Technological Approaches to Detecting Online Disinformation and Manipulation

Horák, Aleš; Baisa, Vít; Herman, Ondřej

doi:10.1007/978-3-030-58624-9_5

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…Online misinformation and disinformation involve numerous approaches such as disinformation diffusion trend analysis and identification, dynamic budget allocation (DBA) strategies [35], computer-supported approaches for detecting disinformation [36], [37], and propagation network-based feature extraction [38]- [40]. These techniques attempt to analyze the propagation of disinformation, mitigate its impact, and detect fake news.…”

Section: Related Workmentioning

confidence: 99%

“…DBA methods integrate refutation, media regulation, and social bot detection to limit the proportion of disinformation-supportive accounts on online social networks (OSNs) [35]. Computer-supported systems predominantly apply ML methods to perform factchecking, subject identification, text style analysis, and message filtering in social media channels [37]. On the other hand, propagation network-based feature extraction collects the propagation properties of bogus news for detection and applying ML for classification [42].…”

Section: Related Workmentioning

confidence: 99%

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Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Shah,

Thapa,

Acharya

et al. 2024

IEEE Access

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This paper explores the dual role of Large Language Models (LLMs) in the context of online misinformation and disinformation. In today's digital landscape, where the internet and social media facilitate the rapid dissemination of information, discerning between accurate content and falsified information presents a formidable challenge. Misinformation, often arising unintentionally, and disinformation, crafted deliberately, are at the forefront of this challenge. LLMs such as OpenAI's GPT-4, equipped with advanced language generation abilities, present a double-edged sword in this scenario. While they hold promise in combating misinformation by fact-checking and detecting LLM-generated text, their ability to generate realistic, contextually relevant text also poses risks for creating and propagating misinformation. Further, LLMs are plagued with many problems such as biases, knowledge cutoffs, and hallucinations, which may further perpetuate misinformation and disinformation. The paper outlines historical developments in misinformation detection and how it affects social media consumption, especially among youth, and introduces LLMs and their applications in various domains. It then critically analyzes the potential of LLMs to generate and counter misinformation and disinformation in sensitive topics such as healthcare, COVID-19, and political agendas. Further, it discusses mitigation strategies, ethical considerations, and regulatory measures, summarizing previous methods and proposing future research direction toward leveraging the benefits of LLMs while minimizing misuse risks. The paper concludes by acknowledging LLMs as powerful tools with significant implications in both spreading and combating misinformation in the digital age.

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