Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Data Collection
Data Pre-Processing and Feature Engineering
Results
Urls Analysis
Discussion
Conclusion
References
All Topics
Computer Science

Detection of Fake News on COVID-19 on Web Search Engines

Profile image of andrea rapisardaandrea rapisarda

2021, Frontiers in Physics

https://doi.org/10.3389/FPHY.2021.685730
visibility

…

description

18 pages

link

1 file

Abstract

In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which is misleading or even harmful, which has widely spread around the globe. Although social media are increasingly being used as the information source, web search engines, such as Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through search engines. By using a real-...

Related papers

Combating the infodemic: COVID-19 induced fake news recognition in social media networks
Shankar Biradar

Complex & Intelligent Systems, 2022

COVID-19 has caused havoc globally due to its transmission pace among the inhabitants and prolific rise in the number of people contracting the disease worldwide. As a result, the number of people seeking information about the epidemic via Internet media has increased. The impact of the hysteria that has prevailed makes people believe and share everything related to illness without questioning its truthfulness. As a result, it has amplified the misinformation spread on social media networks about the disease. Today, there is an immediate need to restrict disseminating false news, even more than ever before. This paper presents an early fusion-based method for combining key features extracted from context-based embeddings such as BERT, XLNet, and ELMo to enhance context and semantic information collection from social media posts and achieve higher accuracy for false news identification. From the observation, we found that the proposed early fusion-based method outperforms models that...

View PDFchevron_right
Evaluating Intelligent Methods for Detecting COVID-19 Fake News on Social Media Platforms
Dr. Mohd Waris Khan

Electronics

The advent of Internet-based technology has made daily life much easy than earlier days. The exponential rise in the popularity of social media platforms has not only connected people from faraway places, but has also increased communication among humans. However, in several instances, social media platforms have also been utilized for unethical and criminal activities. The propagation of fake news on social media during the ongoing COVID-19 pandemic has deteriorated the mental and physical health of people. Therefore, to control the flow of fake news regarding the novel coronavirus, several studies have been undertaken to automatically detect the fake news about COVID-19 using various intelligent techniques. However, different studies have shown different results on the performance of the predicting models. In this paper, we have evaluated several machine learning and deep learning models for the automatic detection of fake news regarding COVID-19. The experiments were carried out ...

View PDFchevron_right
COVID-19 Infodemic - Understanding content features in detecting fake news using a machine learning approach
Eric G . C . Laporte

Malaysian Journal of Computer Science, 2023

The use of content features, particularly textual and linguistic for fake news detection is underresearched, despite empirical evidence showing the features could contribute to differentiating real and fake news. To this end, this study investigates a selection of content features such as word bigrams, part of speech distribution etc. to improve fake news detection. We performed a series of experiments on a new dataset gathered during the COVID-19 pandemic and using Decision Tree, K-Nearest Neighbor, Logistic Regression, Support Vector Machine and Random Forest. Random Forest yielded the best results, followed closely by Support Vector Machine, across all setups. In general, both the textual and linguistic features were found to improve fake news detection when used separately, however, combining them into a single model did not improve the detection significantly. Differences were also noted between the use of bigrams and part of speech tags. The study shows that textual and linguistic features can be used successfully in detecting fake news using the traditional machine learning approach as opposed to deep learning.

View PDFchevron_right
Detecting and Mitigating the Dissemination of Fake News : Challenges and Future Research Opportunities
International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

