# Blessing or curse? A survey on the Impact of Generative AI on Fake News

Alexander Loth  0009-0003-9327-6865 Microsoft
<EMAIL_ADDRESS>Martin Kappes  Frankfurt University of Applied
Sciences
<EMAIL_ADDRESS>Marc-Oliver Pahl  0000-0001-5241-3809 IMT Atlantique,
UMR IRISA, Chaire Cyber CNI
<EMAIL_ADDRESS>
###### Abstract

Fake news significantly influence our society. They impact consumers, voters,
and many other societal groups. While Fake News exist for a centuries,
Generative AI brings fake news on a new level. It is now possible to automate
the creation of masses of high-quality individually targeted Fake News. On the
other end, Generative AI can also help detecting Fake News. Both fields are
young but developing fast. This survey provides a comprehensive examination of
the research and practical use of Generative AI for Fake News detection and
creation in 2024. Following the Structured Literature Survey approach, the
paper synthesizes current results in the following topic clusters 1) enabling
technologies, 2) creation of Fake News, 3) case study social media as most
relevant distribution channel, 4) detection of Fake News, and 5) deepfakes as
upcoming technology. The article also identifies current challenges and open
issues.

###### Index Terms:

Generative Artificial Intelligence, Generative AI, GenAI, Fake News Detection,
Deep Learning, Information Dissemination, Social Media, Natural Language
Processing, NLP, Ethical AI, Misinformation, Content Creation, Synthetic
Media, Information Security, Misinformation Mitigation.

##  I Introduction

The development of information dissemination has a long history of influencing
public perception and political landscapes. In the era of Stalin, photo
manipulation was employed to erase political rivals from historical records,
altering public perception of history. Goebbels, the Reich Minister of
Propaganda in Nazi Germany, masterfully used propaganda to control public
opinion and further the Nazi agenda.

Cambridge Analytica, a British political consulting firm, became widely known
for its role in the 2016 United States presidential election and the Brexit
referendum[1]. Their case shows the immense influential potential of
individually-targeted information. The company specialized in data mining,
data analysis, and strategic communication during electoral processes. The
Cambridge Analytica scandal used data analytics in influencing voter behavior
through targeted, at the time, hand crafted political advertising on social
media. Cambridge Analytica’s techniques were used in various political
campaigns, including the 2016 US presidential election and the Brexit
referendum. The company claimed that its data-driven approach was highly
effective in shaping political outcomes.

Generative AI significantly transforms our society[2]. This technology can
automate the creation of realistic images, videos, and texts, making it
increasingly difficult to distinguish between real and artificial content. The
generation comes at almost no cost, enabling the massive creation of
individually-targeted information. The sophistication of Generative AI raises
unprecedented challenges in the realms of news, media, and information
integrity.

Fake news specifically refers to fabricated information that mimics news media
content in form but not in organizational process or intent, disinformation is
false information deliberately spread to deceive people, and misinformation is
false information spread without an intention to mislead[3, 4]. The
proliferation of fake news represents a significant challenge, necessitating
advanced solutions for detection and mitigation[5]. In this context,
Generative Artificial Intelligence (GenAI) has emerged as a dual-use
technology capable of both generating sophisticated fake content and detecting
it.

Surveys have started exploring various facets of fake news and Generative AI,
highlighting the technical challenges and the development of both
identification and mitigation techniques[5]. This survey fills a gap by
systematically exploring how Generative AI technologies are applied to both
propagate and combat fake news. The rapid evolution of Generative AI
techniques, particularly in Natural Language Processing (NLP) and deep
learning, underscores the urgency for an updated analysis of this domain.

This survey addresses the interplay between Generative AI and fake news:

  1. 1.

How is Generative AI used in the context of fake news creation and detection?

  2. 2.

How is the domain of Generative AI structured in the context of fake news?

  3. 3.

How to cluster existing research systematically?

  4. 4.

What are the relevant venues and the active research groups worldwide driving
this field?

  5. 5.

What are currently open issues and what is possible future work?

This survey delineates several key contributions to the literature on
Generative AI and fake news, marking a novel departure from existing surveys:

  1. 1.

It offers a comprehensive structural analysis of the Generative AI domain
within the context of fake news.

  2. 2.

By clustering existing work, this survey identifies and categorizes the
existing approaches.

  3. 3.

It systematically enumerates the leading venues and research groups, providing
insights into the epicenters of current research efforts.

  4. 4.

This survey highlights unresolved issues and potential future research
directions, thereby setting a roadmap for forthcoming investigations.

This survey is structured as follows: Section II examines existing surveys and
underlining the need for this survey. Section III introduces the key concepts
and aspects of the domain under consideration. Section IV details the
methodological framework adopted for this study. Section V offers an in-depth
review of recent research developments within the domain. Section VI delves
into the evolution of the research topic, highlighting prominent venues.
Section VII synthesizes the findings from the papers presented in this survey,
distilling the essence of current research and its implications for the
future.

##  II Related Work

This survey identifies a gap in the intersection of Generative AI and fake
news generation and detection. Most studies focus on utilizing Natural
Language Processing and machine learning. The generative capabilities of AI in
creating fake news receive less attention. While advancements in Generative AI
are documented, their direct impact on fake news remains secondary. Social
media’s role in misinformation is well-studied, yet the potential of
Generative AI in content creation is often overlooked. Interdisciplinary
approaches to fake news are common, but the specific impact of Generative AI
is less explored. Deepfake research focuses on specific applications without a
broader view of Generative AI in fake news. This survey fills these gaps,
highlighting Generative AI’s role in both generating and detecting
misinformation.

###  II-A Presentation and Analysis of Related Works

While numerous components already exist in the field, this research
contributes a novel perspective by focusing on Generative AI. The related work
is structured in five sub-sections (as described in Section V):

  * •

Enabling technologies

  * •

Creation of Fake News

  * •

Case study social media as most relevant distribution channel

  * •

Detection of Fake News

  * •

Deepfakes as upcoming technology

####  II-A1 Enabling technologies

Oshikawa et al. (2020) delve into the application of Natural Language
Processing (NLP) for fake news detection, highlighting the critical role of
NLP in automating the identification of false information online[6]. Similar
to our survey, Oshikawa et al. emphasize the importance of technological
solutions in combating fake news. Unlike their work, we focus on the
generative aspect of AI, exploring how these technologies not only detect but
also contribute to the creation of fake news.

Villela et al. (2023) analyze machine learning algorithms used in fake news
detection. It aligns with our survey in terms of the technological approach to
combating fake news but does not delve into the specific role of Generative AI
for creating fake news[7].

Leo (2019) examines machine learning applications in banking risk management.
Although not directly related to our survey’s focus, it provides insights into
machine learning applications in a different field, showing the versatility of
these technologies[8].

####  II-A2 Creation of Fake News

Cao et al. (2023) provide an extensive historical overview of Generative AI,
including ChatGPT and DALL-E-2. They focus on the efficiency and accessibility
of AI in content creation. The survey’s emphasis on the technological
evolution and capability of Generative AI models aligns with our focus on AI’s
role in content generation. However, it does not specifically focus on the
fake news aspect, which is central to our survey[9].

Kalyan & Ammus (2021) delve into transformer-based pretrained models in NLP,
crucial for understanding modern Generative AI. It provides a technical
foundation that is relevant to our survey but does not directly address the
application of these technologies in generating or detecting fake news[10].

####  II-A3 Case study social media as most relevant distribution channel

Melchior et al. (2023) investigate the motivations behind sharing fake news on
social media, exploring both intrinsic and extrinsic factors. While it
provides insights into the psychology of fake news distribution, the article
lacks the emphasis on Generative AI’s role in content creation, which is a key
area of our survey[11].

