• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1119-1130
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• 2019

With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.33-48
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• 2019

The current Moon Jai-in administration in South Korea is facing serious challenges as a result of a scandal involving the manipulation of news online. Staff in Moon's camp are suspected of manipulating public opinion by creating millions of fake news comments online, contributing to Moon being elected president. This South Korean political scandal raises a number of theoretical issues with regard to new platform technologies and media manipulation. First, the incident exposes the technological limits of blocking manipulation of the news, partly because of the nature of social media and partly because of the nature of contemporary technology. Contemporary social media is often monopolistic in nature; with the majority of people are using the same platforms, and hence it is likely that they will be subject to forms of media manipulation. Second, the Korean case of news manipulation demonstrates a unique cultural aspect of Korean society. News comments and readers' replies have become a major channel of alternative news in Korea. This phenomenon is often designated as "reply journalism," since people are interested in reading the news replies of ordinary readers equally to reading news reports themselves. News replies are considered indicators of public opinion and are seen as affecting trias politica in Korean society. Third, the Korean incident of news manipulation implicates a new form of populism in the 21st century and the nature of democratic participation. This article aims to explicate key issues in media manipulation by including wider technological, cultural, and political aspects in the South Korean news media context.

• Journal of Science and Technology Studies
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• v.20 no.1
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• pp.39-85
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• 2020

With the development of information technology, fake news is becoming a serious social problem. Individual measures to manage the problem, such as fact-checking by the media, legal regulation, or technical solutions, have not been successful. The flood of fake news has undermined not only trust in the media but also the general credibility of social institutions, and is even threatening the foundations of democracy. This is why one cannot leave fake news unchecked, though it is certainly a difficult task to accomplish. The problem of fake news is not about simply judging its veracity, as no news is completely fake or unquestionably real and there is much uncertainty. Therefore, managing fake news does not mean removing them completely. Nor can the problem be left to individuals' capacity for rational judgment. Recurring fake news can easily disrupt individual decision making, which raises the need for socio-technical measures and multidisciplinary collaboration. In this study, we introduce a new public online platform for fake news management, which incorporates a multidimensional and multidisciplinary approach based on citizen science. Our proposed platform will fundamentally redesign the existing process for collecting and analyzing fake news and engaging with user reactions. People in various fields would be able to participate in and contribute to this platform by mobilizing their own expertise and capability.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.348-356
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• 2022

The main objective of this paper is to probe the extent to which misinformation propagated through the different social media platforms contribute effectively in the process of directing, shaping and reshaping societal awareness of Saudis. In so doing, this paper attempts to delve into the relationship between linguistic misinformation and societal awareness, by exploring the perception of Saudis towards social media news, particularly misinformation and the extent to which this misinformation influences the social attitudes of Saudis in terms of various societal issues. Two main research questions are addressed in this study. First, to what extent does social media misinformation affect Saudis' awareness? Second, what are the linguistic manifestations of misinformation presented in the different social platforms? Two main findings have been recorded in this study: first, misinformation significantly contributes to the societal awareness of Saudis; and, second, however misinformation is linguistically manifested at the different levels of linguistic analysis, it is highly representative at the lexicalization level of language use.

• Journal of Contemporary Eastern Asia
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• v.18 no.2
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• pp.9-15
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• 2019