With increasing popularity in the use of social media for news consumption, the substantial widespread dissemination of fake news has the potential to adversely affect individuals as well as the society as a whole. Even in the midst of the current covid-19 pandemic, false information shared on websites such as WhatsApp, Twitter, and Facebook have the potential to cause panic and shock a large number of people in various parts of the world. These misconceptions obscure healthier habits and encourage incorrect procedures, which aid in the transmission of the virus and, as a result, result in poor physical and psychological health results for individuals. Therefore, it is a research challenge to validate the source, content and publisher of a news article for classifying it as genuine or fake. The existing systems and techniques are not efficient enough to accurately classify a given news based on its statistical rating. Machine learning plays an imperative part in categorizing news data and information, despite some limitations. Our project not only aims on fake news detection but also on generation of real news once the fake news is detected. We propose a user-friendly webpage on which the user enters the news article statement. It is then tested by our machine learning algorithm which then classifies it as genuine or fake, after which the important words are extracted from the statement which helps to get the corresponding genuine news by scraping it from trusted sources and show it to the user. We have compared two machine learning algorithms in this which are- Passive Aggressive Classifier and Naïve Bayes algorithm. We got an accuracy of about 93.5% from Passive Aggressive Classifier and about 83.5% from Naïve Bayes algorithm.

View PDFchevron_right
Fake News Classification and Disaster in case of Pandemic: COVID-19
k190948 Tooba Qazi

Research Square (Research Square), 2022

Presently, one of the very common and prevalent means of consumption and spread of the news is social media. Unfortunately, this does not include news with merit only but also a wide array of false news, leaving negative imprints and in uences in the world. Specially now, when there is a birth of a new story and news every second, the widespread of false news is a matter of concern. These issues and concerns have made it undeniably essential to limit the spread of incorrect data by analyzing and detecting data. In the period of this kind of tragedy where the knowledge base is not appropriate to classify the news as false or true since the topic is unique and therefore no prior information is known. To stop this misguidance against human health issues, this work aims to examine the imperative aspects of social networking and the fake News data and information it provides. For confronting this issue, we will be building a data set of COVID-19 collected from social networking sites and detecting the visible patterns and characteristics using social analysis tools in both fake and real news by using the different Machine Learning algorithms in collaboration with Deep learning techniques. Wherever a problem involving fake news detection is concerned, deep learning techniques have been applied as they skip feature engineering and automatically represent features.

View PDFchevron_right
Fake news: the role of search engines and website content. [0185]
Melius Weideman

2018

The concept of fake news is quite old - a comic strip from 1894 shows journalists with a news item bearing this name. It can be loosely defined as being false (often sensational) information, appearing to be truthful news, being spread to influence public political or other views. Various generators of fake news have been identified - in almost all cases with a clear intent to misinform. Libraries, having always been a source of accurate and truthful information, are being pressurized into acting on this problem. The general perception (especially amongst the younger generation) that whatever the Internet says must be true, has not helped in this situation. The Trump election of 2016 has shown that social media (Facebook specifically) can be a popular and powerful platform for distributing fake news. Twitter has also been used to produce a false impression of a given situation, as used by the “Russian trolls”. Many free software programs have been identified, which can be used to generate large amounts of fake content in a very short time, based on supplied seed content. In all known cases, it was found that website content being generated has been at the centre of the fake news situation. This content generation could be done using ordinary web design platforms, any content management system, or as was done in most cases, using a popular social media platform. This is not a new phenomenon, as it has been done for many years in the world of black-hat search engine optimisation, to create “false” content in an attempt to impress the search engine algorithms. The way search engine crawlers and algorithms operate is at the centre of the fake news phenomenon. In conclusion, there seems to be no easy way of preventing fake news from reaching the consumer. This is a result of the ease with which website content can be generated and added to the Internet, and the lack of any gate-keeper function (for example, a librarian or editor) on webpages in general. The use of sentiment analysis should be investigated further in the striving towards finding a solution for this problem, as well as adapting search engine algorithms to spot fake news, as is currently being done to identify thin content, keyword spamdexing and other black-hat technologies.

View PDFchevron_right
Illusion of Truth: Analysing and Classifying COVID-19 Fake News in Brazilian Portuguese Language
Antonia Egli

Big Data and Cognitive Computing

Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically. In this study, we explore the use of machine lea...