Shu et al. (2017) review fake news detection on social media, emphasizing the
challenges and unique characteristics of social media platforms. It intersects
with our survey in the context of fake news but only covers aspects of
Generative AI in this process[3].

####  II-A4 Detection of Fake News

Zhou et al. (2020) explore fake news detection from various perspectives,
including the content’s falsehood, style, propagation patterns, and source-
credibility. It encourages interdisciplinary research on fake news. While it
shares our focus on fake news, our survey specifically examines the
intersection with Generative AI, which is not the focus of Zhou et al.[12].

Bondielli and Marcelloni (2019) investigate various techniques for detecting
fake news and rumors, underscoring the significance of classification, data
mining, and machine learning methods[13]. While Bondielli and Marcelloni
provide an extensive overview of detection methodologies, our survey uniquely
integrates the generative capabilities of AI, examining both the challenges
and opportunities they present in the context of misinformation.

Sharma et al. (2019) present an in-depth exploration of identification and
mitigation techniques for combating fake news[5]. Their work is akin to ours
in its multidisciplinary approach to addressing fake news. However, our survey
distinguishes itself by specifically analyzing the impact of Generative AI in
both the propagation and detection of fake news, filling a gap they have not
explored.

####  II-A5 Deepfakes as upcoming technology

Khanjani (2023) focuses on audio deepfakes, discussing their creation,
detection, and impact. While it explores a specific application of Generative
AI, its emphasis on audio content differentiates it from our survey, which has
a broader focus on fake news encompassing text and visual content[14].

These works underscore the advancements and challenges in the domains of
Generative AI and Fake News. The present study differs by focusing on the
synergy of these domains, proposing a unified approach to understanding and
mitigating fake news through Generative AI.

##  III The Domain

The term “Generative Artificial Intelligence (AI)” refers to AI technologies
designed to produce new and original content. This content includes text,
images, audio, and other media forms, often matching the sophistication and
authenticity of human-generated output. Models like GPT-4, with their human-
like text generation, are able to pass the Turing test[15].

Advanced machine learning models, such as Generative Adversarial Networks
(GANs), transformers, and variational autoencoders, enable this capability. By
harnessing extensive datasets, these models learn to generate novel instances
that mimic the patterns they have acquired[9, 16].

The synthesis of recent advancements in Generative AI and its application to
the generation and detection of fake news reveals a rapidly evolving field
marked by interdisciplinary contributions. From the foundational work by
Devlin et al. (2018) on BERT, which revolutionized natural language processing
(NLP) through deep bidirectional transformers, to the innovative detection
models like exBAKE by Jwa et al. (2019) and the application of transformers in
identifying automatically generated headlines by Maronikolakis et al. (2021),
the landscape of fake news detection has significantly expanded[17, 18, 19].

Cao et al. (2020) and Liu et al. (2019) extend the discussion to multimedia
content and short fake news stories, respectively, showcasing the breadth of
content that now falls under the purview of fake news detection efforts[20,
21]. The pandemic has further underscored the critical need for timely and
accurate detection mechanisms, as evidenced by Karnyoto et al. (2022) and Novo
(2021), who focus on COVID-19 misinformation[22, 23].

Emerging concerns around synthetic media, or ”deepfakes”—highly realistic AI-
generated audiovisual content intended to deceive by impersonating real
individuals—have prompted researchers like Bansal et al. (2023) and Botha and
Pieterse (2020) to develop detection methods that address the nuanced
challenges posed by this content[24, 25]. Meanwhile, the work of Wang et al.
(2020) and Cardenuto et al. (2023) highlights the importance of distinguishing
between genuine and artificially created images and realities[26, 27].

The role of AI in both facilitating and combating the spread of fake news
introduces a paradox that researchers must navigate. While generative AI
models have the potential to create highly realistic and misleading content,
as explored by Mosallanezhad et al. (2020) and Cocchi et al. (2023), they also
offer the tools necessary for the development of sophisticated detection
algorithms[28, 29].

The inception of generative models can be traced back to the 1950s, marked by
the development of Hidden Markov Models (HMMs) and Gaussian Mixture Models
(GMMs). However, the introduction of GANs and the progression of models such
as BERT, GPT, and DALL-E represented transformative milestones in the
field[9]. These models have been instrumental in producing content that is not
only realistic but also of high quality, paving the way for their extensive
application across various industries.

Alongside its rise, Generative AI has sparked significant concerns regarding
its potential misuse, particularly in the generation of fake news. Fake news,
which is characterized by the deliberate presentation of misleading or false
information as legitimate news, has become an increasingly problematic issue.
This is especially true in the context of social media platforms that enable
rapid and widespread dissemination of information[30, 31, 3]. The capacity of
Generative AI to create convincingly authentic text and media intensifies this
problem, as it blurs the line between genuine and AI-generated content, making
it progressively difficult to differentiate between the two.

Generative AI(1) Fake News Creation(2) Fake News Detection(3) Mitigation
Strategies(4) Ethical
ConsiderationsTextGenerationImageSynthesisAudioGenerationVideoGenerationSocial
MediaAnalysisContentVerificationCrowdSourcingAutoencodersVAEsTransformersGPTsGANsPublicAwarenessRegulatoryPoliciesPrivacyConcernsBias
&FairnessAbbreviations:GPT: Generative Pre-trained TransformerVAE: Variational
AutoencoderGAN: Generative Adversarial Network

Figure 1: Structural overview of the domain of Generative AI and its impact on
fake news, illustrating the main themes of creation and detection, along with
their subtopics and interrelations. Blue indicates the main themes of the
domain; green highlights the subtopics related to these main themes; and
yellow denotes key technologies and methodologies central to understanding and
addressing the challenges in this domain.

The domain of fake news in the context of Generative AI encompasses the
intersection of misinformation dissemination and advanced computational
techniques designed to generate or detect such content. Generative AI refers
to the subset of AI technologies capable of creating content, including text,
images, and videos, that is indistinguishable from content created by humans.
This technology has profound implications for the production and dissemination
of news, particularly concerning the authenticity and trustworthiness of
online content.

The advent of Generative AI has ushered in new methods for creating highly
realistic and convincing fake news, a phenomenon that poses significant
challenges to information tendencies on digital platforms[32]. Fake news,
defined as news articles and stories that are factually incorrect yet designed
to appear credible, has been identified as a major societal problem. It
influences public opinion, affecting electoral processes, and undermining
trust in media institutions[31].

The impact of Generative AI on fake news is multifaceted, affecting both the
creation of deceptive content and the development of detection mechanisms. On
one hand, Generative AI models, such as GPT-3 and DALL-E, have demonstrated
the ability to generate coherent and persuasive text and imagery, raising
concerns about their potential misuse for generating disinformation. On the
other hand, these advances also offer promising avenues for combating fake
news, through the development of AI-driven tools capable of identifying and
flagging synthetic content with high accuracy.

The domain’s complexity is further amplified by the dynamic nature of social
media platforms, where much of today’s fake news is propagated. The algorithms
governing content dissemination on these platforms play a crucial role in
amplifying or mitigating the spread of fake news, making the study of
Generative AI’s impact on fake news an interdisciplinary challenge that spans
computer science, communications, and behavioral science[33, 34].

Given these considerations, the domain of Generative AI and fake news
encompasses not only the technological advancements that facilitate the
creation of synthetic content but also the societal implications of these
developments, including ethical concerns, regulatory challenges, and the
broader effects on democratic discourse. This literature review elucidates the
current state of research, identifying key trends, challenges, and
opportunities for future investigation.

