In recent years, disinformation has become a significant problem in the media environment. The topic is therefore increasingly relevant in recent research, and authors approach it in different ways. This research aims to provide an answer to the need...
The increasing prevalence of disinformation has led to a growing interest in leveraging artificial intelligence (AI) for detecting and combating this phenomenon. This article presents a thematic analysis of the potential benefits of automated disinfo...
This article focuses on the problem of detecting disinformation about COVID-19 in online discussions. As the Internet expands, so does the amount of content on it. In addition to content based on facts, a large amount of content is being manipulated,...
Fake news is one of the biggest challenging issues in today’s technological world and has a huge impact on the population’s decision-making and way of thinking. Disinformation can be classified as a subdivision of fake news, the main purp...
Detecting disinformation in morphologically rich and under-resourced languages remains a significant challenge in natural language processing. This paper examines the role of lemmatization as a preprocessing strategy for disinformation detection in C...
The emergence of the Internet and web technologies has magnified the occurrence of disinformation events and the dissemination of online fake news items. Fake news is a phenomenon where fake news stories are created and propagated online. Such events...
The proliferation of disinformation on social media platforms poses a significant challenge to the reliability of online information ecosystems and the protection of public discourse. This study investigates the role of emotional sequences in detecti...
This paper presents a multi-level annotation model for detecting fake news in Kazakh and Russian languages, aiming to enhance understanding of disinformation strategies in multilingual digital media environments. Unlike traditional binary models, our...
This paper addresses the lack of bilingual annotated resources for automatic fake news detection in the Kazakh–Russian media space, as well as the limitations of binary annotation, which does not always allow disinformation to be represented as...
The frequency with which fake news or misinformation is published on social networks is constantly increasing. Users of social networks are confronted with many different posts every day, often with sensationalist titles and content of dubious veraci...
In the digital age, the rapid proliferation of misinformation and disinformation poses a critical challenge to societal trust and the integrity of public discourse. This study presents a comprehensive machine learning framework for fake news detectio...
Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. M...
Due to the exponential increase in internet and social media users, fake news travels rapidly, and no one is immune to its adverse effects. Various machine learning approaches have evaluated text and images to categorize false news over time, but the...
The integrity of global elections is increasingly under threat from artificial intelligence (AI) technologies. As AI continues to permeate various aspects of society, its influence on political processes and elections has become a critical area of co...
We live in a hyper-informed society that is constantly being fed with information stimuli. That information may not be correct, and society may be vulnerable to it. We present a methodological proposal with a mixed approach that allows the learning o...
During elections, information manipulation on social media has accelerated the use of artificial intelligence, yet the evidence is difficult to interpret without an integrated view of methods, data, and evaluation. We mapped 557 English-language jour...
Digital misinformation presents a dual challenge: achieving high detection accuracy while ensuring interpretability. This paper introduces X-FRAME (Explainable FRAMing Engine), a hybrid framework that combines semantic representations from XLM-RoBERT...
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This study investigates the effectiveness of a proposed version of Meta’s LLaMA 3 model in detecting fake claims across bilingual (English and Romanian) datasets, focusing on a multi-class approach beyond traditional binary classifications in o...
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 effective...
Fake news generation and propagation occurs in large volumes, at high speed, in diverse formats, while also being short-lived to evade detection and counteraction. Despite its role as an enabler, Artificial Intelligence (AI) has been effective at fak...
This study explores the relationship between journalistic background, content creation experience, and self-reported information literacy among global content creators. Based on an online survey of 500 content creators in eight languages around the w...
The overwhelming amount of information and misinformation on social media platforms has created a new role that these platforms are inclined to take on, that of the Internet custodian. Mainstream platforms, such as Facebook, Twitter and YouTube, are...
This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficul...
Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes r...
Synthetic media are digital artifacts (image, video, audio, text, or multimodal content) that are wholly or partly generated or materially manipulated by artificial intelligence (AI), particularly by deep-learning models. In consequence, their form,...
Disinformation does not always take the form of a fake news item, it also appears in much less evident formats which are subtly filtered into public opinion, thus making its detection more difficult. A method is proposed in this paper to address the...
Introduction: Public health disinformation is a significant problem as demonstrated by the recent scientific literature on the COVID-19 pandemic. However, further studies that analyse the presence of the disinformation mitigation strategies in public...
As disinformation has become a topic of conversation in the media in recent years, the theory of agenda setting is once again making its presence known. The aim of this research is to verify the degree of interest in disinformation by the media (main...
Background and Objectives: Rumors, disinformation, and fake news are problems of contemporary society. We live in a world where the truth no longer holds much importance, and the line that divides the truth from lies, between real news and disinforma...
The effects of disinformation in the media and social networks have been extensively studied from the perspective of reception studies. However, the perception of this media phenomenon expressed by different types of audiences in distant geographic l...
The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the publi...
The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checki...
This article presents a scoping review of the academic literature published between 2021 and 2024 on the intersection of generative artificial intelligence (AI) and disinformation. Drawing from 64 peer-reviewed studies, the review examines the curren...
The proliferation of fake news poses a significant threat to democratic institutions, public trust, and crisis management, particularly as it becomes a central tactic in coordinated information operations. This paper explores the application of machi...
Image manipulation is easier than ever, often facilitated using accessible AI-based tools. This poses significant risks when used to disseminate disinformation, false evidence, or fraud, which highlights the need for image forgery detection and local...
The combat against fake news and disinformation is an ongoing, multi-faceted task for researchers in social media and social networks domains, which comprises not only the detection of false facts in published content but also the detection of accoun...
The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stem...
Scientific disinformation has emerged as a critical challenge at the interface of science and society. This paper examines how false or misleading scientific content proliferates across both social media and traditional media and evaluates strategies...
In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn wor...
In contemporary society, the internet, particularly social media, has become a significant area where individuals spend considerable amounts of time engaging in various activities. Concurrently, the growing utilization of artificial intelligence (AI)...
As the anti-vaccination movement is spreading around the world, this paper addresses the ever more urgent need for health professionals, communicators and policy-makers to grasp the nature of vaccine mis/disinformation on social media. A one-by-one c...
This study examines journalists’ perceptions of the impact of artificial intelligence (AI) on disinformation, a growing concern in journalism due to the rapid expansion of generative AI and its influence on news production and media organizatio...
Spreading quickly throughout populations, whether animal or human-borne, infectious illnesses provide serious risks and difficulties. Controlling their spread and averting disinformation requires effective risk assessment and epidemic identification....
Disinformation in the form of news articles, also called fake news, is used by multiple actors for nefarious purposes, such as gaining political advantages. A key component for fake news detection is the ability to find similar articles in a large do...
The rapid advancement of artificial intelligence (AI), particularly generative models, has enabled the creation of highly realistic deepfake content, significantly transforming the digital media landscape. While these technologies offer innovative ap...
Today, social media are an integral part of everyone’s life. In addition to their traditional uses of creating and maintaining relationships, they are also used to exchange views and all kinds of content. With the development of these media, th...
Deepfakes, synthetic multimedia files generated by artificial intelligence, are drastically undermining digital credibility. Their ability to manipulate our perception of reality has created a new and complex battleground for disinformation, posing a...
Deepfakes are digital audio, video, or images manipulated using machine learning algorithms. These manipulated media files can convincingly depict individuals doing or saying things they never actually did. Deepfakes pose significant risks to our liv...
We investigate the impact of textual content and its structural characteristics on the detection of false information. We propose a Bidirectional Graph Convolutional Neural Network (ICP-BGCN) that integrates message content with its propagation paths...
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