• Title/Summary/Keyword: fake news

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Fake News Detection on Social Media using Video Information: Focused on YouTube (영상정보를 활용한 소셜 미디어상에서의 가짜 뉴스 탐지: 유튜브를 중심으로)

  • Chang, Yoon Ho;Choi, Byoung Gu
    • The Journal of Information Systems
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    • v.32 no.2
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    • pp.87-108
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    • 2023
  • Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

Fake News Checking Tool Based on Siamese Neural Networks and NLP (NLP와 Siamese Neural Networks를 이용한 뉴스 사실 확인 인공지능 연구)

  • Vadim, Saprunov;Kang, Sung-Won;Rhee, Kyung-hyune
    • Proceedings of the Korea Information Processing Society Conference
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    • 2022.05a
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    • pp.627-630
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    • 2022
  • Over the past few years, fake news has become one of the most significant problems. Since it is impossible to prevent people from spreading misinformation, people should analyze the news themselves. However, this process takes some time and effort, so the routine part of this analysis should be automated. There are many different approaches to this problem, but they only analyze the text and messages, ignoring the images. The fake news problem should be solved using a complex analysis tool to reach better performance. In this paper, we propose the approach of training an Artificial Intelligence using an unsupervised learning algorithm, combined with online data parsing tools, providing independence from subjective data set. Therefore it will be more difficult to spread fake news since people could quickly check if the news or article is trustworthy.

Development of a Fake News Detection Model Using Text Mining and Deep Learning Algorithms (텍스트 마이닝과 딥러닝 알고리즘을 이용한 가짜 뉴스 탐지 모델 개발)

  • Dong-Hoon Lim;Gunwoo Kim;Keunho Choi
    • Information Systems Review
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    • v.23 no.4
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    • pp.127-146
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    • 2021
  • Fake news isexpanded and reproduced rapidly regardless of their authenticity by the characteristics of modern society, called the information age. Assuming that 1% of all news are fake news, the amount of economic costs is reported to about 30 trillion Korean won. This shows that the fake news isvery important social and economic issue. Therefore, this study aims to develop an automated detection model to quickly and accurately verify the authenticity of the news. To this end, this study crawled the news data whose authenticity is verified, and developed fake news prediction models using word embedding (Word2Vec, Fasttext) and deep learning algorithms (LSTM, BiLSTM). Experimental results show that the prediction model using BiLSTM with Word2Vec achieved the best accuracy of 84%.

The Effect of Social Anxiety on Fake News Acceptance Attitude : Focused on the Use Degree of SNS (사회불안감이 가짜뉴스 수용태도에 미치는 영향 : SNS 이용정도를 중심으로)

  • Oh, Ji-Hee
    • Journal of Korea Entertainment Industry Association
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    • v.15 no.6
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    • pp.179-191
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    • 2021
  • Social anxiety continues due to the emergence and spread of covid-19 infections. In this situation, false information related to the covid-19 infection is distributed through SNS in the form of fake news, which is a stumbling block to overcoming the national crisis. This study tried to present a theoretical basis for the establishment of policies for the regulation and eradication of fake news circulated through SNS by examining the effect of social anxiety on the fake news acceptance attitude by focusing on the use degree of SNS. For this study, a survey of 380 university students in the Seoul metropolitan area was conducted, and 336 data collected among them were analyzed as SPSS 25.0 and AMOS 23.0. According to the analysis results, social anxiety has a positive effect on the fake news acceptance attitude and the use degree of SNS, also the use degree of SNS has a positive effect on the fake news acceptance attitude. In addition, social anxiety has been confirmed to have a positive effect on fake news acceptance attitude through the use degree of SNS. These results empirically confirm the relationship between social anxiety, fake news acceptance attitude, and the use degree of SNS.

A Study on Automated Fake News Detection Using Verification Articles (검증 자료를 활용한 가짜뉴스 탐지 자동화 연구)

  • Han, Yoon-Jin;Kim, Geun-Hyung
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.12
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    • pp.569-578
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    • 2021
  • Thanks to web development today, we can easily access online news via various media. As much as it is easy to access online news, we often face fake news pretending to be true. As fake news items have become a global problem, fact-checking services are provided domestically, too. However, these are based on expert-based manual detection, and research to provide technologies that automate the detection of fake news is being actively conducted. As for the existing research, detection is made available based on contextual characteristics of an article and the comparison of a title and the main article. However, there is a limit to such an attempt making detection difficult when manipulation precision has become high. Therefore, this study suggests using a verifying article to decide whether a news item is genuine or not to be affected by article manipulation. Also, to improve the precision of fake news detection, the study added a process to summarize a subject article and a verifying article through the summarization model. In order to verify the suggested algorithm, this study conducted verification for summarization method of documents, verification for search method of verification articles, and verification for the precision of fake news detection in the finally suggested algorithm. The algorithm suggested in this study can be helpful to identify the truth of an article before it is applied to media sources and made available online via various media sources.

Information Sharing and Evaluation as Determinants of Spread of Fake News on Social Media among Nigerian Youths: Experience from COVID-19 Pandemic

  • Sulaiman, Kabir Alabi;Adeyemi, Ismail Olatunji;Ayegun, Ibrahim
    • International Journal of Knowledge Content Development & Technology
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    • v.10 no.4
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    • pp.65-82
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    • 2020
  • This study examined information sharing and evaluation as determinants of the spread of fake news among Nigerian youths on social media using experience from COVID-19 pandemic. A descriptive survey design was adopted for the study and a Web-based questionnaire (Google Forms) was used to collect data for the study. The total responses of 278 were collected from the participants, which represents the unit of analysis. The finding of the study revealed that most Nigerian youths used Facebook, Twitter, WhatsApp and Instagram to share information on COVID-19. However, only a few Nigerians used Linkedln and other types of social media to share information on COVID-19. It was also found that building a relationship with social media communities, enjoyment and risk taking, and political inclination influence the sharing behavior of Nigerian youths during the COVID-19 pandemic. Results show that social media handle/page found sharing of fake news on COVID-19 especially on the treatment, vaccines numbers of cases and symptoms. The study concludes that there is a positive relationship between information evaluation and the spreading of fake news on COVID-19 among Nigerians. Information sharing and evaluation should be done with the utmost level of objectivity and sincerity.

An Exploratory Study on the Establishment and Provision of Universal Literacy for Sustainable Development in the Era of Fake News (가짜뉴스의 시대, 지속가능한 발전을 위한 보편적 리터러시의 구축 및 제공에 대한 실험적 연구)

  • Lee, Jeong-Mee
    • Journal of the Korean Society for Library and Information Science
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    • v.55 no.1
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    • pp.85-106
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    • 2021
  • The purpose of this study is to examine the concept and definition of fake news focusing on misinformation/false information and is to examine the ways in which our society can respond to the distortion of social reality and damage to democracy caused by information distortion such as fake news. To do this, the concept of fake news was examined based on the level of facticity and intention to device, and our social environment in which fake news was created and spread was examined from the perspective of datafication. In this environment, the library community, which plays a pivotal role in human access to and use of information, argued that it should strive to establish and provide universal literacy education in order to realize the Sustainable Development Goals of the UN 2030 agenda. The core of universal literacy education is to understand the society by investigating and analyzing data communication types according to the degree of datafication and the political, economic, social, and cultural background of society. For this reason, it was concluded that universal literacy should be implemented flexibly according to the degree of datafiation and users of each society.

An Analysis of Trends on the Safety Area Utilizing Big Data : Focused on Fake News (빅데이터를 활용한 안전분야 트렌드 분석 : 가짜뉴스(fake news)를 중심으로)

  • Joo, Seong Bhin
    • Convergence Security Journal
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    • v.17 no.5
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    • pp.111-119
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    • 2017
  • As of March 2017, fake news is largely focused on political issues. Outside the country, main issues of the fake news have been a hot topic in the US presidential election in 2016 and emerged as a new political and social problem in countries like Germany and France. In Korea, issues of the fake news are also linked with political issues such as presidential impeachment and prosecution, impeachment quota, early election, etc. This phenomenon has recently led to the production and spread of fake news related to safety and security issues as well as political issues in connection with various methods of generating articles and sharing information. As a result, there is a high possibility that the information will be transformed into information that can cause considerable confusion to the public. Therefore, the recognition of such problems means that it is important at this point to consider the related situation analysis and effective countermeasures. To do this, we tried to make accurate and meaningful analysis for the diagnosis, analysis, forecasting and management of issues utilizing Big Data. As a result, it is found that the fake news is continuously generated in relation to the safety and security issue as well as the political issue in the South Korea, and differs from the general form occurring outside the country.

Motivation Versus Intention of Sharing Fake News Among Social Media Users during the Pandemic - A SEM Model

  • Alvi, Irum;Saraswat, Niraja
    • Journal of Contemporary Eastern Asia
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    • v.20 no.2
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    • pp.40-62
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    • 2021
  • Are intentions important in deciding the outcome of an action such as sharing misinformation among social media users during the pandemic? What is their role and how far they are important for the very act of fake sharing news? The social media users' actions on the social platform are determined by what they plan to do themselves; however, their motivation has an immense role to play in the dissemination of fake news on social media. The study proposes a conceptual model for understanding how select factors affect fake news sharing motivation and intentions of social media users. The study scrutinizes the relationship between content and context, fear of missing out (FoMO), news verification and news sharing gratification on the motivation and intention of social media users of networked Asian society. Empirical Data were drawn from social media users (N = 243) from India, using an online questionnaire based on prior studies and structural equation modeling (SEM) approach was used to analyze the data collected. Results indicate that news content, news verification, and news sharing gratification have a direct and positive relationship with sharing motivation. On the other hand, news context and content, FoMO and news sharing gratification have a positive significant relationship with sharing intention. Likewise, it was discovered that news verification will decrease sharing intention of the social media users. However, news context, that is the pandemic in the case of the present study and FoMO were not identified as determinant variables for sharing motivation among social media users. The research limitations and further scope were discussed.

A Study on the Effect of the Document Summarization Technique on the Fake News Detection Model (문서 요약 기법이 가짜 뉴스 탐지 모형에 미치는 영향에 관한 연구)

  • Shim, Jae-Seung;Won, Ha-Ram;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.25 no.3
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    • pp.201-220
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    • 2019
  • Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.