The diagram presented in Figure 1 shows a visual representation of the domain
of Generative AI and its multifaceted impact on fake news. At the core,
Generative AI serves as the foundation, branching out into two principal
thematic areas: the creation and detection of fake news. The Creation aspect
(1) is further subdivided into methods such as Text Generation, Image
Synthesis, Audio Generation, and Video Generation. These represent the diverse
capabilities of Generative AI to produce content that is often
indistinguishable from that created by humans.

The Detection branch (2) is concerned with identifying and verifying the
authenticity of content on social media and other platforms, and is
categorized into Content Verification, Social Media Analysis, and Crowd
Sourcing. These areas reflect the different strategies used to discern the
origins of news items and validate their truthfulness.

Adjacent to these core themes, the diagram also integrates crucial peripheral
domains such as mitigation strategies and ethical considerations. Mitigation
strategies (3) encompass public awareness and regulatory policies,
underscoring the societal and governmental responses to the challenges posed
by fake news. Ethical considerations (4), including privacy concerns and
issues of bias and fairness, reflect the moral implications of using
Generative AI in the context of news dissemination.

Connecting these nodes are sub-nodes that represent the enabling technologies
of Generative AI, including Autoencoders, GANs (Generative Adversarial
Networks), Transformers, GPTs (Generative Pre-trained Transformers), and VAEs
(Variational Autoencoders). These technologies underpin the operations of
Generative AI and are integral to both the creation of synthetic content and
the development of systems to detect and analyze fake news.

###  III-A Functioning of Generative AI for Fake News Generation

Generative AI models synthesize new data by learning from existing datasets.
They function through deep learning architectures such as GANs, VAEs, and
Transformers. GANs pit two neural networks against each other to produce new,
synthetic instances of data. GANs generate realistic images and videos to
accompany synthetic fake news stories.

Transformers utilize attention mechanisms to generate coherent sequences of
text[35]. Transformers, like GPT models, are trained on vast corpora of text.
Transformers are able to produce all kind of text, including fake news[36,
37].

###  III-B Technical Background

This section provides an overview of the key technologies and concepts
foundational to understanding the intersection of Generative AI and fake news.
It introduces the essential definitions and methodologies employed in the
survey.

####  III-B1 Generative Artificial Intelligence

Generative AI refers to a subset of AI technologies designed to create content
that mimics real-world data. These models learn to generate new data samples
that are indistinguishable from authentic datasets.

####  III-B2 Generative Adversarial Networks (GANs)

GANs consist of two neural networks, the generator and the discriminator,
which are trained simultaneously through adversarial processes. The generator
creates data samples aimed at fooling the discriminator, while the
discriminator evaluates them against real data, improving both models
iteratively[38].

####  III-B3 Variational Autoencoders (VAEs)

VAEs are generative models that use a probabilistic approach to produce data.
They learn to encode input data into a latent space and reconstruct it back,
ensuring that generated samples adhere to the probability distribution of the
input data[39].

####  III-B4 Transformer Models

Transformers are a type of neural network architecture designed for processing
sequential data, particularly text. They rely on self-attention mechanisms to
weigh the significance of different parts of the input data[35]. A recent
breakthrough in this area is the development of 1-bit Large Language Models
(LLMs), such as those introduced by Ma et al. (2024)[40], which achieve
comparable performance to full-precision models with significantly reduced
computational costs.

##### BERT

Bidirectional Encoder Representations from Transformers (BERT) is a model
designed to pre-train deep bidirectional representations from unlabeled text
by jointly conditioning on both left and right context in all layers[17].

##### GPT

Generative Pre-trained Transformer (GPT) models represent a significant
advancement in language processing, capable of generating coherent and
contextually relevant text based on a given prompt, which is a user-defined
input that guides the model’s text generation process. These models are
distinguished by their ability to perform a wide array of language tasks
without task-specific training[41].

As described in Figure 2, the GPT model architecture is designed to capture
and generate human-like text by processing input through a series of
transformer blocks. Each block enhances the model’s understanding of language
context and structure, allowing for the generation of coherent and
contextually relevant text. This mechanism allows the model to simulate
various forms of written content, including fake news, by leveraging learned
patterns from extensive data sets.

Input EmbeddingPosition EncodingTransformer Block 1Transformer Block
2Transformer Block N$\vdots$Output Layer Figure 2: Schematic representation of
the GPT architecture. The model processes input through an embedding layer
followed by position encoding to maintain sequence order. This is then passed
through multiple transformer blocks, each comprising multi-head self-attention
mechanisms and feed-forward neural networks, to generate the output text. The
architecture illustrates the flow from input to the generated output.

##  IV Methodology

This survey adopts a structured literature review approach. It identifies,
analyzes, and synthesizes the current research on the use of Generative AI for
fake news. The methodology ensures the aggregation of all relevant across
diverse fields.

###  IV-A Rationale Behind Selection of Specific Works

Several key factors drive the selection of the specific works presented in
this survey. They are outlined below.

The selected works propose novel methodologies, or present comprehensive
overviews that are foundational to understanding the current state of research
in Generative AI and fake news. The selected literature encompasses a diverse
range of perspectives, including technological, psychological, and
sociopolitical angles.

Other works, while potentially valuable, are excluded due to reasons such as
redundancy, lack of direct relevance to the specific intersection of
Generative AI and fake news, or failure to meet the scholarly standards set
for this survey.

###  IV-B Search Strategy

This survey conducts a systematic search across well-established academic
databases and search engines. The used platforms are:

  * •

Web of Science

  * •

Google Scholar

  * •

IEEE Xplore

  * •

ACM Digital Library

  * •

PubMed

This survey covers publications up to March 2024. The search encompasses broad
and specific aspects of Generative AI and fake news, focusing on a curated
selection of keywords. Keywords target Generative AI and fake news:

  * •

“Generative AI” AND “fake news”

  * •

“Fake news detection” AND “fake news”

  * •

“AI-generated content” AND “fake news”

  * •

“Deepfakes” AND “fake news”

  * •

“Fake content detection” AND “fake news”

###  IV-C Inclusion and Exclusion Criteria

This survey sets specific inclusion criteria to ensure relevance and quality
of the literature:

  * •

Studies focusing on Generative AI’s role in creating, detecting, or mitigating
fake news.

  * •

Articles offering empirical evidence or theoretical analysis on Generative
AI’s impact on misinformation.

  * •

Works published in peer-reviewed journals or conference proceedings.

  * •

Books that provide a broader perspective for the general audience.

Exclusion criteria are:

  * •

Studies not directly related to Generative AI and fake news.

###  IV-D Selection Process

An initial search identified 95 articles. After removing duplicates, 86
articles were screened by title and abstract. This led to 31 articles being
excluded for not meeting the inclusion criteria. A full-text review of the
remaining articles resulted in 55 articles used for this survey.

###  IV-E Data Extraction and Synthesis

For each selected study, this survey extracts:

  * •

Publication year,

  * •

research objectives,

  * •

Generative AI technologies used,

  * •

main findings, and

  * •

conclusions.

This data is synthesized to identify trends, challenges, and gaps, focusing on
Generative AI’s intersection with fake news. See table I.

###  IV-F Source types

This survey categorizes sources into scientific articles, presentations, and
books. Scientific articles, providing empirical evidence, form the empirical
backbone of this analysis. Presentations, offering insights on current
practices, enrich the discourse with real-world applications. Books,
delivering comprehensive views, extend the understanding of AI’s societal
impact.