View PDFchevron_right
The CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection
Gautam Shahi

Lecture Notes in Computer Science, 2022

The fifth edition of the CheckThat! Lab is held as part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting various factuality tasks in seven languages: Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Task 1 focuses on disinformation related to the ongoing COVID-19

View PDFchevron_right
Fake news outbreak 2021: Can we stop the viral spread
Tanveer Khan, Adnan Akhunzada

Journal of network & Computer Applications

Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. However, easy access to information does not equal an increased level of public knowledge. Unlike traditional media channels, social networks also facilitate faster and wider spread of disinformation and misinformation. Viral spread of false information has serious implications on the behaviours, attitudes and beliefs of the public, and ultimately can seriously endanger the democratic processes. Limiting false information's negative impact through early detection and control of extensive spread presents the main challenge facing researchers today. In this survey paper, we extensively analyze a wide range of different solutions for the early detection of fake news in the existing literature. More precisely, we examine Machine Learning (ML) models for the identification and classification of fake news, online fake news detection competitions, statistical outputs as well as the advantages and disadvantages of some of the available data sets. Finally, we evaluate the online web browsing tools available for detecting and mitigating fake news and present some open research challenges.

View PDFchevron_right
Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic
Molla Rashied Hussein

Healthcare

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial ne...

View PDFchevron_right

References (46)

