going?

In recent decades, social networks have experienced an exponential increase involving millions of users and transforming the way they communicate in the medical community, becoming an important tool in medicine, where plastic surgery has not been an exception. We aimed to present a review of the effects of social networks in the area of plastic surgery, the main existing regulatory frameworks, and propose the basic aspects for the adequate use of these platforms in the practice of our profession. A literature search was performed, including scientific articles, specific existing guides from the USA, Australia, Brazil, and Argentina, as well as articles in digital newspapers and Google regarding emblematic cases. These platforms are an important source of information and communication in medicine and are frequently used by different scientific societies, mainly for educational and dissemination purposes. In the area of plastic surgery in particular, there is a high presence of specialists in the field who use these tools mainly for orientation, education, promotion of their brand, and peer-to-peer relationships. The main negative aspects of social networks are a high presence of publications generated by non-professionals in the field, false information, and the generation of esthetic violence. The regulations of several existing scientific societies, although heterogeneous on some points, mostly agree that the need for informed consent, protection of patient privacy and safety, truthfulness of the information published, and patient education campaigns are key points. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A new era in early childhood education (ECE): Teachers’ opinions on the application of artificial intelligence

Abstract This study aimed to evaluate the role of AI in preschool from the perspective of preschool teachers. The research group consisted of 101 preschool teachers. The study group was selected according to the easily accessible case sampling method, one of the purposeful sampling methods. The study was a phenomenology, which is a qualitative research design. Qualitative data were collected using a semi-structured interview form. Data were analyzed using inductive content analysis. As a result of the analyses, the participants stated they did not feel sufficient regarding Al literacy and competencies. The participants also emphasized that using AI in preschool was appropriate and would improve children’s skills, such as AI literacy and computational thinking. Moreover, they stated that using AI in preschool would cause privacy and security concerns for different reasons, such as not protecting personal data, using children’s pictures, and providing false and misleading information. The participants stated that they were worried about the implementation process of Al due to lack of content knowledge, lack of infrastructure, physical structure of the classroom, and lack of materials. In addition, participants emphasized that if the existing concerns were eliminated, AI could be easily integrated into the preschool period. In addition, it was determined that most participants had problems in modeling and drawing a model related to AI.

Evaluating BERT-based language models for detecting misinformation

Abstract Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further.

Is the content of social media on physiotherapy assessment and treatment for people with low back pain evidence-based?

Background: Clinicians increasingly seek professional information on social media platforms whose there is no regulation or revision before the information been published. Objective: This study aimed to characterize and qualify the content published in social media regarding the diagnosis and conservative treatment of low back pain. Methods: Three researchers collected posts from YouTubeTM, InstagramTM, and TiktokTM in Portuguese, English, and Spanish using the terms "dor lombar,” "low back pain," and "dolor lumbar.” The researchers collected 100 videos each per language and analyzed their content and quality, comparing them with recommendations from clinical guidelines for low back pain. Content analysis of the posts was conducted considering: assessment, which represented a total score of up to 6 points, and treatment, with a score of up to 9 points. The maximum score for content analysis was 15 points. Results: Overall, 115 videos were analyzed from YouTubeTM, 54 from InstagramTM, and 116 from TikTokTM. Most of the posts of YouTubeTM, InstagramTM, and TikTokTM were poor about quality of information on the assessment and treatment of low back pain. Conclusion: The dissemination of outdated or false information on social media platforms with a global reach could produce false beliefs regarding the effectiveness or ineffectiveness of techniques. Health professionals should not update themselves using social media information.

MEDIA LITERACY AS A FACTOR IN COUNTERING DISINFORMATION AND PROPAGANDA IN THE DIGITAL ENVIRONMENT

The article examines media literacy as a key tool for users’ resistance to misinformation in the modern digital world. The author identifies the characteristic signs of false information and methods of its dissemination, analyzes the effectiveness of various methods of checking news and provides recommendations on the formation of media literate behavior in Russian society.

Criminal Sanctions for Online Gambling Offenders

The advancement of digital technology has significantly impacted various forms of crime, including increasingly complex online gambling facilitated through trading and investment platforms. The case of Doni Salmanan, who promoted the Quotex platform via social media, resulting in victim losses of IDR 24.3 billion, illustrates how binary option trading platforms can be misused as covert gambling tools in the digital era. This study examines two primary issues: the factors driving offenders to engage in online gambling crimes and the judicial considerations in imposing criminal sanctions, based on Court Decision No. 576/Pid.Sus/2022/PN Blb. Understanding these issues is crucial for analyzing the role of law enforcement in addressing online gambling cases disguised as investments. This research employs normative and empirical juridical approaches, utilizing literature reviews and interviews with district and high court judges. A qualitative juridical analysis was conducted on relevant regulations, particularly Law No. 11 of 2008 on Electronic Information and Transactions. The findings indicate that offenders are motivated by easy internet access and profit opportunities as affiliates, exacerbated by weak regulation of binary option platforms. The court sentenced the defendant to four years in prison and fined IDR 1 billion for disseminating false information harmful to consumers but did not classify Quotex as an online gambling platform due to the absence of clear regulations. The study recommends strengthening regulations for binary option platforms, enhancing inter-agency coordination in law enforcement, and educating the public on the risks of illegal investments as potential gambling channels.