####  IV-F1 Scientific sources

Scientific sources lay the foundation for significant advancements in AI
technologies, especially in the realms of NLP and deep learning models. These
advancements are critical for the enhanced detection and analysis of fake
news. Published primarily in peer-reviewed journals and conference
proceedings, these sources furnish empirical evidence and theoretical
frameworks. Such frameworks are vital for navigating and mitigating the
complex dynamics of misinformation. The focus on multimedia content by Cao et
al. (2020), alongside the innovative employment of BERT and GPT models by
Karnyoto et al. (2022) and Jwa et al. (2019), mirror the evolving
methodologies within this domain[20, 22, 18]. Hence, scientific literature
serves as a pivotal cornerstone for future explorations, grounding the
discussion on generative AI’s dual role in creating and detecting fake
content.

####  IV-F2 Presentations

Presentations focus on the practical applications and implications of AI
technologies in combating fake news. They serve as platforms for discussing
cutting-edge methodologies and sharing insights. The presentation by Schütz et
al. (2021) demonstrates the use of pre-trained transformer models for
automatic fake news detection, showcasing practical applications of generative
AI[42]. It emphasizes the efficiency of fine-tuning state-of-the-art AI
technologies to distinguish between fact and fiction. These forums are
essential for the real-time exchange of ideas and critiques, fostering a
collaborative environment that advances the field. Loth (2023) adds to this
discourse by exploring Generative AI’s broader impact on innovation and
enhancing human collaboration, underlining the necessity of ethical frameworks
in AI development[43].

####  IV-F3 Books

Books offer an exhaustive exploration of topics, providing context, historical
perspectives, and in-depth analysis of generative AI and fake news. For
instance, ”True Or False” by Otis (2020) guides readers in identifying fake
news, enriching the discourse with the author’s intelligence analysis
background[44]. Similarly, ”Decisively Digital” by Loth (2021) and ”KI für
Content Creation” by Loth (2024) delves into AI’s integration in business and
content creation, demonstrating these technologies’ broad impact beyond fake
news detection[45, 46]. Books significantly contribute to the discourse,
offering nuanced insights, practical guidance, and a comprehensive
understanding of generative AI and misinformation’s societal implications for
a broader general audience.

##  V Overview on relevant existing works

This survey methodically investigates the complex interplay between Generative
Artificial Intelligence (AI) and the phenomenon of fake news.

Structured into five interconnected sub-sections, the survey addresses:

  * •

Enabling technologies examines technological underpinnings such as Natural
Language Processing (NLP) and Transformers, essential for Generative AI’s
functionality in generating and detecting fake news.

  * •

Creation of Fake News discusses the dual capacity of Generative AI in content
creation, focusing on its contributions to both, the generation and
dissemination of content.

  * •

Case study social media as most relevant distribution channel analyzes the
influence of social media as a channel for disinformation and the effects of
fake news on public perception.

  * •

Detection of Fake News explores methodologies and technologies for fake news
detection and analysis are, with an emphasis on Generative AI’s role in
augmenting these efforts.

  * •

Deepfakes as upcoming technology discusses challenges posed by deepfakes and
synthetic media, including current detection strategies and the dynamic
between creation and detection.

####  V-1 Enabling technologies

Enabling technologies, such as Natural Language Processing (NLP) and
Transformers, serve as foundations for Generative AI’s evolution. These
methodologies improve the understanding and generation of human language.
Their implications for detecting, analyzing, and generating fake news are
significant.

Schütz et al. (2022) explore the efficacy of the XLM-RoBERTa model in
detecting fake news across languages, specifically targeting English and
German texts[47]. Their approach, which involves pre-training on additional
datasets and fine-tuning on target data, underscores the potential of
multilingual transformers in addressing the global challenge of fake news.

Schütz et al. (2021) apply pre-trained transformer models to automatically
detect fake news, showcasing the efficiency of state-of-the-art AI
technologies in discerning fact from fiction[42].

Devlin et al. (2018) introduce BERT, a transformer model that revolutionizes
NLP by enabling deep bidirectional training of language representations[17].
This model lays the groundwork for sophisticated AI applications, including
nuanced fake news detection mechanisms.

In ”Visual Analytics with Tableau,” Loth (2019) discusses the integration of
NLP techniques in data visualization through tools like Tableau, highlighting
the intersection of data analysis and language processing in uncovering
insights from complex datasets[48].

Peters et al. (2018) provide an in-depth analysis of how contextual word
embeddings, as used in models like BERT, contribute to understanding the
nuances of language, a critical aspect in differentiating genuine from fake
news[49].

Maronikolakis et al. (2021) tackle the challenge of identifying automatically
generated headlines, demonstrating that transformers significantly outperform
humans in distinguishing real from fake news content[19].

De Oliveira et al. (2021) survey the application of NLP in identifying fake
news on social networks, emphasizing the importance of machine learning and
language processing techniques in combating misinformation[50].

Howard and Ruder (2018) propose a method for universal language model fine-
tuning, illustrating how pre-trained models can be adapted for specific tasks
like text classification, including fake news detection, enhancing the
precision of AI-driven analyses[51].

Vijjali et al. (2020) develop a two-stage transformer-based model for
detecting COVID-19 related fake news, combining fact-checking with textual
entailment to verify claims, showcasing the adaptability of transformer models
to current events and specific domains[52].

Loth (2023) examines the ethical dimensions of AI, relating technological
progress to moral responsibility[43].

Vaswani et al. (2023) propose the Transformer model[35]. It replaces complex
recurrent or convolutional networks with attention mechanisms and achieves
superior performance in sequence transduction tasks. By dispensing with
recurrence and convolutions entirely, the Transformer demonstrates increased
parallelizability and reduced training time. This model not only improves the
English-to-German translation task BLEU score by over 2 points but also sets a
new single-model record for the English-to-French task after minimal training
on eight GPUs. The adaptability of the Transformer is further evidenced by its
successful application to English constituency parsing across varying data
sizes.

Bubeck et al. (2023) examine GPT-4, highlighting its near-human capabilities
across multiple disciplines, indicating a move towards artificial general
intelligence (AGI)[53]. GPT-4 emerges as a frontrunner among a new generation
of LLMs, including ChatGPT and Google’s PaLM, demonstrating capabilities that
closely mirror human intelligence across diverse fields such as mathematics,
coding, vision, medicine, law, and psychology. GPT-4’s performance, superior
to predecessors, showcases its versatility in solving novel tasks without
specific prompts. This study positions GPT-4 as an early form of AGI,
emphasizing its societal implications and the necessity for a new research
paradigm beyond next-word prediction.

Ma et al. (2024) introduce a 1-bit Large Language Model (LLM) variant, namely
BitNet b1.58, which employs ternary parameters {-1, 0, 1}[40]. This
development ushers in a new era of efficiency by matching the performance of
full-precision (FP16 or BF16) Transformer LLMs in both perplexity and end-task
performance, despite using significantly less memory and energy. The 1.58-bit
LLM not only defines a new scaling law for training high-performance, cost-
effective LLMs but also suggests a shift towards a new computational paradigm
potentially supported by specialized hardware optimized for 1-bit operations.

These works collectively highlight the transformative impact of NLP and
transformer models in processing and understanding complex language patterns,
offering robust solutions for detecting and analyzing fake news across various
platforms and languages.

####  V-2 Creation of Fake News

Generative AI’s role in content creation spans various domains, from aging
research to synthetic data generation, highlighting its transformative impact
on digital innovation and its potential ethical considerations.

