Fake News Research Articles

Implementation of a Health Communication Strategy in the Success of the COVID-19 Vaccine Program in West Jakarta

The research will focus on the West Jakarta Health Service's Communication Strategy for Disseminating the COVID-19 Vaccine. This research is inspired by the concern that the Jakarta COVID-19 vaccination achievement remains below target, particularly for the elderly, at 64.85 percent for the first dose and 58.44 percent for the second dose in October 2021. There are many obstacles such as hoax news and low public knowledge about vaccines. This research focuses on how to examine the Jakarta Health Office's communication strategy in inviting people to implement COVID-19 immunization. This research will expand on health communication concepts and theories by employing Push, Pull, and Pass communication tactics. A variety of doctors, and health professionals were interviewed for this study. The study results show that the push approach is implemented by opening direct vaccine centres, pulling through persuasively, door prizes, basic food packages, cooperation cross-sectors, and passes with public figure testimonials. The job of Dasawisma cadres is critical because they are the ones who directly persuade the people in simple languages like Javanese and Sundanese. The community also trusts the Dasawisma cadres because they are previously acquainted. The City of West Jakarta achieved 98% of the first dosage of the vaccination, and 65% dosage 2nd in December 2022. Keywords: Health communication, vaccine, communication strategies, COVID-19, Dasawisma.

Optimizing Commenting Ethics on Social Media: “Efforts to Create a Positive Digital Environment"

As the use of social media platforms increases, unethical commenting often leads to conflict and creates a hostile atmosphere in cyberspace. Optimizing the ethics of commenting on social media is essential to creating a positive digital environment. This socialization activity aims to build a digital communication culture that is more civilized, respectful, and beneficial to all—creating a positive social media space where every comment is a reflection of wisdom and empathy as a user. The methods used are talk, games, and focus group discussions regarding the ethics of commenting on social media. This event was attended by 30 Dasawisma XVI mothers aged 30-40 years. This activity results in Dasawisma XVI mothers actively using social media to understand and continue to practice valuable things in their comments. This results from Mrs. Rini conveying messages and impressions as Chair of Dasawisma XVI. The Dasawisma XVI mothers can also properly sort through or use media and behave appropriately if there is a risk of encountering hoaxes or false news. Keywords: Social Media, Ethics, Comment, Digital Environment, Communication Culture

Using machine learning models to detect artificially generated images

The use of machine learning models to classify real and artificially generated images is becoming an increasingly relevant area of research in the field of artificial intelligence. This paper is dedicated to the analysis and development of various machine learning models used for this task. Our main goal is to investigate the effectiveness of different neural network architectures in classifying images that have been created both in by human and with the help of artificial intelligence. As part of the study, we used nine different neural network architectures designed to analyse their effectiveness in classifying images. We studied both real images and those generated by artificial intelligence. The latter are becoming more and more common in the modern world, which means that there is a need for their identification and classification. The main results of our study include a detailed analysis of the characteristics of the generated images, as well as a comparison of the models using various metrics. We used metrics such as accuracy and F1-score, which allowed us to objectively evaluate the performance of each model. In addition, we identified the most effective models for the image classification task. One of the key findings of our study is that models that use data regularisation and augmentation performed better. Data regularisation and augmentation help to ensure more stable classification accuracy and reduce the tendency to overfitting, which is an important issue in machine learning. These results can be useful for developing strategies to counteract disinformation, which has become an issue in the modern information society. By using machine learning models, artificially generated images can be detected and separated from real images, which can help prevent the spread of fake news and disinformation. In addition, the results of our research can contribute to the further improvement of image generation algorithms, which is an important area of artificial intelligence development. They can serve as a basis for creating more accurate and efficient models that can generate images that are as close to real life as possible.

Data Perception and Information Disorder in the Italian Context During the Pandemic

This paper aims to learn how Italian audiences received and perceived data-driven television news during the Covid-19 pandemic. During this time, the logic and practices of creating and consuming television journalism were disrupted by the pandemic emergency. The paper presents the results of research carried out as part of a wider project titled “The Social Effects of Fake News” which was conducted from 2018 to 2022 within the CoRiS department of Sapienza University of Rome. The research project was reshaped in 2020, to understand the effects of what has been called “information disorder” in the areas of health, medicine, and science at a time when, together with the pandemic, an “infodemic” also emerged.1 We use Wardle and Derakhshan’s notion of “information disorder” as presented in their Council of Europe report.2 The results of the survey research we conducted show a surprising paradox. We found an interesting pattern of receiving and using information content based on data, in which users trust those who produce and validate certain data and at the same time do not believe that same data. This pattern of use indicates a particular approach to dealing with news among the Italian public. We have labelled it “know-it-all.” This article aims to deepen the understanding of this paradox of trust in experts but not in the data they deliver and how journalistic practice should deal with this conundrum.

