Fake News Research Articles

Confronting Fake News Through Collective Intelligence in Adolescents

Adolescents are vulnerable to the exponential increase in fake news spread. Collective judgment on the quality of news has been an effective intervention. Collective Learning platform makes it possible to apply collective intelligence through simultaneous interaction in a large group, similar to social networks. The objective of this article is to analyze whether this platform effectively improves how adolescents confront fake news. 360 Spanish students aged 15–16 years old participated in the experiment confronting a fictitious case of fake news. The quantitative analysis using ANOVA demonstrated that there were significant positive changes for addressing fake news, both overall as well as in each of the participants. We discuss how these results confirm that collective intelligence improves digital literacy in the Internet challenges.

Analyzing common lexical features of fake news using multi-head attention weights

Numerous approaches have been developed to identify fake news through machine learning; however, these methods are predominantly assessed using singular datasets specific to certain fields, leading to a scarcity of research on versatile models adaptable to a range of domains. This study evaluates the adaptability of a fake news detection model across diverse fields, employing three distinct datasets. Furthermore, the study leverages the multi-head attention feature of bidirectional encoder representations from transformers (BERT) to scrutinize the feature extraction process in the model. In our analysis, we focused on words that are commonly emphasized by machine learning in fake news detection. The dataset comprised 27,442 instances of genuine news and 28,359 instances of fabricated news, each distinctly labeled. To examine the focal words, we utilized multi-head attention, a component of BERT. This mechanism assigns greater weight to words that receive more attention. Our investigation aimed to identify which words were assigned higher weights in each article. The findings indicate that while representing a minor percentage, a common characteristic of fake news is the heightened attention to words that influence the credibility of the article. To assess the versatility of the model, we applied the model trained on one dataset to classify other datasets. The results demonstrate a notable decline in accuracy, attributable to the distinctive characteristics of the training data. These observations suggest that common features among fake news, which could be extracted using the fine-tuned BERT model, are limited.

AI For Real-Time Detection And Mitigation Of Fake News Via Multi-Source Verification

The drought of fake news in the cover of the digital age makes information quality and its influence by senses. To address this issue, this paper introduces an innovative process to counter the misinformation spread by designing a real-time detection and mitigation model which is based on an AI Environment. Existing solutions do postpublication fact-checking, but our proposed system performs cross-verification of news sources for extracting facts from publications, it observes language patterns and compares content with trusted databases, analyzing the reliability among them in real-time. We empirically show that this approach achieves a significant improvement in performance over the state-of-the-art on both the LIAR dataset and real-time social media analysis. Our results demonstrate that the system is able to attain 89% of accuracy on detecting fake news, with a processing latency lower than 1.5s per article. It provides a real-time scalable solution to detect and combat fake news, assisting in the continued fight to keep online information accurate

Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.

Combating Misinformation on Social Media Using Social Noise and Social Entropy as a Measure of Uncertainty

ABSTRACTRecent events around the world, including the war in Ukraine and the conflict in Gaza have highlighted the effective use of social media as a tool to voice concerns about social issues to create awareness. At the same time, social media has become a fertile ground for spreading misinformation, fake news, and conspiracy theories. Misinformation and conspiracy theories have existed since the existence of mankind. What is new today is the speed by which misinformation can be created, magnified and spread using social media. Efforts to regulate social media and control the widespread spread of misinformation are still lacking due to rapid advances in technology and concerns regarding free speech. One approach to minimizing the impact of misinformation is to focus on social noise as an important factor in magnifying and spreading misinformation. In this paper, we investigate methods of identifying and quantifying social noise using social entropy as a measure of uncertainty and topic modeling. Results from the study have shown a direct relationship between social noise and social entropy. The results have also shown that social noise and social entropy decrease with the use of URLs and rich content (sematic information). Further studies will include the use of machine learning and AI techniques to improve the definition of social news and social entropy.

Dual-stream fusion network with multi-head self-attention for multi-modal fake news detection

With the rapid advancement of social media platforms like Weibo and WeChat, alongside the emergence of deepfake technologies, tackling fake information has become a major challenge for society and government institutions. To address this, developing efficient and intelligent methods for fake news detection is crucial. This paper introduces a dual-stream fusion network model (DSF-MHSA) based on deep learning, designed to detect fake news across web pages, images, and text. The model tackles issues such as cross-lingual discrepancies, data imbalance, and multimodal information fusion by integrating deep learning models like ERNIE-M, AlexNet, and ShuffleNet, along with three multi-head self-attention mechanisms. It processes textual and image data separately to capture long-range dependencies and global information, enhancing understanding and recognition. A unified multi-head self-attention mechanism then merges these insights to strengthen cross-modal correlation detection. The model is tested on datasets from Twitter, Weibo, and IKCEST-2023, which includes real online news from various social media sources. Results show that the DSF-MHSA model achieves over 90 % accuracy, surpassing traditional models in news detection tasks. This research offers significant practical value for the identification and understanding of news content.