Zhavoronkov et al. (2019) discuss the application of AI in aging research,
demonstrating how AI can generate synthetic biological data and identify new
therapeutic targets, showcasing the broader potential of generative models in
biotechnology[54].

Gozalo-Brizuela and Garrido-Merchan (2023) provide a comprehensive review of
large generative AI models, such as ChatGPT and Stable Diffusion, emphasizing
their revolutionary impact across various sectors and the critical need for
ethical considerations[16].

In the book ”Decisively Digital,” Loth (2021) explores the integration of AI
in business, highlighting the strategic adoption of generative AI models like
GPT-3 for enhancing digital culture and fostering innovation in content
creation[45].

Jin et al. (2020) review the application of Generative Adversarial Networks
(GANs) in computer vision, illustrating the versatility of GANs in image
generation and style transfer, and addressing challenges such as model
collapse[55].

Arora and Arora (2022) highlight the role of GANs in generating synthetic
patient data, underlining the benefits for clinical research and the
importance of addressing ethical issues related to data privacy and
authenticity[56].

Radford et al. (2019) demonstrate how language models, trained on vast
datasets, can perform a range of tasks without explicit supervision,
suggesting a pathway for AI to learn from the diversity of human language and
content[57].

In the book ”KI für Content Creation,” Loth (2024) examines the use of AI in
generating diverse content types, emphasizing the technological advancements
and ethical challenges posed by generative AI in creative processes[46].

Weisz et al. (2023) propose design principles for generative AI applications,
aiming to foster productive use while mitigating potential harms, signifying
the growing need for ethical frameworks in AI development[58].

Simon et al. (2023) address fears surrounding generative AI’s role in
misinformation, arguing that the impact may be overestimated and calling for
evidence-based approaches to understanding and mitigating potential
issues[59].

Sandrini and Somogyi (2023) analyze the effects of Generative AI advancements
on news media consumption[36]. As GenAI progresses, it initially leads
consumers to increase their consumption of deceptive content, such as fake
news and clickbait. This early-stage GenAI distorts consumer incentives and
reduces welfare by encouraging the consumption of lower value goods. However,
after reaching a certain development threshold, GenAI starts to benefit
consumers. This paper recognizes that the negative effects of early-stage
GenAI also include reduced investment in news production.

Ferrara (2024) highlights the potential misuse of GenAI and LLMs, where
powerful capabilities bring forth significant societal challenges[37]. Because
GenAI applications can generate indistinguishable deepfakes and synthetic
identities, they pose risks of enabling malicious campaigns and targeted
misinformation. This article calls attention to the urgent need to address the
misuse of GenAI in creating sophisticated malware, fabricating identities, and
crafting synthetic realities. The societal implications of such misuse
underscore the blurring lines between virtual and real worlds, impacting
everyone.

These works collectively illuminate the dynamic interplay between generative
AI and content creation, underscoring the technology’s innovative potential
and the critical need for responsible application and ethical consideration.

####  V-3 Case study social media as most relevant distribution channel

The intersection of social media, disinformation, and public perception is
critical in understanding the dynamics of fake news. This section presents key
studies that explore these themes, highlighting their contributions and
findings in the broader discourse on fake news.

Allcott and Gentzkow (2017) investigate the distribution and impact of fake
news during the 2016 U.S. presidential election. They find that fake news
favoring Donald Trump was shared more widely on Facebook compared to content
favoring Hillary Clinton. This study underscores the significant role of
social media in the dissemination of fake news and its potential to influence
public opinion and electoral outcomes[30].

Bradshaw, Bailey, and Howard (2021) provide a global overview of organized
social media manipulation, detailing the strategies employed by various actors
to spread disinformation. Their report highlights the industrial scale at
which disinformation campaigns are orchestrated, reflecting the evolving
nature of digital propaganda and its implications for democratic
processes[33].

Bradshaw et al. (2022) examine the propaganda strategies of Russian-affiliated
media outlets in their coverage of the #BlackLivesMatter protests. The study
reveals divergent framing strategies among different Russian media properties,
illustrating how state-backed outlets exploit social issues to influence
public discourse and sow division[60].

Lewandowsky and Van Der Linden (2021) explore the psychological underpinnings
of misinformation and propose inoculation theory as a proactive strategy to
build resilience against fake news. Their work presents evidence that
preemptive exposure to weakened forms of misinformation can ”vaccinate”
individuals, reducing susceptibility to fake news[61].

In the book ”True Or False,” Otis (2020) offers a guide to identifying fake
news, drawing on her experience as a CIA analyst. The book provides readers
with tools to discern truth from falsehood, emphasizing critical thinking and
the importance of verifying sources. Otis’ work is a valuable resource for
enhancing media literacy in the age of disinformation[44].

Shu et al. (2020) discuss various strategies for combating disinformation on
social media, highlighting the roles of education, research, and
collaboration. Their overview presents an array of approaches to mitigate the
spread of fake news, from technological solutions to public awareness
campaigns[34].

Shu et al. (2020) introduce FakeNewsNet, a comprehensive data repository
containing news content, social context, and spatiotemporal information. This
resource facilitates the study of fake news dissemination on social media,
offering researchers valuable data for developing detection algorithms and
understanding the dynamics of misinformation spread[62].

Yang and Tian (2021) investigate the third-person effect in the context of
COVID-19 fake news, finding that individuals perceive others as more
susceptible to misinformation than themselves. This perception may hinder
efforts to address fake news, as it leads to underestimation of one’s own
vulnerability to disinformation[63].

Pennycook and Rand (2021) delve into the psychology behind the belief and
dissemination of fake news on social media[64]. They challenge the prevalent
view that political bias is the primary driver of susceptibility to fake news,
showing that individuals exhibit better discernment between truth and
falsehood when evaluating politically aligned news. The research highlights
that poor judgment of truthfulness correlates with a lack of careful
reasoning, adequate knowledge, and reliance on heuristic cues like
familiarity. Moreover, they uncover a significant gap between individuals’
beliefs and what they share online, attributing this to inattention rather
than intentional sharing of misinformation. This work proposes that
interventions focusing on enhancing users’ attention to accuracy and
leveraging crowdsourced veracity ratings can improve content reliability on
social media platforms.

These works collectively offer an understanding of the challenges posed by
disinformation on social media, highlighting the importance of technological
solutions, psychological resilience, and critical thinking in addressing the
issue. Each contributes unique perspectives on detection strategies,
psychological defenses, and the broader societal impacts, making them
particularly relevant to discussions on generative AI’s role in the
propagation of fake news.

####  V-4 Detection of Fake News

The proliferation of fake news and its detection have become paramount
concerns in the digital age. This section reviews seminal works that have
contributed significantly to the understanding and detection of fake news.

Cao et al. (2020) delve into the significance of multimedia content in the
dissemination of fake news. Their comprehensive review outlines the types of
visual content leveraged by fake news, effective visual features for
detection, and the challenges in multimedia fake news detection. Unlike other
works, this study emphasizes the multimedia aspect of fake news, presenting a
unique angle on detection methods[20].

Jwa et al. (2019) introduce exBAKE, an automatic fake news detection model
leveraging Bidirectional Encoder Representations from Transformers (BERT). By
examining the relationship between the headline and body text, they achieve
significant improvements in detection accuracy. This model’s reliance on deep
contextual embeddings from BERT offers insights into the linguistic nuances of
fake news, setting a foundation for future research in text-based fake news
detection[18].

