• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1419-1426
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• 2015

Distinguishing border between news and advertising is disappearing. Traditional journalism considered editorial part deals news and ad part handle commercial messages. But now this classification is meaningless. Current news consumers do not separate advertising content and non-advertising content. In Korea, making fake news or paid news pages is becoming social problem. Fake news uses various camouflages to pretend to be real news. This paper descriptively analyzed Korean fake news cases and suggested some guidelines for publishing news. We analyzed 3 major newspaper web sites from July to September, 2014. These three newspapers publish section pages everyday containing fake news or sponsored news. Totally more than one thousand articles were selected for content analysis. We coded the numbers of fake news, day of the week, the rate of sponsored news, average fake news publication number per pages, the conformity between news and advertising, and the type of fake news. We also coded the number of sponsored news article in day sections. We used method of comparing the advertising contents and news articles. As a result, 24.8% of news article were published for the advertising sponsors. Advertorial or fake news were sometimes arranged same pages the same day. We coded the conformity between same advertising and news content. More than 60 percent (60.9%) of fake news match with their sponsors. PR style of fake news is top and advertising type of fake news is the lowest.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.219-234
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• 2019

Fake news threaten democratic elections and causes social conflicts, resulting in major damage. However, the concept of fake news is hard to define, as there is a saying, "News is not fake, fake is not news." Fake news, however, has irreversible characteristics that can not be recovered or reversed completely through post-punishment of economic and political benefits. It is also rapidly spreading in the early days. Therefore, it is very important to preemptively detect these types of articles and prevent their blind proliferation. The existing countermeasures are focused on reporting fake news, raising the level of punishment, and the media & academia to determine the authenticity of the news. Researchers are also trying to determine the authenticity by analyzing its contents. Apart from the contents of fake news, determining the behavioral characteristics of the promoters and its qualities can help identify the possibility of having fake news in advance. The online community has a fake news interception and response tradition through its long-standing community-based activities. As a result, I attempted to model the fake news by analyzing the affirmation-denial analysis and posting behavior by securing the web board crawl of the 'M community' bulletin board during the 2017 Korean presidential election period. Random forest algorithm deemed significant. The results of this research will help counteract fake news and focus on preemptive blocking through behavioral analysis rather than post-judgment after semantic analysis.

• Asian Journal for Public Opinion Research
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• v.8 no.2
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• pp.105-125
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• 2020

The purpose of this paper is to analyze 1) who created and distributed fake news, 2) the distribution channels of fake news, 3) who fake news has targeted, and 4) the effects on voting and the impact of fake news on Korean politics. In South Korea, fake news was mainly created by candidates or election campaigns. The reason is that in the wake of the impeachment of President Park Guen Hye, all the political parties in Korea used fake news as a means of mobilizing supporters for each of their candidates or parties to gain an advantage in situations involving political divisions and confrontations between the pro-impeachment, progressive young generation and anti-impeachment, conservative senior generation. Voters' media usage patterns were polarized through social network services (SNS) media and television. Fake news was mostly received through these two media outlets. According to the spreading structure of fake news in Korea, the younger generation generally uses SNS posts intended for unspecified individuals, and the older generation uses closed SNS like KakaoTalk or Naver's BAND. In the end, it is typically characteristic of the older generation to spread fake news through existing offline human networks. In the 2017 presidential election, fake news has been confirmed to have the effect of mobilizing supporters for each political party. In the presidential election, an increase in voter turnout was confirmed among those in their 20s and those in their 60s or older. Evidently, fake news influenced the election of Moon Jae-In. The influence of fake news is expected to grow further as ideological polarization and consequent political polarization continues to intensify in South Korea.

• Journal of Information Technology Applications and Management
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• v.25 no.1
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• pp.19-32
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• 2018

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University's FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4958-4970
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• 2019

As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.267-283
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• 2023

The COVID-19 pandemic, which began in December 2019 and continues to this day, has left the public needing information to help them cope with the pandemic. However, COVID-19-related fake news on social media seriously threatens the public's health. In particular, if fake news related to COVID-19 is massively spread with similar content, the time required for verification to determine whether it is genuine or fake will be prolonged, posing a severe threat to our society. In response, academics have been actively researching intelligent models that can quickly detect COVID-19-related fake news. Still, the data used in most of the existing studies are in English, and studies on Korean fake news detection are scarce. In this study, we collect data on COVID-19-related fake news written in Korean that is spread on social media and propose an intelligent fake news detection model using it. The proposed model utilizes the frequency information of parts of speech, one of the linguistic characteristics, to improve the prediction performance of the fake news detection model based on Doc2Vec, a document embedding technique mainly used in prior studies. The empirical analysis shows that the proposed model can more accurately identify Korean COVID-19-related fake news by increasing the recall and F1 score compared to the comparison model.

• 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 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.

• 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%.

• 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.