Communication Networks and the Transnational Spread of Ethnic Conflict

How the interconnectedness of the global system influences the network of ethnic conflict communication is the focus of this work. The study aims at understanding how the analyses of ethnic conflicts gather momentum and spread throughout the world through the review of literature, case studies, and media coverage, social media issues, and deliberate disinformation campaigns. The study employs a qualitative approach, drawing on secondary sources and theoretical frameworks to explore hypotheses that talk about how media coverage is rising in proportion to the ethnic conflict’s transnational diffusion, how social media usage is connected with diaspora mobilisation and conflict extension, and how the purposeful spread of fake news increases the intensity and geographical scope of the conflict. The Rohingya crisis is also one of the best examples of the shifts in communication networks and their impact on the ethnic conflicts by influencing the international community’s perception and response. Communications networks may have the potential to escalate conflict and ‘spread’ wrong information, but they also have the potential to create awareness and tackle conflict. In the light of these observations, the study offers recommendations in the areas of codes of ethical media practices, regulation of use of social media, especially in multi-ethnic societies, to prevent incitement, and techniques of combating disinformation.

Solusi Al-Qur’an dalam Mengatasi Pengaruh Media Sosial TikTok Terhadap Keagamaan Generasi Muda

This research aims to determine the influence of TikTok social media on the religion of the younger generation from the perspective of the Koran. This research was conducted using the library research method, which includes analysis of literature such as the Al-Qur'an, hadith, journals, and related books. In the digital era, social media has become an important platform for the younger generation to express themselves and seek information. TikTok, as a popular application, allows users to create short videos with various interesting effects. However, its inappropriate use can have negative impacts, such as reducing valuable time, reducing a culture of shame, and spreading hoax news. This research found that TikTok has a significant effect on the religion of the younger generation, including the potential for antisocial behavior, wasting time, and decreasing Islamic values. To overcome these negative influences, the Qur'an provides important guidance, including commands to socialize well, make the best use of time, and maintain a culture of shame. This article offers a unique perspective on how the younger generation can use social media wisely and still retain their religious values ​​through the teachings of the Koran. Thus, it is hoped that this research can provide practical and theoretical benefits for the younger generation and the general public in overcoming the challenges of using social media in the digital era.

Navigating truth and disinformation: A comparative analysis of generational responses to the 6 February 2023 earthquake in digital media in Türkiye

This study aims to examine how different generations perceived and responded to news and disinformation about the February 6, 2023 Earthquake in Türkiye, focusing on their trust in news sources and methods of verifying authenticity. In this study, the data were collected from semi-structured interviews held with 30 participants using qualitative methods and they were analyzed with MAXQDA Analytics Pro 2022, through thematic analysis to uncover generational nuances in digital media engagement and trust. The analysis revealed five primary themes: digital media usage habits, trust and reliability in news sources, fake news verification practices, causes of fake news, and views on media legislation. The findings of the study indicated significant generational differences in digital media consumption habits. Notably, maintaining consistent online presence and integrating digital media into everyday life in Generation Z stood out as decisive factors in their reactions to news and disinformation about the February 6, 2023 Earthquake. The study also highlighted varied approaches among generations toward detecting disinformation. While Generation X preferred to use the methods of verification over broadcast media, Generations Y and Z showed a propensity for utilizing digital tools for identifying and verifying fake news. Attitudes toward media legislation differed among generations, yet there was a general consensus on the necessity of such laws to adapt to the digital age's challenges and play a crucial role in combating disinformation. This study offered a detailed comparative analysis on how different generations use digital media and their attitudes toward accuracy of news, particularly in response to significant events such as the February 6, 2023 Earthquake in Türkiye. This study would contribute to adopt a deeper understanding about the critical role of accurate information access during crises and the varying media consumption habits and attitudes toward disinformation across generations. The study emphasized the importance of tailored approaches in media literacy education and disinformation counter-strategies, as well as the need for media laws to be updated in accordance with the demands of the digital era.

The Influence of Fake News, Personality of Muslim Customers and Anger Against Hateful Behavior of Muslim Consumers at Brands Halal Products With Religiosity As Moderation Variable

This study aims to determine the role of religiosity in moderating the influence of fake news variables, the personality of Muslim customers, and anger towards the hateful behavior of Muslim consumers towards halal product brands. Primary data collection was carried out by purposive sampling with a total sample of 340 response questionnaires collected from consumers of fast food restaurant outlets (Kentucky Fried Chicken, McDonald's, and Pizza Hut) domiciled in Purwokerto, Tegal, Yogyakarta, Surakarta, and Semarang. The relationship between fake news variables, Muslim customer personality, and anger towards hateful behavior towards brands were analyzed using Structural Equation Modeling from the Warp Partial Least Squares regression program package. Empirical research shows the influence of fake news, Muslim personality, and anger on brand hatred. The test results show that religiosity can be a moderating variable between fake news, Muslim personalities, and anger towards brand hate for halal products. Hatred about the brand can be suppressed if the company provides information quickly regarding the products offered, and this news can be quickly verified through social media; the information provided is straightforward to understand, and consumers are always optimistic and able to provide good perceptions so consumers do not feel they hate food and beverages and can ensure a halal product.

Exploring the effects of location information on perceptions of news credibility and sharing intention

In recent years, the integration of location-based services into social media platforms has seen a significant surge, coinciding with the growing challenges posed by the proliferation of fake news online. However, the influence of location data on readers’ perceptions of online news credibility, particularly in relation to the reporters’ whereabouts, remains unclear. To investigate this relationship, we conducted a 3 (Topics: crime, science, health) × 2 (Location anchor: event-anchored or participant-anchored) × 4 (Proximity to location anchor - no, same, close-by or faraway location) mixed-method online study (N = 288) on Prolific. Our data collection involved presenting participants with news articles and assessing their credibility assessments and sharing intentions based on the proximity of those disseminating the news to both the subject matter of the news and the audience consuming it. Our findings reveal that the proximity of the reporter’s location to the readers’ location had a noticeable adverse impact on perceptions of news credibility and the likelihood of sharing it. Furthermore, we also identified a weak positive correlation between sharing intentions and trust in social media platforms. In addition, we observed that crime news were generally perceived as less credible compared to health and science news. Our research contributes significantly to a nuanced understanding of how location-based cues impact user behaviour when interacting with online news articles. Furthermore, it provides design insights for social media platforms aiming to enhance user trust and promote pro-social behaviours.

Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection

Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection

Climate change as fake news. Positive attribute framing as a tactic against corporate reputation damage from the evaluations of sceptical, right-wing audiences

PurposeThe paper aims to examine whether CSR communication about a company’s support for climate change created using different content framing categories (positive vs negative) can lead climate change-sceptical audiences to positively influence their evaluations of the credibility of CSR communication, of a company and its actions, and lead to higher purchase intentions.Design/methodology/approachThe paper used an experimental design. About 266 respondents recruited via the Prolific platform were invited to participate in an online study. A between-subject design was used, and data was analysed using the bootstrapping technique, allowing to identify moderators of the relationship between CSR communication framing and different evaluations of a company.FindingsThe paper provides empirical support for the role of political preferences and climate change beliefs in predicting the preference for positive attribute framing among climate change sceptical audiences. It is argued that climate change sceptics are still in the process of deliberation about whether climate change is occurring.Research limitations/implicationsThe research findings may not be generalizable to countries where support for climate change is low, and a technique like attribute framing may not lead to noticeable differences in message reception.Practical implicationsThe paper underscores the impact of the type of attribute framing in CSR communication on different aspects of company evaluations depending on beliefs in climate change. Commercial communicators should additionally invest in climate change education to address the climate change challenge.Social implications Addressing climate change effectively requires support from companies to communicate their CSR efforts purposefully and to address climate change sceptical audiences.Originality/valueThe paper identifies beliefs in climate change as an important moderator of CSR communication attribute framing effectiveness.

Gendered disinformation in Spanish-language fact-checking: origin, methodology, and perspectives

The recent surge in disinformation targeting gender themes poses a substantial threat to women’s equality in democratic systems. This study investigates the efforts of three fact-checking platforms prominent in Ibero-America (Maldita.es and Newtral in Spain and Chequeado in Argentina) in countering antifeminist fake news. Employing a dual methodology, we conducted a content analysis of pieces published on gender issues over one year and semi-structured interviews with representatives from these platforms. The findings unveil a consistent production of texts addressing contemporary feminist affairs. Disinformers target both the feminist movement and its historical grievances, including gender-based violence. Fact checks emerge as the primary format for combating these hoaxes, with explainers gaining against increasingly complex disinformation narratives. The verification methodology emphasizes consulting official sources and expert testimony. Interviewees acknowledge that there is still much progress to be made in gender-related fact-checking.