Focusing on the COVID-19 pandemic, Karnyoto et al. (2022) apply BERT and GPT-2
models augmented with GRU-CRF for enhanced fake news detection. Their approach
highlights the effectiveness of transfer learning and feature augmentation in
addressing the rapid spread of misinformation during health crises[22].

Lazer et al. (2018) provide a multidisciplinary analysis of fake news, calling
for collaborative efforts across fields to mitigate its impact. Their review
synthesizes research on the spread of fake news, its societal effects, and
potential detection and intervention strategies. This work sets a broad
context for understanding fake news, emphasizing the need for a multifaceted
approach to its detection[31].

Liu and Wu (2018) propose a novel model utilizing recurrent and convolutional
networks to classify news propagation paths for early fake news detection on
social media. Their method underscores the importance of analyzing user
engagement patterns, offering a proactive approach to curtailing the spread of
misinformation[65].

Liu et al. (2019) present a two-stage model that leverages BERT for detecting
short fake news stories. By treating fake news detection as a fine-grained
classification task, they address the challenge of identifying misleading
information in concise texts, providing a valuable tool for real-time news
verification[21].

Novo (2021) introduces MisInfoBot, a novel system designed to combat COVID-19
misinformation on Twitter through supervised learning classification and
information retrieval. This work is particularly relevant in the context of
health misinformation, offering a timely example of AI’s potential to address
specific misinformation challenges[23].

Przybyla (2020) investigates automatic methods for detecting documents of low
credibility based on their writing style. By focusing on the sensational and
affective vocabulary typical of fake news, this study contributes to
understanding the stylistic markers that distinguish fake news from credible
reports[66].

Qian et al. (2018) leverage collective user intelligence to detect fake news,
proposing a model that generates user responses to news articles for analysis.
This innovative approach demonstrates the utility of simulating user reactions
as a means to enhance fake news detection algorithms[67].

Shu et al. (2019) explore the influence of social context in detecting fake
news. Their work underscores the tri-relationship among publishers, news
pieces, and users, illustrating how these dynamics can be leveraged for more
accurate fake news detection. This approach of incorporating social media
interactions provides a complementary perspective to traditional content
analysis methods, highlighting the importance of considering the broader
dissemination environment of fake news[68].

Silva et al. (2020) address the challenge of fake news in Portuguese,
providing a novel dataset and a comprehensive analysis of machine learning
methods for its detection. This work not only fills a gap in language-specific
resources for fake news detection but also offers insights into the
effectiveness of various classification strategies, enriching the global
discourse on combating misinformation[69].

Zhou and Zafarani (2019) explore the utilization of network-based clues from
social media for fake news detection, presenting a pattern-driven
approach[32]. They emphasize the patterns of fake news propagation through the
analysis of spreaders and their relationships within social networks. This
study not only adds a new dimension to fake news detection by moving beyond
content analysis but also enhances the explainability of fake news features.
Their findings demonstrate the effectiveness of incorporating network-level
patterns, including node-level, ego-level, triad-level, community-level, and
overall network patterns, in improving the accuracy of fake news detection.

Each of these contributions enriches the field of fake news detection by
offering unique perspectives, methodologies, and findings. From leveraging
visual content and advanced NLP techniques to incorporating social context and
user interactions, these works collectively advance our understanding of fake
news dynamics. While each has its context and focus, together they form a
multifaceted view of the challenges and opportunities in detecting fake news,
providing a solid foundation for future research in the intersection with
Generative AI.

####  V-5 Deepfakes as upcoming technology

The advent of deepfakes and synthetic media has posed new challenges and
opportunities in the domain of information security, necessitating advanced
detection techniques to safeguard against the propagation of fake news and
manipulated content.

Bansal et al. (2023) explore the utilization of Convolutional Neural Networks
(CNN) and Generative Adversarial Networks (DCGANs) for the detection and
elimination of deepfake multimedia content, highlighting the critical role of
deep learning models in mitigating the spread of fabricated information[24].

Botha and Pieterse (2020) provide an overview of the creation and detection
methods for fake news and deepfakes, emphasizing the significant threat they
pose to 21st-century information security and the necessity for robust
detection tools to distinguish between real and synthetic content[25].

Wang et al. (2020) propose FakeSpotter, a novel approach based on monitoring
neuron behaviors, to detect AI-synthesized fake faces, demonstrating that deep
learning systems can effectively identify manipulations in facial images, thus
contributing to the integrity of visual information[26].

Cardenuto et al. (2023) delve into the implications of synthetic realities
generated through AI, underlining the urgency for digital forensic techniques
that can discern harmful synthetic creations from reality, especially in the
context of combating fake news and misinformation[27].

Mosallanezhad et al. (2020) tackle the generation of topic-preserving
synthetic news via an adversarial deep reinforcement learning approach,
illustrating the dual-use potential of AI in both generating realistic news
content and developing methodologies for its detection[28].

Cocchi et al. (2023) conducte an experimental analysis on the robustness of
deepfake detection methods against various image transformations, revealing
the susceptibility of detection models to manipulation and the need for
adaptable and resilient detection algorithms[29].

These works collectively underscore the escalating arms race between the
creation of deepfakes and the development of detection technologies. They
highlight the challenges presented by synthetic media and the imperative for
continuous innovation in detection methodologies to ensure the veracity of
digital content in the age of generative AI.

##  VI Findings

###  VI-A Evolution of the topic

The landscape of research surrounding Generative AI and its implications for
fake news has undergone significant transformation from 2022 to 2024.
Initially, the focus was predominantly on understanding the basics of
Generative AI technologies and their potential applications. Early research
concentrated on foundational models and techniques, such as those discussed in
[57] and [17], which laid the groundwork for later advancements in content
generation and analysis.

As the technology matured, attention shifted towards exploiting Generative AI
for more specific tasks. The creation and detection of synthetic media,
including deepfakes, emerged as a critical area of investigation, with
significant contributions from [24] and [25]. Parallel to these technical
explorations, researchers also delved into the societal impact of fake news,
scrutinizing its influence on public perception and social media dynamics, as
highlighted by [30] and [33].

More recently, the focus has expanded to encompass ethical considerations and
mitigation strategies, addressing the broader consequences of Generative AI’s
capabilities. Studies such as [59] and [58] underscore the importance of
responsible AI use and the development of frameworks to counter the
proliferation of fake news effectively.

The evolution of this research topic reflects in the diversification of
domains and methodologies, as indicated in Table I. Initially centered on the
technical aspects of Generative AI, the research community gradually embraced
a more holistic approach. This shift acknowledges the multifaceted nature of
fake news, incorporating technological, social, and ethical dimensions into
the discourse.

The landscape of Generative AI in the context of fake news encompasses
significant advancements and challenges in March 2024. Sophisticated
Generative AI models, such as the latest GPT iterations, advanced
autoencoders, and the innovative 1-bit LLMs introduced by Ma et al.
(2024)[40], now produce hyper-realistic fake news content in text, image,
audio, and video formats[37, 46].

Year |  2017 |  2018 |  2019 |  2020 |  2021 |  2022 |  2023 |  2024
---|---|---|---|---|---|---|---|---
Enabling technologies |  |  [17], [51], [49] |  [8], [48] |  [6], [52] |  [50], [10], [19], [42] |  [47] |  [43], [35], [53] |  [40]
Creation of Fake News |  |  |  [57], [54] |  [55] |  [45] |  [56] |  [9], [16], [59], [58], [36] |  [46], [37]
Case study social media as most relevant distribution channel |  [30] |  |  |  [44], [34], [62], [70] |  [33], [61], [63] |  [60] |  [11] |
Detection of Fake News |  [3] |  [31], [65], [67] |  [13], [18], [21], [5], [68], [32] |  [20], [66], [69], [12] |  [23] |  [22] |  [7] |
Deepfakes as upcoming technology |  |  |  |  [25], [28], [26] |  |  |  [24], [27], [29], [14] |

TABLE I: Domains over time.