  1. J.D. Greer. Evaluating the credibility of online information: A test of source and advertising influence. Mass Communication & Society, pages 11-28, 2003.
  2. Z. Xianjin, Y. Haijuan, Y. Yalan, L. Kunfeng, and H. Chengsong. Exploring the effect of social media information quality, source credibility and reputation on informational fit-to-task: Moderating role of focused immersion. Computers in Human Behavior, 79:227-237, 2018.
  3. W. Y. Chou, Y. M. Hunt, E. B. Beckjord, R. P. Moser, and B. W. Hesse. Social media use in the united states: implications for health communication. Journal of medical Internet research, 11(4), 2009.
  4. J. Y. Breland, L. M. Quintiliani, K. L. Schneider, C. N. May, and S. Pagoto. Social media as a tool to increase the impact of public health research. American journal of public health, 107(12):1890-1891, 2017.
  5. S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1):107-117, 1998.
  6. S. Manjesh, T. Kanakagiri, P. Vaishak, V. Chettiar, and G. Shobha. Clickbait pattern detection and classification of news headlines using natural language processing. 2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), pages 1-5, 2017.
  7. P. Bourgonje, J. M. Schneider, and G. Rehm. From clickbait to fake news detection: An approach based on detecting the stance of headlines to articles. Proceedings of the 2017 EMNLP workshop: Natural language processing meets journalism. Association for Computational Linguistics, pages 84-89, 2017.
  8. M. Aldwairi and A. Alwahedi. Detecting fake news in social media networks. Procedia Computer Science, 141:215-222, 2018.
  9. M.K. Elhadad, K.F. Li, and F. Gebali. Detecting misleading information on covid-19. IEEE Access, 8:165201- 165215, 2020.
  10. V. Agarwal, H.P. Sultana, S. Malhotra, and A. Sarkar. Analysis of classifiers for fake news detection. Procedia Computer Science, 165:377-383, 2019.
  11. Reuters. 2020. https://www.reuters.com, Accessed: 2020-07-20.
  12. Y. Madani, M. Erritali, and B. Bouikhalene. Using artificial intelligence techniques for detecting covid-19 epidemic fake news in moroccan tweets. Results in Physics, 25:104266, 2021.
  13. S. Helmstetter and H. Paulheim. Collecting a large scale dataset for classifying fake news tweets using weak supervision. Future Internet, 13:114, 2021.
  14. A.S. Desuky and S. Hussain. An improved hybrid approach for handling class imbalance problem. Arabian Journal for Science and Engineering (AJSE), 46:3853-3864, 2021.
  15. M.M. Al-Rifaie and H.A. Alhakbani. Handling class imbalance in direct marketing dataset using a hybrid data and algorithmic level solutions. 2016 SAI Computing Conference (SAI), pages 446-451, 2016.
  16. M. Sokolova, N. Japkowicz, and S. Szpakowicz. Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation. AI 2006: Advances in Artificial Intelligence, Lecture Notes in Computer Science, 4304:1015-1021, 2006.
  17. G. H. Lee and S. Y. Shin. Federated learning on clinical benchmark data: Performance assessment. Journal of Medical Internet Research, 22:446-451, 2020.
  18. Financial Times. 2021. https://ig.ft.com/coronavirus-lockdowns/, Accessed: 2021-04-27.
  19. S. B. Naeem, R. Bhatti, and A. Khan. An exploration of how fake news is taking over social media and putting public health at risk. Health information and libraries journal, 2020.
  20. Poynter.org. 2020. https://www.poynter.org/, Accessed: 2020-03-15.
  21. C. Beleites, U. Neugebauer, T. Bocklitz, C. Krafft, and J. Popp. Sample size planning for classification models. Analytica Chimica Acta, 2013.
  22. S. Sarica and J. Luo. Stopwords in technical language processing. ArXiv, abs/2006.02633, 2020.
  23. Y. Zhang, R. Jin, and Z.H. Zhou. Understanding bag-of-words model: a statistical framework. International Journal of Machine Learning and Cybernetics, pages 43-52, 2010.
  24. B. Al Asaad and M. Erascu. A tool for fake news detection. 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pages 379-386, 2018.
  25. H. Ahmed, Traore I., and S. Saad. Detecting opinion spams and fake news using text classification. Security and Privacy, 1:e9, 2017.
  26. E. Zhu, Y. Chen, C. Ye, X. Li, and F. Liu. Ofs-nn: An effective phishing websites detection model based on optimal feature selection and neural network. IEEE Access, 7:73271-73284, 2019.
  27. T. Li, G. Kou, and Y. Peng. Improving malicious urls detection via feature engineering: Linear and nonlinear space transformation methods. Information Systems, 91:101494, 2020.
  28. A. Luque, A. Carrasco, A. Martín, and A. de las Heras. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91:216-231, 2019.
  29. W. Xie, G. Liang, Z. Dong, B. Tan, and B. Zhang. An improved oversampling algorithm based on the samples' selection strategy for classifying imbalanced data. Mathematical Problems in Engineering, pages 1-13, 2019.
  30. K. Agrawal, Y. Baweja, D. Dwivedi, R. Saha, P. Prasad, S. Agrawal, S. Kapoor, P. Chaturvedi, N. Mali, V. U. Kala, and V. Dutt. A comparison of class imbalance techniques for real-world landslide predictions. 2017 International Conference on Machine Learning and Data Science (MLDS), pages 1-8, 2017.
  31. J. Lever, M. Krzywinski, and N. Altman. Model selection and overfitting. Nature Methods, 13:703-704, 2013.
  32. X. Cho, D. Hoa, and V. Tisenko. Malicious url detection based on machine learning. International Journal of Advanced Computer Science and Applications, 2020.
  33. G. Wejinya and S. Bhatia. Machine learning for malicious url detection. In: Tuba M., Akashe S., Joshi A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, 1270, 2021.
  34. G. Di Leo and F. Sardanelli. Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach. European radiology experimental, 4(1):18, 2020.
  35. K. P. Vatcheva, M. Lee, J. B. McCormick, and M. H. Rahbar. Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology (Sunnyvale, Calif.), 6(2):227, 2016.
  36. J. Gómez-Ramírez, M. Ávila Villanueva, and M.Á. Fernández-Blázquez. Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods. Scientific Reports, 10:20630, 2020.
  37. S. Garera, N. Provos, M. Chew, and A. D. Rubin. A framework for detection and measurement of phishing attacks. Proceedings of the 2007 ACM Workshop on Recurring Malcode -WORM '07, 2007.
  38. S.C. Jeeva and E.B. Rajsingh. Intelligent phishing url detection using association rule mining. Human-centric Computing and Information Sciences (HCIS), 6, 2016.
  39. S. Sankhwar, P. Dhirendra, and R. Khan. Email phishing: An enhanced classification model to detect malicious urls. ICST Transactions on Scalable Information Systems, 6:158529, 2018.
  40. M. Bekkar, H. Djema, and T.A. Alitouche. Evaluation measures for models assessment over imbalanced data sets. Journal of Information Engineering and Applications, 3:27-38, 2013.
  41. L. A. Jeni, J. F. Cohn, and F. De La Torre. Facing imbalanced data recommendations for the use of performance metrics. Proceedings -2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013, pages 245-251, 2013.
  42. R. Dhanalakshmi and C. Chellappan. Detecting malicious urls in e-mail -an implementation. AASRI Procedia, 4:125-131, 2013.
  43. G. Sonowal. Phishing email detection based on binary search feature selection. SN Computer Science, 1:191, 2020.
  44. W. Wang and K.E. Shirley. Breaking bad: Detecting malicious domains using word segmentation. IEEE Web 2.0 Security and Privacy Workshop (W2SP), 2015.
  45. G. Palaniappan, S. Sangeetha, Balaji. Rajendran, S. Sanjay, Goyal, and B. S. Bindhumadhava. Malicious domain detection using machine learning on domain name features, host-based features and web-based features. Procedia Computer Science, 171:654-661, 2020.
  46. Factcheck.org. 2016. https://www.factcheck.org/, Accessed: 2016-11-18.