Types of Knowledge in Post-Scientistic Society

At times of informational deluge, the man in the street is especially vulnerable to the harmful influence of media, to which not only closed groups of people sharing the same beliefs and views contribute but also bots and trolls spreading fake news in the form of conspiracy theories, misinformation, disinformation and propaganda. This audience without proper education and not reflecting on the information gained, can spread more information of this type. Scholars closed in their own reality, conceived of only their inerrancy not only are unable to support, but also are not conducive to the development of science. They also vilify people having different views and the representatives of other disciplines, which they consider inferior. This introduction presents the value of different types of knowledge, not only the scientific ones, and shows its importance for people who may not have other possibilities to learn and understand true science which indicates the meaning of knowledge, which is accessible for people without an academic education. Different types of knowledge, even the seemingly non-scientific type, are the basics for human development and learning. The key to this development, however, is teaching critical thinking. Essential for any change is also the proper choice of academic powers, appreciating and supporting researchers who really affect science, apart from the closed environment of faithful fanatics, and who are able to do science at a global level.

Extracting Features from Text Flows based on Semantic Similarity for Text Classification: an Approach Inspired by Audio Analysis

Text classification is a mainly investigated challenge in Natural Language Processing (NLP) research. The higher performance of a classification model depends on a representation that can extract valuable information about the texts. Aiming not to lose crucial local text information, a way to represent texts is through flows, sequences of information collected from texts. This paper proposes an approach that combines various techniques to represent texts: the representation by flows, the benefit of the word embeddings text representation associated with lexicon information via semantic similarity distances, and the extraction of features inspired by well-established audio analysis features.In order to perform text classification, this approach splits the text into sentences and calculates a semantic similarity metric to a lexicon on an embedding vector space. The sequence of semantic similarity metrics composes the text flow. Then, the method performs the extraction of twenty-five features inspired by audio analysis (named Audio-Like Features). The features adaptation from audio analysis comes from a similitude between a text flow and a digital signal, in addition to the existing relationship between text, speech, and audio. We evaluated the method in three NLP classification tasks: Fake News Detection in English, Fake News Detection in Portuguese, and Newspaper Columns versus News Classification. The approach efficacy is compared to baselines that embed semantics in text representation: the Paragraph Vector and the BERT. The objective of the experiments was to investigate if the proposed approach could compete with the baselines methods improve their efficacy when associated with them. The experimental evaluation demonstrates that the association between the proposed and the baseline methods can enhance the baseline classification efficacy in all three scenarios. In the Fake News Detection in Portuguese task, our approach surpassed the baselines and obtained the best effectiveness (PR-AUC = 0.98).

The Importance Of Public Relations In The Post-Truth Era And The Strategies Followed

‘Fake news’, which are generally created for a specific purpose on different topics, spread rapidly through social media platforms and search engines and prevent the public from accessing real information. Throughout history, fake news and misinformation have been used for various purposes. However, with the introduction of social media into our lives, the dissemination of unproven information or fake news has accelerated, and such content has reached large masses in a short time through both professional content providers and users. Despite the fake news about health and nutrition, raising awareness is a prerequisite for creating healthy societies that consume and eat consciously, and this can be made easier with sustainable public relations. In this study, the function of verification platforms in preventing such information disseminated in the new media environment in the post-truth era is examined. Within the scope of the study, the results of the verification platform teyit.org on health and nutrition claims were analysed. In the study, it was observed that only four out of 14 claims on health and nutrition shared on the internet and social media were found to be true information, and that such information was mostly disseminated through social media.

