• Asian Journal for Public Opinion Research
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• v.9 no.1
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• pp.25-47
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• 2021

This study analyzes 419 fake news items published in India, a fake-news-prone country, to identify the major themes, content types, and sources of social media fake news. The results show that fake news shared on social media has six major themes: health, religion, politics, crime, entertainment, and miscellaneous; eight types of content: text, photo, audio, and video, text & photo, text & video, photo & video, and text & photo & video; and two main sources: online sources and the mainstream media. Health-related fake news is more common only during a health crisis, whereas fake news related to religion and politics seems more prevalent, emerging from online media. Text & photo and text & video have three-fourths of the total share of fake news, and most of them are from online media: online media is the main source of fake news on social media as well. On the other hand, mainstream media mostly produces political fake news. This study, presenting some novel findings that may help researchers to understand and policymakers to control fake news on social media, invites more academic investigations of religious and political fake news in India. Two important limitations of this study are related to the data source and data collection period, which may have an impact on the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1135-1143
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• 2021

Recently, text collection using a web crawler for big data analysis has been frequently performed. However, in order to collect only the necessary text from a web page that is complexly composed of numerous tags and texts, there is a cumbersome requirement to specify HTML tags and style attributes that contain the text required for big data analysis in the web crawler. In this paper, we proposed a method of extracting text using the frequency of text appearing in web pages without specifying HTML tags and style attributes. In the proposed method, the text was extracted from the DOM tree of all collected web pages, the frequency of appearance of the text was analyzed, and the main text was extracted by excluding the text with high frequency of appearance. Through this study, the superiority of the proposed method was verified.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1713-1723
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• 1999

Optical character recognition(OCR) might be the most plausible method in building database out of printed matters. This paper describes the points to be considered when one selects an OCR system in order to build database. Based on the considerations, we evaluated four commercial OCR systems, and chose one which shows the best recognition rate to build OCT-text database. The subject text, the KT-test collection, is a set of abstracts from proceedings of different printing quality, fonts, and formats. KT-test collection is also provided with typed text database. Recognition rate was calculated by comparing the recognition result with the typed text. No preprocessing such as learning and slant correction was applied to the recognition process in order to simulate a practical environment. The result shows 90.5% of character recognition rate over 970 abstracts. This recognition rate is still insufficient for practical use. The errors in OCR texts are different from those of manually typed texts. In this paper, we classify the errors in OCR texts for the further research.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.71-95
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• 2016

In recent years, text mining has been used to extract meaningful insights from the large volume of unstructured text data sets of various domains. As one of the most representative text mining applications, topic modeling has been widely used to extract main topics in the form of a set of keywords extracted from a large collection of documents. In general, topic modeling is performed according to the weighted frequency of words in a document corpus. However, general topic modeling cannot discover the relation between documents if the documents share only a few terms, although the documents are in fact strongly related from a particular perspective. For instance, a document about "sexual offense" and another document about "silver industry for aged persons" might not be classified into the same topic because they may not share many key terms. However, these two documents can be strongly related from the R&D perspective because some technologies, such as "RF Tag," "CCTV," and "Heart Rate Sensor," are core components of both "sexual offense" and "silver industry." Thus, in this study, we attempted to discover the differences between the results of general topic modeling and R&D perspective topic modeling. Furthermore, we package social issues from the R&D perspective and present a prototype system, which provides a package of news articles for each R&D issue. Finally, we analyze the quality of R&D perspective topic modeling and provide the results of inter- and intra-topic analysis.

• Proceedings of the Zoological Society Korea Conference
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• 2000.04a
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• pp.75.1-75
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• 2000

No Abstract, See Full Text

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.27-32
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• 2005

Recently there has been much interest in applying data mining to computer network intrusion detection. A new approach, based on the k-Nearest Neighbour(kNN) classifier, is used to classify Program behaviour as normal or intrusive. Each system call is treated as a word and the collection of system calls over each program execution as a document. These documents are then classified using kNN classifier, a Popular method in text mining. A simple example illustrates the proposed procedure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.27-33
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• 2014

This paper proposes a gradation image processing method for the development of a Road Sign Recognition Platform (RReP), which aims to facilitate the rapid and accurate management and surveying of approximately 160,000 road signs installed along the highways, national roadways, and local roads in the cities, districts (gun), and provinces (do) of Korea. RReP is based on GPS(Global Positioning System), IMU(Inertial Measurement Unit), INS(Inertial Navigation System), DMI(Distance Measurement Instrument), and lasers, and uses an imagery information collection/classification module to allow the automatic recognition of signs, the collection of shapes, pole locations, and sign-type data, and the creation of road sign registers, by extracting basic data related to the shape and sign content, and automated database design. Image division and merging, which were applied in this study, produce superior results compared with local binarization method in terms of speed. At the results, larger texts area were found in images, the accuracy of text recognition was improved when images had been gradated. Multi-threshold values of natural scene images are used to improve the extraction rate of texts and figures based on pattern recognition.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.506-509
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• 2017

Opinion mining is a way to analyze the emotions in the text and is used to identify the emotional state of the author and to find out the opinions of the public. As you can analyze individual emotions through opinion mining, if you analyze the text by region, you can find out the emotional state you have in each region. The regional sentiment analysis can obtain information that could not be obtained from personal sentiment analysis, and if a certain area has emotions, it can understand the cause. For regional sentiment analysis, we need text data created by region, so we need to collect data through Twitter crawling. Therefore, this paper designs a Twitter data collection system for regional sentiment analysis. The client requests the tweet data of the specific region and time, and the server collects and transmits the requested tweet data from the client. Through the latitude and longitude values of the region, it collects the tweet data of the area, and it can manage the text by region and time through collected data. We expect efficient data collection and management for emotional analysis through the design of this system.

• Journal of Korean Library and Information Science Society
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• v.24
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• pp.45-79
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• 1996

Rising publication costs and declining financial resources have resulted in renewed interest among librarians in resource sharing. Although the idea of sharing resources is not new, there is a sense of urgency not seen in the past. Driven by rising publication costs and static and often shrinking budgets, librarians are embracing resource sharing as an idea whose time may finally have come. Resource sharing in electronic environments is creating a shift in the concept of the library as a warehouse of print-based collection to the idea of the library as the point of access to need information. Much of the library's material will be delivered in electronic form, or printed. In this new paradigm libraries can not be expected to su n.0, pport research from their own collections. These changes, along with improved communications, computerization of administrative functions, fax and digital delivery of articles, advancement of data storage technologies, are improving the procedures and means for delivering needed information to library users. In short, for resource sharing to be truly effective and efficient, however, automation and data communication are essential. The possibility of using full-text online databases as a su n.0, pplement to interlibrary loan for document delivery is examined. At this point, this article presents possibility of using full-text online databases as a means to interlibrary loan for document delivery. The findings of the study can be summarized as follows : First, turn-around time and the cost of getting a hard copy of a journal article from online full-text databases was comparable to the other document delivery services. Second, the use of full-text online databases should be considered as a method for promoting interlibrary loan services, as it is more cost-effective and labour saving. Third, for full-text databases to work as a document delivery system the databases must contain as many periodicals as possible and be loaded on as many systems as possible. Forth, to contain many scholarly research journals on full-text databases, we need guidelines to cover electronic document delivery, electronic reserves. Fifth, to be a full full-text database, more advanced information technologies are really needed.