High performance fake review detection using pretrained DeBERTa optimized with Monarch Butterfly paradigm

In this era of internet, e-commerce has grown tremendously and the customers are increasingly relying on reviews for product information. As these reviews influence the purchasing ability of the future customer, it can give a positive or negative impact on the businesses. The effectiveness of online reviews is compromised by fake reviews that provide false information about the product. Fake reviews can not only impact the reputation of the businesses but also involve financial losses. Thus, detection of fake reviews is essential to solve the problem for maintaining the integrity of online reviews. Existing Machine learning models often struggle with deep contextual understanding. Scaling machine learning models while maintaining accuracy and efficiency becomes increasingly challenging as the volume of online reviews continues to grow. Hence, this research work introduces a novel MBO-DeBERTa, a deep neural network with Monarch Butterfly Optimizer. The proposed model improves the capacity to differentiate between overlapping characteristics of fake and authentic reviews. MBO-DeBERTa attained a classification accuracy of 98% for detecting the fake reviews. The proposed framework is tested on three different datasets such as Amazon, Fake Review and Deceptive Opinion Spam containing 21000,40000 and 1600 reviews respectively which are publicly available in Kaggle. The proposed model also detects adversarial attacks using the Fast Gradient Sign Method (FGSM) and thereby evaluating its resistance to such attacks and noise. The proposed model was also tested on the unseen data of Myntra and Amazon verified customer reviews and our model works efficiently for real world data. Thus the results show that the suggested model outperforms the current models showing increased accuracy, precision, recall, F1 score and reduced loss rate.

Research on the Application of AI Technology in Short Video News and Media Ethical Anomie under Field Theory

In the era of intelligent media, the connection between news production and digital media has become increasingly deeper and has penetrated into the stages of news gathering, production, presentation and distribution. While technology facilitates news production, it also brings new ethical challenges to the news industry, such as false news, information flooding, deep falsification and pan-entertainment of news. On the basis of the application status of AI technology in the progress of news production, combined with Bourdieu's field theory, this paper discusses media ethical anomie in short video news from the perspectives of four fields—economy, politics, culture and technology—and from the two factors of habitus and capital of actors in the field. This paper analyzes the causes of media ethics anomie and proposes countermeasures. The use of technology has both advantages and disadvantages. During this process, human beings should occupy the main position and use technology rationally. Meanwhile, as a comprehensive field, the governance of the news industry needs the cooperation of multiple subjects to jointly create a good news environment.

The Authentic Deed that is Degraded due to False Information

An authentic deed is known as the strongest and most legally binding evidence in legal proceedings. However, its integrity can be compromised when fraudulent data is incorporated, leading to the degradation of its evidentiary value. This research aims to analyze the legal implications of authentic deeds that have been degraded due to data falsification and to explore the notary’s responsibility in such cases. This research used normative juridical approach, this study examined statutory regulations, court decisions, and legal doctrines related to document authenticity. The findings indicate that an authentic deed containing falsified data may lose its status as a perfect piece of evidence and be downgraded to an underhand deed, depending on the extent of the forgery. Additionally, notaries may be held accountable if negligence is proven in verifying the validity of the data. This research contributes to a better understanding of the legal protection mechanisms for parties involved in notarial transactions and highlights the importance of strict verification procedures to uphold the authenticity of legal documents.

Belief in Science‐Related Conspiracy Theories

ABSTRACTConspiracy theories attempt to explain events and circumstances by attributing them to the supposed secret actions of powerful, malevolent groups. Due to their associations with potentially harmful non‐normative behaviors at both the individual and collective levels, researchers have expressed particular concern over conspiracy theories that malign science. To better understand such beliefs, we conducted a national US survey to gauge respondents’ agreement with 11 science‐related conspiracy theories and their political, psychological, and social characteristics. We find that beliefs in specific science‐related conspiracy theories represent two unique factors that are (i) related to non‐normative behaviors including political violence, vaccine refusal, and sharing false information online, and (ii) undergirded by a range of non‐normative personality traits and attitudes. We conclude by discussing the potential role of political leaders in propagating science‐related conspiracy theories and the implications for preventing or reversing science‐related conspiracy theory beliefs.

Criminal Law Arrangements in Handling the Spread of Social Media Bot-Based False Information in Indonesia

This study examines the legal arrangements in Indonesia for handling the spread of false information through social media bots, employing a normative juridical analysis. The proliferation of disinformation through automated bots has become a significant challenge to public trust, social cohesion, and democratic processes. This paper explores the adequacy of existing legal frameworks, such as the Information and Electronic Transactions Law (ITE Law), the Indonesian Penal Code, and the Election Law, in addressing bot-generated false information. The study identifies key challenges, including technological limitations, legal ambiguities, and jurisdictional issues, and compares Indonesia’s regulatory approach with that of the European Union and the United States. The research highlights the need for clear legal definitions, enhanced platform accountability, improved enforcement capabilities, and greater international cooperation to effectively address this issue. The study concludes with recommendations for legal reforms and increased public awareness to mitigate the adverse impact of social media bots on public discourse in Indonesia.

University students' reactions to ChatGPT: Enhancing academic English writing and addressing challenges

This study investigates university students' reactions to using ChatGPT to support writing academic English. Data were collected from 200 students across two universities using a survey. Responses showed that most students used the tool regularly, expressing high satisfaction with its contributions to grammar correction, sentence structure improvement, idea generation, and overall coherence. On the other hand, many students reported some challenges, such as misinterpretation of prompts, false information, and lack of personal writing style. Additionally, students overwhelmingly supported integrating ChatGPT into writing classes, emphasizing the need for structured guidance. While ethical concerns about over-reliance and misuse were raised, the majority opposed strict limitations on its use in academic contexts. The study concluded that ChatGPT is a valuable tool that should be integrated into writing curricula with structured guidance and ethical training.

Implementation of Ethical Artificial Intelligence Law to Prevent the Use of AI in Spreading False Information (Deepfake) in Indonesia

The rapid advancement of artificial intelligence (AI) technologies has introduced new challenges, particularly in the creation and dissemination of deepfakes—manipulated media that can deceive viewers into believing false information. In Indonesia, the existing legal frameworks do not specifically address the issue of deepfakes, leaving a gap in the regulation and prevention of AI-generated misinformation. This paper analyzes the legal application of AI ethics in preventing the misuse of deepfakes, using a normative juridical approach to examine current Indonesian laws, ethical standards in AI, and international legal frameworks. The findings highlight the insufficiency of Indonesia's current legal provisions, including the Electronic Information and Transactions (ITE) Law and the Personal Data Protection Law, in addressing deepfake-related issues. The study proposes the introduction of a specific legal framework for deepfakes, integration of AI ethics into national legislation, and international collaboration to mitigate the spread of harmful AI-generated content. By implementing these reforms, Indonesia can better safeguard individuals’ rights and maintain digital media integrity.

Artificial Intelligence Tools in Misinformation Management during Natural Disasters

Abstract Ensuring accurate information during natural disasters is vital for effective emergency response and public safety. Disasters like earthquakes and hurricanes often trigger misinformation, complicating response efforts and endangering lives. Historical events, such as Hurricane Katrina and the COVID-19 pandemic, illustrate the harmful impact of false information. Artificial intelligence (AI), with technologies like natural language processing and machine learning, offers promising solutions for detecting and mitigating misinformation. This paper explores AI’s role in managing misinformation during disasters, highlighting its potential to improve disaster response, enhance public trust, and strengthen community resilience.

Factors contributing to vaccine hesitancy and potential solutions to increase vaccination rates and public confidence in vaccination: A Cross-Sectional Study from Madhepura District of Bihar

Background: Vaccine hesitancy is characterised as the delay in the adoption or outright rejection of vaccines notwithstanding their accessibility constitutes a substantial risk to public health. Comprehending the fundamental reasons for vaccine hesitancy and its repercussions is essential for formulating successful methods to enhance rates of vaccination and public confidence in vaccinations. Aim: This investigation seeks to examine the factors contributing to vaccine hesitancy and its repercussions, as well as to assess options for improving vaccination rates and public confidence in vaccinations within a designated community. Materials and Methods: A cross-sectional investigation was performed. 150 participants in all were selected at random and assessed using validated questionnaires and structured interviews. In order to look at correlations and find predictors of vaccine reluctance, data were analysed using SPSS version 23.0, which used logistic regression, chi-square tests, and descriptive statistics. Results: The research indicated that 35% of individuals demonstrated vaccine reluctance, with notable correlations identified with age, educational attainment, and employment position. Advanced education correlated with reduced vaccine reluctance (OR = 0.449, p = 0.0449), whereas unemployment heightened the probability of hesitancy (OR = 1.649, p = 0.0449). Confidence in medical practitioners was elevated, whereas reliance on social media for vaccine information was minimal. Individuals concerned about vaccines were markedly more prone to missing planned shots (p < 0.001). Conclusion: Vaccine reluctance is affected by socioeconomic and educational characteristics, carrying substantial public health consequences. Initiatives to mitigate reluctance should concentrate on educational strategies and the resolution of socioeconomic obstacles. Strengthening trust in medical practitioners and addressing false information on social media are critical tactics to elevate immunisation rates. Recommendations: Public health measures must emphasise focused educational initiatives, provide access to credible vaccine information, and tackle socioeconomic inequalities. It is advisable for and community leaders to collaborate in order to establish public trust and combat vaccine disinformation. Keywords: Vaccination Rate, Vaccine Hesitancy, Socioeconomic Factors, Public Health