Knowledge-aware multimodal pre-training for fake news detection

Amidst the rapid propagation of multimodal fake news across various social media platforms, the identification and filtering of disinformation have emerged as critical areas of academic research. A salient characteristic of fake news lies in its diversity, encompassing text–image inconsistency, content–knowledge inconsistency, and content fabrication. However, existing endeavors are generally tailored to a specific subset of fake news, leading to limited universality. Moreover, these models primarily rely on scarce and exorbitant manually labeled annotations, which is incapable of providing sufficient learning signals to detect a variety of fake news. To address these challenges, we propose a novel knowledge-aware multimodal pre-training paradigm for fake news detection, dubbed KAMP. Our motivation lies in incorporating unsupervised correlations through pre-training tasks as complementary to alleviate the dependency on annotations. KAMP consists of a novel multimodal learning model and various delicate pre-training tasks to simultaneously capture valuable knowledge from single modality, multiple modalities, and background knowledge graphs. Our proposal undergoes comprehensive evaluation across two widely utilized datasets, and experimental results demonstrate the superiority of our proposal.

Modeling the Diffusion of Fake and Real News through the Lens of the Diffusion of Innovations Theory

These days, people have increasingly used social media as a go-to resource for any information need and daily news diet. In the past decade, the news ecosystem and information flow have been dramatically transformed by the popularity of such platforms. Social media users can, in fact, easily access nearly any kind of information and then spread it nearly without friction through activities such as tweets/retweets in Twitter (now X) and similar means on other social media. This seemingly innocuous activity of spreading information has a collective consequence of making social media users responsible for radical changes in the way news is distributed, including both authentic and fake news. Moreover, malicious individuals have been implicated in capitalizing on the ease of introducing and spreading information in these platforms to create misinformation, spread it to a wider audience, and subsequently influence public opinion on important topics through information diffusion. Therefore, understanding the factors that motivate a user’s decision to share is of paramount importance in understanding the information diffusion phenomenon in social media. In this article, we propose an approach based on the Diffusion of Innovation theory to model, characterize, and compare real and fake news sharing in social media with a focus on different levels of influencing factors including innovation, communication channels, and social system. We apply that approach to identify factors related to the spread of fake news as they relate to users, the structure of news items themselves, and the networks through which news is circulated. We address the problem of predicting real and fake news sharing as a classification task and demonstrate the potentials of the proposed features by achieving an AUROC of around 0.97 and an average precision ranging from 0.88 to 0.95, consistently outperforming baseline models with a higher margin (at least 13% of average precision). In addition, we also found out that empirically identifiable characteristics of news items themselves and users who share news are the strongest element allowing accurate prediction of real and fake news sharing, followed by network-based features. Moreover, our proposed approach can be effectively used to model news diffusion as a multi-step propagation process.

Can You Tell Real from Fake Face Images? Perception of Computer-Generated Faces by Humans

With recent advances in machine learning and big data, it is now possible to create synthetic images that look real. Face generation is often of particular interest, as faces can be used for various purposes. However, improper use of such content can lead to the dissemination of false information, such as fake news, and thus pose a threat to society. This work studies whether people believe the truthfulness of faces using eye tracking and self-reports, including free-form textual explanations when participants encounter real and computer-generated faces. We used three different datasets for our evaluations, and our experimental results show that while people are relatively better at identifying the truthfulness of real faces and faces generated by earlier machine learning algorithms with different gazing behaviors in viewing and rating phases, they perform less accurately when deciding the truthfulness of synthetic face images that are generated by newer algorithms. Our findings provide important insights for society and policymakers.

From pen to algorithm: optimizing legislation for the future with artificial intelligence

AbstractThis research poses the question of whether it is possible to optimize modern legislative drafting by integrating LLM-based systems into the lawmaking process to address the pervasive challenge of misinformation and disinformation in the age of AI. While misinformation is not a novel phenomenon, with the proliferation of social media and AI, disseminating false or misleading information has become a pressing societal concern, undermining democratic processes, public trust, and social cohesion. AI can be used to proliferate disinformation and misinformation through fake news and deepfakes; can AI also be used for beneficial purposes to develop the antidote legislation combatting these challenges? Leveraging the capabilities of LLMS, such as ChatGPT and others, can present a promising direction for optimizing legislative drafting. By proposing the methodological approach of an AI bun, this article explores an important approach in which LLMS can support lawmakers and policy experts in crafting legislation. The article contributes to the discourse through a nuanced understanding of the opportunities and challenges in harnessing LLM-powered tools for legislative innovation. Ultimately, it underscores the transformative potential of LLMs as a potential resource for lawmakers seeking to navigate decision-making while developing legislation on an example of navigating the intricate landscape of misinformation and disinformation regulation in the age of AI.

Role of Social Media Fake News in Directing Voting Behavior: A case study on the 2020 U.S Presidential Elections

Role of Social Media Fake News in Directing Voting Behavior: A case study on the 2020 U.S Presidential Elections