###  VI-B Relevant venues

The landscape of research pertaining to Generative AI and fake news detection
diverts across a variety of scholarly venues. This reflects the novelty and
resulting fragmentation of this multidisciplinary field and underscores the
importance nature of the topic. Table II presents a summary of the venues with
the associated citations, offering a panoramic view of where significant
research contributions have been presented.

From the data collated, it is evident that the Association for Computational
Linguistics and the AAAI Conference on Artificial Intelligence are prominent
platforms for presenting seminal works, indicative of the strong reliance on
advanced computational methods in this domain. The recurring presence of
certain conferences, such as the International Conference on Pattern
Recognition and the International Joint Conference on Artificial Intelligence,
highlights the importance of AI and machine learning techniques in the
development and assessment of fake news detection methodologies.

Furthermore, the Trends in Cognitive Sciences and Information Sciences
journals stand out as critical repositories of research that offer theoretical
frameworks and empirical studies that advance our understanding of fake news
dynamics and its detection. These venues, along with the Science journal, have
been instrumental in disseminating research that bridges the gap between
technical solutions and their psychological and societal implications.

Clusters can be observed within certain conference series, suggesting a
community of researchers consistently contributing to and building upon the
collective knowledge in the field. For instance, the consistent contributions
to the ACM International Conference on Multimedia and various ACM-sponsored
outlets reflect a focus on multimedia aspects in fake news, such as deepfake
detection.

The diversity of venues also reflects the broad spectrum of Generative AI
applications, from content creation in Harvard Kennedy School (HKS)
Misinformation Review to ethical considerations in the Workshop on Human-AI
Co-Creation with Generative Models (HAI-GEN). These patterns reveal the depth
and complexity of the research, demanding a cross-sectional approach that
spans technical, ethical, and regulatory dimensions.

Type |  Venue | References
---|---|---
Conference |  Association for Computational Linguistics (ACL) |  [51], [6], [49]
Conference |  AAAI Conference on Artificial Intelligence |  [65], [66]
Journal |  Trends in Cognitive Sciences |  [33], [70]
Conference |  International Joint Conference on Artificial Intelligence |  [67], [26]
Conference |  International Conference on Pattern Recognition |  [42], [47]
Journal |  Information Sciences | [13]
Journal |  The International Journal of Press/Politics | [60]
Journal |  Science | [31]
Journal |  Sage New Media & Society | [11]
Journal |  MDPI Risks | [8]
Journal |  MDPI Information | [50]
Journal |  MDPI Applied Sciences | [18]
Journal |  Journal on Interactive Systems | [7]
Conference |  International Conference on Knowledge Science, Engineering and Management | [21]
Conference |  International Conference on IoT, Communication and Automation Technology (ICICAT) | [24]
Conference |  International Conference on Cyber Warfare and Security | [25]
Conference |  Image Analysis and Processing – ICIAP | [29]
Journal |  ACM Computing Surveys | [12]
Journal |  Harvard Kennedy School (HKS) Misinformation Review | [59]
Journal |  Future Healthcare Journal | [56]
Journal |  Frontiers in Big Data | [14]
Journal |  Expert Systems with Applications | [69]
Journal |  European Review of Social Psychology | [61]
Journal |  Computers in Human Behavior | [63]
Journal |  Computer Science and Information Systems (ComSIS) | [22]
Journal |  Ageing Research Reviews (ARR) | [54]
Journal |  ACM Transactions on Intelligent Systems and Technology | [5]
Journal |  ACM SIGKDD Explorations | [3]
Conference |  ACM International Conference on Web Search and Data Mining | [68]
Conference |  ACM International Conference on Multimedia | [55]
Workshop |  Workshop on Human-AI Co-Creation with Generative Models (HAI-GEN) | [58]
TABLE II: Relevant venues.

###  VI-C Most active groups

The research landscape in generative AI, deepfakes, and fake news detection
features contributions from a select number of academic groups and
institutions globally. These entities have collectively propelled advancements
in understanding and mitigating misinformation’s effects. Figure 3 illustrates
the evolution and focus of research activities by leading groups from 2018 to
2023, underlining the growing efforts to combat disinformation and enhance
generative AI technologies. Aside from this survey, only a few groups delve
into generative AI within the context of fake news, indicating the field’s
emerging nature. The involvement of multiple groups, rather than isolated
researchers, adds significant weight to the research conducted in this domain.

Year2018201920202021202220232024NTU [71][19]LMU [72][19]Cam [73][56]AIT
[42][47]PEC [74] Figure 3: Timeline of research activities by leading groups
from 2018 to 2023, illustrating the dynamic landscape of contributions in the
fields of digital forensics, disinformation detection, and generative AI. Each
colored box represents a significant milestone or publication by the
respective research group.

Research on generative AI’s role in misinformation involves notable academic
and technological institutions, divided into universities and other research
entities.

####  VI-C1 Universities

##### Cambridge University

Cambridge University’s research team explores generative AI’s societal
impacts, focusing on misinformation. They investigate both theoretical aspects
and AI’s practical uses against fake news, with key studies addressing
psychological motivations for sharing political fake news [73] and utilizing
generative adversarial networks for synthetic patient data generation [56].

##### Nanyang Technological University, Singapore

At Nanyang Technological University, Singapore, researchers contribute
significantly to AI and media integrity. Their innovations include FakeSpotter
for identifying AI-generated fake images [26] and studies on fake news
definitions [71].

##### Ludwig-Maximilians-University of Munich

This group uses NLP and machine learning for misinformation detection, notably
in identifying AI-generated content [19] and analyzing fake news discourse
[72].

####  VI-C2 Other Research Entities

##### Austrian Institute of Technology

Specializing in cross-lingual fake news detection, the Austrian Institute of
Technology employs transformers to identify misinformation in multiple
languages [47] and explores automatic fake news detection [42].

##### Pôle d’excellence cyber (PEC)

Focusing on cybersecurity and information manipulation, Pôle d’excellence
cyber (PEC) provides insights into combating information manipulation through
comprehensive expert reports. Their contributions, while not exclusively on
generative AI, are vital for understanding cybersecurity aspects of fake news
and deepfakes[74].

These groups contribute to a dynamic ecosystem focused on combating
misinformation, a critical issue in the digital era. Their efforts underscore
the interdisciplinary approach required to tackle fake news, merging insights
from computer science, psychology, media studies, and cybersecurity.

###  VI-D Open Issues

The emergence of Generative AI has opened new frontiers in content creation,
including the generation of human-like text, images, and videos[46]. The
intersection of Generative AI and fake news is a field, marked by several
unresolved issues that underscore the complexity of the benefits and risks of
advanced AI technologies.

One pressing unresolved issue is the specific application of Generative AI in
fake news creation, which, while showcasing technological advancement, also
poses significant risks to information integrity[20, 18]. While they offer
significant potential for positive applications, their capacity for creating
convincingly realistic fake content poses severe threats to informational
integrity and public trust [16]. The technological arms race between
Generative AI advancements and detection countermeasures underscores a
significant research gap[22, 31]. Challenges include the rapid evolution of
Generative AI, ethical concerns, and the scarcity of comprehensive
datasets[65, 21].

####  VI-D1 Notable Gap in Literature: Generative AI and Fake News

The literature has yet to fully explore the intersection of Generative AI and
fake news. This gap is attributed to the nascent stage of these technologies
and the complexity of predicting their misuse [58, 59].

Future studies should focus on simulative research, cross-domain analysis, and
the exploration of both technological and social countermeasures. This
comprehensive approach is essential for safeguarding information integrity and
maintaining public trust in the age of advanced AI [27, 28].

Addressing the synergy between Generative AI technologies and the propagation
of fake news is critical. Future research must navigate not only technological
innovations but also the broader societal, ethical, and psychological
dimensions of this challenge.

##  VII State of the Research

###  VII-A Impact of Generative AI on news

Generative AI significantly impacts news production, from creating innovative
content to spreading misinformation.

Models like BERT have pushed the boundaries of natural language understanding
and generation, simulating human-like articles[17]. Gozalo-Brizuela and
Garrido-Merchan (2023) highlight Generative AI’s role in diverse content
creation across sectors[16].

Bansal et al. (2023) and Wang et al. (2020) demonstrate Generative AI’s
ability to produce realistic synthetic media, raising concerns about
deepfakes[24, 26]. This advancement challenges the integrity of information
dissemination, necessitating robust fake news detection methods[25, 27].

Mosallanezhad et al. (2020) show Generative AI’s sophistication in creating
contextually relevant synthetic news, blurring the lines between real and AI-
generated content[28].

Simon et al. (2023) discuss the ethical considerations of Generative AI in
news, emphasizing the need for balance between its benefits and risks[59]. The
dual-use nature of Generative AI poses ethical dilemmas, requiring careful
application in journalism to maintain trust and credibility.

###  VII-B Mitigation strategies

Generative AI’s innovative potential for content creation also necessitates
robust mitigation strategies against fake news and misinformation.

Lewandowsky and Van Der Linden (2021) propose inoculation theory, a
psychological approach to build resilience against misinformation[61]. This
approach emphasizes preemptive education and awareness to mitigate fake news
impacts.

Weisz et al. (2023) suggest design principles for generative AI that
prioritize safety and ethical use[58]. These principles focus on preventing
harmful outputs and misuse in AI systems.

Shu et al. (2020) explore collaborative measures against disinformation on
social media, involving governments, private sector, and civil society[34].
Collaboration is key to forming an effective response against misinformation.

Simon et al. (2023) highlight the need for evidence-based methods to address
fears about generative AI’s misinformation role[59]. Dialogue among academics,
policymakers, and developers is crucial for addressing misinformation
complexities.

Advanced detection algorithms, like those developed by Jwa et al. (2019) and
Bansal et al. (2023), are vital for identifying AI-generated fake news[18,
24]. The progress of these technologies is essential for maintaining digital
information integrity.

Countering Generative AI’s adverse effects on news requires diverse
strategies, including technology, education, ethics, and collaboration. As
Generative AI evolves, adaptation and innovation in mitigation strategies are
imperative.

###  VII-C Risks and stakeholders

Generative AI revolutionizes content creation and introduces risks requiring
stakeholder reevaluation. This synthesis examines risks from Generative AI in
news and identifies stakeholders involved in mitigation. Generative AI
generates realistic fake news and deepfakes, threatening information integrity
and public trust. Bradshaw et al. (2021) show social media manipulation
campaigns using Generative AI to spread global disinformation[33]. Botha and
Pieterse (2020) emphasize the threat of fake news to information security,
highlighting detection technology’s role[25].

Stakeholders include academics, technology developers, social media platforms,
policymakers, and the public. Academics develop detection algorithms and study
misinformation’s psychological aspects, proposing strategies like inoculation
theory[61].

Technology developers and platforms play crucial roles in deploying AI
detection systems and enforcing misuse prevention policies. Weisz et al.
(2023) suggest design principles for ethical AI application, guiding
responsible development[58].

Policymakers create regulations that protect public interests while fostering
innovation in AI content generation. Otis (2020) advocates for public
awareness and media literacy to help individuals discern information
authenticity[44].

Collaboration among stakeholders is vital for a comprehensive approach to
Generative AI risks in news. Fostering dialogue, research, and policy
development, stakeholders aim to minimize AI-generated misinformation impacts
while leveraging technology’s benefits.

##  VIII Conclusion

This survey has undertaken a systematic exploration of the interplay between
Generative AI and fake news, filling a critical void in the existing corpus of
literature. In Section II, this survey has substantiated our methodological
approach, highlighting the essential nature of this exhaustive survey within
the dynamic milieu of Generative AI and misinformation. By categorizing the
existing body of work, this survey revealed diverse methodologies in the fight
against fake news, facilitated by Generative AI. Section III established a
foundational comprehension of the domain, shedding light on pivotal concepts
and aspects crucial for understanding the broader ramifications of Generative
AI in the realm of fake news. It presented a comprehensive structural analysis
of the domain, identifying gaps that were previously unaddressed. Section IV
delineated the methodological framework employed in this investigation,
guaranteeing a methodical exploration.

Section V delivered a comprehensive review of recent scholarly endeavors,
accentuating the progress achieved in leveraging Generative AI for both the
detection and creation of fake news. A systematic enumeration of leading
venues and research groups showcased the focal points of current research,
highlighting where significant advancements were being made. Section VI
charted the developmental trajectory of this research area, spotlighting
noteworthy venues and the contributions of eminent research collectives. The
survey brought to light unresolved issues within the domain, suggesting
avenues for future research and setting a clear direction for scholarly
inquiry. It also spotlighted pressing research queries that propel further
scholarly endeavors. Section VII amalgamated the insights derived from the
survey, presenting a condensed perspective of the contemporary state of the
art and delineating prospective avenues for domain advancement. By pinpointing
the state of the art, it outlined the forefront of technological advancements
in Generative AI, emphasizing its dual role in both generating and detecting
fake news. These contributions collectively enhanced our understanding of the
domain, providing a valuable resource for researchers, practitioners, and
policymakers engaged in the ongoing battle against misinformation.

Section VI charted the trajectory of research on Generative AI and its
interplay with fake news. Researchers initially focused on foundational
Generative AI technologies. [57] and [17] provided the early groundwork. As
Generative AI matured, the research pivoted towards its application in
creating and detecting synthetic media. Significant contributions in this
phase included [24] and [25]. Concurrently, the societal impact of fake news
received attention, with studies like [30] and [33] examining its effects on
public perception and social media.

Recent research has broadened to include ethical considerations and mitigation
strategies. Works such as [59] and [58] emphasized responsible AI use. This
topic’s evolution is marked by a diversification in domains and methodologies.
The community transitioned from a technical to a holistic approach,
recognizing the complex nature of fake news. By March 2024, the landscape
featured advanced Generative AI models capable of producing hyper-realistic
content across multiple formats [37, 46]. It highlights the increasing
sophistication of Generative AI applications in the realm of fake news,
pointing to both the technological prowess and the attendant ethical,
societal, and technical challenges.

This survey stands as a reference within the domain of Generative AI and fake
news, offering insights and directions for tackling misinformation. Its
comprehensive analysis and categorization illuminated the interplay between AI
technologies and the dissemination of fake news. By highlighting active
research groups and prominent venues, the survey fostered a collaborative
research environment poised for breakthroughs in misinformation detection and
mitigation.

We hope that the identification of unresolved issues and future research
directions will serve to mature and propel the field forward, emphasizing the
critical need for innovative solutions in this problem that is highly relevant
for our society. In that direction, our second focus on the state-of-the-art
methodologies of Generative AI, for positioning it as a key tool in the
ongoing fight against fake news.

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