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Blessing or curse? A survey on the Impact of Generative AI on Fake News (2404.03021v1)

Published 3 Apr 2024 in cs.CL and cs.AI

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.

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Authors (3)
  1. Alexander Loth (1 paper)
  2. Martin Kappes (2 papers)
  3. Marc-Oliver Pahl (7 papers)
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Summary

Comprehensive Survey on the Impact of Generative AI on Fake News

Introduction

The proliferation of fake news in the digital age has been significantly influenced by advancements in Generative Artificial Intelligence (GenAI). This survey meticulously examines the dual role of Generative AI in both creating and detecting fake news, offering a systematic review of the current literature spanning from technological underpinnings to ethical implications. By delineating the contributions and identifying gaps within the corpus of existing research, this survey sets a foundation for future investigations into the ways Generative AI continues to shape the landscape of information dissemination.

Enabling Technologies

Central to the creation and detection of fake news are enabling technologies like NLP and transformers. These technologies have evolved to understand and produce human-like text, bringing forth models such as BERT and GPT that are capable of generating content indistinguishable from that created by humans. This section explores how these technologies have been utilized for fake news detection, underscoring their importance in developing more sophisticated detection mechanisms.

Creation of Fake News

The capability of GenAI to automate content creation has profound implications for the production of fake news. By reviewing recent advancements in AI models, this section highlights the increasing realism and complexity of generated text, images, audio, and video content. The survey examines the generation of deepfake technology and its implications for creating synthetic media, emphasizing the need for ongoing research in detection strategies to counteract these advancements.

Case Study: Social Media as Distribution Channel

This section focuses on social media's pivotal role in disseminating fake news. It examines the impact of generative AI on the spread of misinformation across platforms, analyzing how AI-generated content can manipulate public discourse. With social media being the primary distribution channel for fake news, the survey emphasizes the necessity for platforms to employ advanced detection technologies to mitigate the spread of misinformation.

Detection of Fake News

Technological advancements in GenAI have not only facilitated the creation of fake content but also improved methods for detecting such content. This section reviews state-of-the-art technologies and methodologies employed in the detection of fake news. From using transformer-based models to leveraging user behavior and interaction patterns on social media, the survey showcases diverse approaches to identifying and combating fake news.

Deepfakes as Upcoming Technology

The emergence of deepfakes presents new challenges in distinguishing between real and AI-generated content. This section outlines the current state of deepfake technology and its potential for creating highly convincing fake news. The survey reviews detection methods that utilize machine learning algorithms and deep learning models, highlighting the ongoing arms race between deepfake creation and detection technologies.

Open Issues and Future Directions

Despite the significant progress in understanding and mitigating the impact of GenAI on fake news, several open issues remain unaddressed. This section outlines the unresolved challenges, including the rapid evolution of GenAI technologies, ethical concerns surrounding AI-generated content, and the lack of comprehensive datasets for training detection models. The survey emphasizes the importance of interdisciplinary research and collaboration among stakeholders to tackle these challenges effectively.

Conclusion

The interplay between Generative AI and fake news is a dynamic and evolving field that presents both challenges and opportunities. By offering a comprehensive review of the current state of research, this survey provides valuable insights into the capabilities and limitations of GenAI in the context of information dissemination. As the technology continues to advance, future research must focus on developing innovative solutions to detect and mitigate fake news, ensuring the integrity of information in the digital age.

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