Analysis of the Subjectivity Level in Fake News Fragments

Vieira, Lucas Lima; Jerônimo, Caio Libânio Melo; Campelo, Cláudio E. C.; Marinho, Leandro Balby

doi:10.1145/3428658.3430978

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Usually, fake content mixes different information with positive or negative feelings to mislead readers. Moreover, subjective language is commonly exploited by fake providers that focus on personal interpretation rather than factual data from an objective point of view [41]. The proposed model makes use of the sentiment algorithm suggested in [42] in order to analyse the sentiment related to the news title S i,title and the news text S i,text ; moreover, it takes advantage of a well-known sentiment analyser illustrated in [43] to evaluate the text objectivity.…”

Section: Sentiment Analysismentioning

confidence: 99%

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Marche,

Cabiddu,

Castangia

et al. 2023

IEEE Trans. Serv. Comput.

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Technological development combined with the evolution of the Internet has made it possible to reach an increasing number of people over the years and given them the opportunity to access information published on the network. The growth in the number of fake news generated daily, combined with the simplicity with which it is possible to share them, has created such a large phenomenon that it has become immediately uncontrollable. Furthermore, the quality with which malicious content is made is increasingly high so even professional experts, such as journalists, have difficulty recognizing which news is fake and which is real. This paper aims to implement an architecture that provides a service to final users that assures the reliability of news providers and the quality of news based on innovative tools. The proposed models take advantage of several Machine Learning approaches for fake news detection tasks and take into account well-known attacks on trust. Finally, the implemented architecture is tested with a well-known dataset and shows how the proposed models can effectively identify fake news and isolate malicious sources.

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Section: Sentiment Analysismentioning

confidence: 99%

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Marche,

Cabiddu,

Castangia

et al. 2023

IEEE Trans. Serv. Comput.

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“…An automatic detection method could involve knowledge base retrieval systems [ 27 ], but breakthrough knowledge may be considered misinformation. Content style analysis is another automated method, based on the assumption that there is a certain pattern in intentional news [ 31 , 35 - 37 ], but outlets may evade detection by manipulating their writing style [ 27 ]. In this study, we employed a style-based approach to fake news detection.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we employed a style-based approach to fake news detection. Generally speaking, the typical characteristics of fake news are associated with the writing style, quantity of subjective language, and sentiment lexical or incited discourse [ 26 , 27 , 31 , 35 - 37 ]. We adopted the scores of suspicion and incitement provided by the Islander news analysis system [ 28 ] in which a language model, RoBERTa [ 38 ], was trained using a supervised learning approach to analyze and score news ( Figure 2 ).…”

Section: Introductionmentioning

confidence: 99%

The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media

Chen¹,

Yiying²,

Yang³

et al. 2022

J Med Internet Res

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Background Vaccination is an important intervention to prevent the incidence and spread of serious diseases. Many factors including information obtained from the internet influence individuals’ decisions to vaccinate. Misinformation is a critical issue and can be hard to detect, although it can change people's minds, opinions, and decisions. The impact of misinformation on public health and vaccination hesitancy is well documented, but little research has been conducted on the relationship between the size of the population reached by misinformation and the vaccination decisions made by that population. A number of fact-checking services are available on the web, including the Islander news analysis system, a free web service that provides individuals with real-time judgment on web news. In this study, we used such services to estimate the amount of fake news available and used Google Trends levels to model the spread of fake news. We quantified this relationship using official public data on COVID-19 vaccination in Taiwan. Objective In this study, we aimed to quantify the impact of the magnitude of the propagation of fake news on vaccination decisions. Methods We collected public data about COVID-19 infections and vaccination from Taiwan's official website and estimated the popularity of searches using Google Trends. We indirectly collected news from 26 digital media sources, using the news database of the Islander system. This system crawls the internet in real time, analyzes the news, and stores it. The incitement and suspicion scores of the Islander system were used to objectively judge news, and a fake news percentage variable was produced. We used multivariable linear regression, chi-square tests, and the Johnson-Neyman procedure to analyze this relationship, using weekly data. Results A total of 791,183 news items were obtained over 43 weeks in 2021. There was a significant increase in the proportion of fake news in 11 of the 26 media sources during the public vaccination stage. The regression model revealed a positive adjusted coefficient (β=0.98, P =.002) of vaccine availability on the following week's vaccination doses, and a negative adjusted coefficient (β=–3.21, P =.04) of the interaction term on the fake news percentage with the Google Trends level. The Johnson-Neiman plot of the adjusted effect for the interaction term showed that the Google Trends level had a significant negative adjustment effect on vaccination doses for the following week when the proportion of fake news exceeded 39.3%. Conclusions There was a significant relationship between the amount of fake news to which the population was exposed and the number of vaccination doses administered. Reducing the amount of fake news and increasing public immunity to misinformation will be critical to maintain public health in the internet age.

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“…In particular, the spread of online misinformation has been linked to the presence of subjective knowledge, especially when it comes to scientific topics, due to the fact that it captures a person's perceived own ability to understand research [3]. Moreover, recent research work on the analysis of the subjectivity level in fake news fragments reinforces the concept that misinformation is correlated with the use of subjective language [4,5]. In general, subjectivity analysis is a classification task, which aims at categorising posts as factual or opinionated and can be used as an indicator, providing social media users with intuition about the trustworthiness of a selected post.…”

Section: Introductionmentioning

confidence: 99%

Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis

2021

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The widespread use of social networks has brought to the foreground a very important issue, the veracity of the information circulating within them. Many natural language processing methods have been proposed in the past to assess a post’s content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human beings. To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post’s subjectivity and the stance provided by the replies it has received to date, letting the user decide whether (s)he trusts or does not trust the provided information. To this end, we fine-tuned state-of-the-art transformer-based language models and compared their performance with previous related work on stance detection and subjectivity analysis. Finally, we discuss the obtained results.

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Analysis of the Subjectivity Level in Fake News Fragments

Cited by 7 publications

References 8 publications

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

Implementation of a Multi-Approach Fake News Detector and of a Trust Management Model for News Sources

The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media

Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis

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