HeadlineStanceChecker: Exploiting summarization to detect headline disinformation

Sepúlveda-Torres, Robiert; Vicente, Marta; Saquete, Estela; Lloret, Elena; Palomar, Manuel

doi:10.1016/j.websem.2021.100660

Cited by 10 publications

(3 citation statements)

References 66 publications

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“…In the abstractive approach, a given text is first interpreted in an intermediate form and then the summary is generated with sentences that are not a part of the original document. Extractive summarization method is far more popular than the abstractive summarization approach [50]. Topic modeling is a powerful NLP technique used to discover thematic clusters and often non-obvious areas of interest within large text corpora.…”

Section: Other Nlp Methodsmentioning

confidence: 99%

Fear of Artificial Intelligence? NLP, ML and LLMs Based Discovery of AI-Phobia and Fear Sentiment Propagation by AI News.

Samuel,

Khanna,

Sundar

2024

Preprint

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Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably one of the most important of dynamics of modern human society: Artificial Intelligence (AI). This study aims to explore the contributions of news media towards this phenomenon - we analyze nearly seventy thousand recent news headlines on AI, using natural language processing (NLP) informatics methods, machine learning (ML) and large language models (LLMs) to draw insights and discover dominant themes. Our theoretical framework was derived from extant literature which posits the power of fear producing articles and news headlines which produce significant impacts on public behavior even when available in small quantities. We applied extensive textual informatics methods using word and phrase frequency analytics, sentiment analysis and human experts based thematic analysis to discover insights on AI phobia inducing news headlines. Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods in NLP (exploratory informatics including BERT, Llama 2 and Mistral based topic identification), ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama 2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fear inducing verbiage in AI news headlines. The framing of AI as being dangerous or as being an existential threat to humanity can have a profound impact on public perception, and the resulting AI phobia and confusion in public perceptions are inherently detrimental to the science of AI. Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with a discussion deducing implications for society and make recommendations for education and policies that could support human identity and dignity.

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Section: Other Nlp Methodsmentioning

confidence: 99%

Fear of Artificial Intelligence? NLP, ML and LLMs Based Discovery of AI-Phobia and Fear Sentiment Propagation by AI News.

Samuel,

Khanna,

Sundar

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Extractive summarization method is far more popular than the abstractive summarization approach. (50). Topic modeling is a powerful NLP technique used to discover thematic clusters and often non-obvious areas of interest within large text corpora.…”

Section: Other Nlp Methodsmentioning

confidence: 99%

Fear of Artificial Intelligence? NLP, ML and LLMs based discovery of AI-phobia and fear sentiment propagation by AI news

Samuel,

Khanna,

Sundar

2024

Preprint

View full text Add to dashboard Cite

Confusion, fear and mixed sentiments prevail in the minds of people towards what is arguably oneof the most important of dynamics of modern human society: Artificial Intelligence (AI). Thisstudy aims to explore the contributions of news media towards this phenomenon - we analyzenearly seventy thousand recent news headlines on AI, using natural language processing (NLP)informatics methods, machine learning (ML) and large language models (LLMs) to draw insightsand discover dominant themes. Our theoretical framework was derived from extant literaturewhich posits the power of fear producing articles and news headlines which produce significantimpacts on public behavior even when available in small quantities. We applied extensive textualinformatics methods using word and phrase frequency analytics, sentiment analysis and humanexperts based thematic analysis to discover insights on AI phobia inducing news headlines.Our rigorous analysis of nearly seventy thousand headlines using multiple validation methods inNLP (exploratory informatics including BERT, Llama 2 and Mistral(1; 2) based topic identification),ML (supervised informatics) and LLMs (neural nets for sentiment classification, with BERT, Llama2 and Mistral) demonstrates the presence of an unreasonable level of emotional negativity and fearinducing verbiage in AI news headlines. The framing of AI as being dangerous or as being anexistential threat to humanity can have a profound impact on public perception, and the resultingAI phobia and confusion in public perceptions are inherently detrimental to the science of AI.Furthermore, this can also impact AI policy and regulations, and harm society. We conclude with adiscussion deducing implications for society and make recommendations for education and policiesthat could support human identity and dignity.

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“…Through an analysis of several levels of engagement within diffusion networks including tweets, retweets, and mentions, it is feasible to accurately differentiate between disinformation and mainstream news [68]. Sepúlveda-Torres et al [69] employs a two-stage classification architecture that leverages summarization approaches to modify the input for both classifiers, minimizing the volume of information to be processed while maintaining crucial information. Automatic extractive summaries as input, together with the two-stage design, is a competitive technique for detecting headline disinformation.…”

Section: A Unimodal Misinformation and Disinformation Detectionmentioning

confidence: 99%

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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HeadlineStanceChecker: Exploiting summarization to detect headline disinformation

Cited by 10 publications

References 66 publications

Fear of Artificial Intelligence? NLP, ML and LLMs Based Discovery of AI-Phobia and Fear Sentiment Propagation by AI News.

Fear of Artificial Intelligence? NLP, ML and LLMs Based Discovery of AI-Phobia and Fear Sentiment Propagation by AI News.

Fear of Artificial Intelligence? NLP, ML and LLMs based discovery of AI-phobia and fear sentiment propagation by AI news

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

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