Related papers

Fake News Detection: Covid-19 Perspective
Shaheena Sultana, Binoyee Farha, Ziaur Shamim

Global Journal of Computer Science and Technology

The development of social media has contributed to a remarkable rise in the spread of fake news. Today people rely more on online news outlets. The chance of receiving fake news on an online platform is high. As we went through a pandemic and the Covid-19 was the most absorbing topic of 2020, much news on Covid-19 was published every day in traditional media and social media. Among that news, some are fake. In this work, we have collected a new dataset for detecting fake news from traditional media on Covid-19. We have gathered more than 3000 pieces of news from traditional media out of the 170 are fake ones that were collected from fact-checking sites. Then we have tested the existing four classification algorithms with our dataset using Count Vectorizer and TF-IDF. We have merged 170 fake news with four scales of true news and analyzed the outcome.

View PDFchevron_right
Analysis of the Popularity Rate of Extracted Keywords From True and Fake News Related to Covid-19
Lívia Kelebercová

At the end of 2019, the whole world began to perceive information about the new infectious virus SARS-CoV-2, which in a short time triggered the COVID-19 pandemic with a global impact on human lives. In the following years, the pandemic had a significant impact on the information on the Internet. News sites have published several new articles about COVID-19 that may not always be true. The paper deals with the extraction of keywords. A supervised technique called KeyBERT was used to extract keywords from the dataset, which contains true and fake news related to COVID-19. We used Google Trends as a tool to obtain the most popular search queries related to our extracted keywords. This paper proves that the level of popularity of related searches contributes to the identification of fake news and we examined that our supposed approach may be included in the models to identify fake news to increase their accuracy.

View PDFchevron_right
Exploring Content-Based and Meta-Data Analysis for Detecting Fake News Infodemic: A case study on COVID-19
'Seun AJAO

The coronavirus pandemic (COVID-19) is probably the most disruptive global health disaster in recent history. It negatively impacted the whole world and virtually brought the global economy to a standstill. However, as the virus was spreading, infecting people and claiming thousands of lives so was the spread and propagation of fake news, misinformation and disinformation about the event. These included the spread of unconfirmed health advice and remedies on social media. In this paper, false information about the pandemic is identified using a content-based approach and metadata curated from messages posted to online social networks. A content-based approach combined with metadata as well as an initial feature analysis is used and then several supervised learning models are tested for identifying and predicting misleading posts. Our approach shows up to 93% accuracy in the detection of fake news related posts about the COVID-19 pandemic.