Multimodal dual perception fusion framework for multimodal affective analysis

The misuse of social platforms and the difficulty in regulating post contents have culminated in a surge of negative sentiments, sarcasms, and the rampant spread of fake news. In response, Multimodal sentiment analysis, sarcasm detection and fake news detection based on image and text have attracted considerable attention recently. Due to that these areas share semantic and sentiment features and confront related fusion challenges in deciphering complex human expressions across different modalities, integrating these multimodal classification tasks that share commonalities across different scenarios into a unified framework is expected to simplify research in sentiment analysis, and enhance the effectiveness of classification tasks involving both semantic and sentiment modeling. Therefore, we consider integral components of a broader spectrum of research known as multimodal affective analysis towards semantics and sentiment, and propose a novel multimodal dual perception fusion framework (MDPF). Specifically, MDPF contains three core procedures: (1) Generating bootstrapping language-image Knowledge to enrich origin modality space, and utilizing cross-modal contrastive learning for aligning text and image modalities to understand underlying semantics and interactions. (2) Designing dynamic connective mechanism to adaptively match image-text pairs and jointly employing gaussian-weighted distribution to intensify semantic sequences. (3) Constructing a cross-modal graph to preserve the structured information of both image and text data and share information between modalities, while introducing sentiment knowledge to refine the edge weights of the graph to capture cross-modal sentiment interaction. We evaluate MDPF on three publicly available datasets across three tasks, and the empirical results demonstrate the superiority of our proposed model.

AI in Fake News Detection: Balancing Technological Efficacy and Ethical Integrity

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[formula omitted]: A multimodal misinformation dataset for media authenticity analysis

With the development of various generative models, misinformation in news media becomes more deceptive and easier to create, posing a significant problem. However, existing datasets for misinformation study often have limited modalities, constrained sources, and a narrow range of topics. These limitations make it difficult to train models that can effectively combat real-world misinformation. To address this, we propose a comprehensive, large-scale Multimodal Misinformation dataset for Media Authenticity Analysis (M3A), featuring broad sources and fine-grained annotations for topics and sentiments. To curate M3A, we collect genuine news content from 60 renowned news outlets worldwide and generate fake samples using multiple techniques. These include altering named entities in texts, swapping modalities between samples, creating new modalities, and misrepresenting movie content as news. M3A contains 708K genuine news samples and over 6M fake news samples, spanning text, images, audio, and video. M3A provides detailed multi-class labels, crucial for various misinformation detection tasks, including out-of-context detection and deepfake detection. For each task, we offer extensive benchmarks using state-of-the-art models, aiming to enhance the development of robust misinformation detection systems.

A Knowledge-Aware NLP-Driven Conversational Model to Detect Deceptive Contents on Social Media Posts

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

Fake News Detection with Machine Learning Algorithms

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.

The nexus of scam and fake news: An exploratory study of cases of fake news-scams in Singapore

The increasing prevalence of fake news as well as scams has been a cause for concern for authorities in Singapore in recent years. In particular, the concurrent rise in fake news and online scams has shown signs of convergence in the form of fake news-scams, where false information is manipulated and employed in scams. However, literature examining this emerging phenomenon is scarce. As such, this study aims to deepen our understanding of this novel issue by exploring how scammers employ fake news in terms of type, channels of transmission, and persuasion techniques. Drawing from local case studies ( n = 90) collected from 2016 to 2021, it was found that authority appeals were the most common theme employed in fake news-scams, and persuasion cues of authority were the most prevalent. In addition, Facebook was identified as the most common channel of transmission in the spread of fake news-scams. These findings serve to inform anti-scam prevention measures by local authorities and suggestions are made as to how various stakeholders (e.g. authorities, companies, and individuals) can be involved in dealing with fake news-scams.

Abuse of Criticism: T.S. Eliot and Northrop Frye’s Academic Ramp-up to “Fake News”

Percy Bysshe Shelley’s “Defence of Poetry” originates T.S. Eliot’s 1932 Harvard Norton Lecture on “taste” maligning “a marked swing in his feelings toward Shelley's verse from fervid imitation to contemptuous disdain.” Lowe, P.J., 2002. Eliot’s acolyte, Northrop Frye, in 1957 follows similar tautology to exclude Shelley’s straw-man agonist, Thomas Love Peacock, from Anatomy of Criticism commentary over Sir Philip Sidney’s “Apology for Poesy,” Shelley’s obvious rhetorical target. Here we treat Eliot and Frye’s methodologies as a Prigozhin troll factory authoritarian oligarchic academic coup that, “like the Internet Research Agency, engage in … ‘coordinated inauthentic activity, ranging from the use of false names and the creation of false audiences to the publication of false stories and the creation of divisive narratives.”

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.