View PDFchevron_right
Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News
Bibhas Sharma

Cornell University - arXiv, 2022

COVID-19 impacted every part of the world, although the misinformation about the outbreak traveled faster than the virus. Misinformation spread through online social networks (OSN) often misled people from following correct medical practices. In particular, OSN bots have been a primary source of disseminating false information and initiating cyber propaganda. Existing work neglects the presence of bots that act as a catalyst in the spread and focuses on fake news detection in 'articles shared in posts' rather than the post (textual) content. Most work on misinformation detection uses manually labeled datasets that are hard to scale for building their predictive models. In this research, we overcome this challenge of data scarcity by proposing an automated approach for labeling data using verified fact-checked statements on a Twitter dataset. In addition, we combine textual features with user-level features (such as followers count and friends count) and tweet-level features (such as number of mentions, hashtags and urls in a tweet) to act as additional indicators to detect misinformation. Moreover, we analyzed the presence of bots in tweets and show that bots change their behavior over time and are most active during the misinformation campaign. We collected 10.22 Million COVID-19 related tweets and used our annotation model to build an extensive and original ground truth dataset for classification purposes. We utilize various machine learning models to accurately detect misinformation and our best classification model achieves precision (82%), recall (96%), and false positive rate (3.58%). Also, our bot analysis indicates that bots generated approximately 10% of misinformation tweets. Our methodology results in substantial exposure of false information, thus improving the trustworthiness of information disseminated through social media platforms.

View PDFchevron_right
Analysis of Fake News In Social Medias for Four Months during Lockdown in COVID-19
Samir Bandyopadhyay

2020

All over the world, development of micro blog and other social platform indicate that Social Media is now the focus and trend of the Internet. Daily life, study and work are influenced by news in Social Medias . Micro blog is new emergent type of media and it spreads information rapidly in the crowd in recent years. Suppose an user searches for specific information about one topic on micro blog. He/she found easily plenty of information related to his/her search in social medias. The problem is to find out the correct information. Normally, multi-document summarization method deals with a collection of documents about one topic for extracting the valuable points and discards useless information. Actually, it needs to extract the topic content by adding topic factors and social patterns. Topic factor is the lexical information related to the topic. Social pattern relates to special interactive mode owned by online social network, such as comment and repost. People has been seen the f...

View PDFchevron_right

Related topics

  • Computer Science

    • Cited by

    A Systematic Literature Review and Meta-Analysis of Studies on Online Fake News Detection
    Timothy Adeliyi

    Information, 2022

    This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

    View PDFchevron_right
    The Breaking News Effect and Its Impact on the Credibility and Trust in Information Posted on Social Media
    Grigorie Sanda

    Electronics

    The development of social media has triggered important changes in our society and in the way consumers read and trust online information. The presence of consumers in the online environment exposes them to a greater extent to various instances of fake news, which are spread more or less intentionally. Sensational and breaking-news-style information are one of the ways in which consumers’ attention is attracted, by posting exaggerated or distorted information. The objective of our research is to determine the impact of sensational and breaking news headlines on content credibility. In a mediation model, we show that the perception of sensationalism mediates the relation between the presence of breaking news headlines and trust in the content of the information. Based on our proposed model, the existence of breaking news headlines increases the consumers’ perception of sensationalism and reduces trust in news content. These results have important implications for patterns of news con...

    View PDFchevron_right
    Investigating Fake and Reliable News Sources Using Complex Networks Analysis
    andrea rapisarda

    Frontiers in Physics

    The rise of disinformation in the last years has shed light on the presence of bad actors that produce and spread misleading content every day. Therefore, looking at the characteristics of these actors has become crucial for gaining better knowledge of the phenomenon of disinformation to fight it. This study seeks to understand how these actors, meant here as unreliable news websites, differ from reliable ones. With this aim, we investigated some well-known fake and reliable news sources and their relationships, using a network growth model based on the overlap of their audience. Then, we peered into the news sites’ sub-networks and their structure, finding that unreliable news sources’ sub-networks are overall disassortative and have a low–medium clustering coefficient, indicative of a higher fragmentation. The k-core decomposition allowed us to find the coreness value for each node in the network, identifying the most connectedness site communities and revealing the structural org...

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved