• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.177-184
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• 2017

The abstract should concisely state what was done, how it was done, principal results, and their significance. It should be less than 300 words for all forms of publication. Recently social disasters have been occurring frequently in the increasing complicated social structure, and the scale of damage has also become larger. Accordingly, there is a need for a way to prevent further damage by rapidly responding to social disasters. Twitter is attracting attention as a countermeasure against disasters because of immediacy and expandability. Especially, collecting public opinion on Twitter can be used as a useful tool to prevent disasters by quickly responding. This study proposes a collecting method of Twitter public opinion through keyword analysis, issue topic tweet detection, and time trend analysis. Furthermore we also show the feasibility by selecting the case of humidifier disinfectant which is a social issue recently.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.1217-1220
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• 2001

The discomfort due to squeaks and rattles appearance inside the car has serious repercussions on consumer's opinion about acoustic quality of the vehicle and may change his economical behaviour. In this paper, we present a set of tools for squeaks and rattles processing implemented on a PC-based two channels analyser and grouped in a friendly interface. (omitted)

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1825-1844
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• 2019

Political campaigns circulate manipulative opinions in online communities to implant false beliefs and eventually win elections. Not only is this type of manipulation unfair, it also has long-lasting negative impacts on people's lives. Existing tools detect political manipulation based on a supervised classifier, which is accurate when trained with large labeled data. However, preparing this data becomes an excessive burden and must be repeated often to reflect changing manipulation tactics. We propose a practical detection system that requires moderate groundwork to achieve a sufficient level of accuracy. The proposed system groups opinions with similar properties into clusters, and then labels a few opinions from each cluster to build a classifier. It also models each opinion with features deduced from raw data with no additional processing. To validate the system, we collected over a million opinions during three nation-wide campaigns in South Korea. The system reduced groundwork from 200K to nearly 200 labeling tasks, and correctly identified over 90% of manipulative opinions. The system also effectively identified transitions in manipulative tactics over time. We suggest that online communities perform periodic audits using the proposed system to highlight manipulative opinions and emerging tactics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4701-4719
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• 2015

A Mobile Ad hoc Network (MANET) consists of mobile nodes which co-operate to forward each other's packets without the presence of any centralized authority. Due to this lack of centralized monitoring authority, MANETs have become vulnerable to various kinds of routing misbehaviour. Sometimes, nodes exhibit non-cooperating behaviour for conserving their own resources and exploiting others' by relaying their traffic. A node may even drop packets of other nodes in the guise of forwarding them. This paper proposes an efficient Reputation and Collaboration technique through route-request for handling such misbehaving nodes. It lays emphasis not only on direct observation but also considers the opinion of other nodes about misbehaving nodes in the network. Unlike existing schemes which generate separate messages for spreading second-hand information in the network, nodes purvey their opinion through route-request packet. Simulation studies reveal that the proposed scheme significantly improves the network performance by efficiently handling the misbehaving nodes in the network.

• Asian Journal for Public Opinion Research
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• v.9 no.1
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• pp.48-82
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• 2021

This research article attempts to understand the current situation of fake news on social media in India. The study focused on four characteristics of fake news based on four research questions: subject matter, presentation elements of fake news, debunking tool(s) or technique(s) used, and the social media site on which the fake news story was shared. A systematic sampling method was used to select a sample of 90 debunked fake news stories from two Indian fact-checking websites, Alt News and Factly, from December 2019 to February 2020. A content analysis of the four characteristics of fake news stories was carefully analyzed, classified, coded, and presented. The results show that most of the fake news stories were related to politics in India. The majority of the fake news was shared via a video with text in which narrative was changed to mislead users. For the largest number of debunked fake news stories, information from official or primary sources, such as reports, data, statements, announcements, or updates were used to debunk false claims.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.59-69
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• 2017

The best 'hub' to communicate with the citizen is using social media to marketing the business. However, there has several issued and the most common issue that face in critical is a capital issue. This issue is always highlight because most of automatic sentiment detection tool for Facebook or any other social media price is expensive and they lack of technical skills in order to control the tool. Therefore, in directly they have some obstacle to get faster product's feedback from customers. Thus, the personal online retailing need to struggle to stay in market because they need to compete with successful online company such as G-market. Sentiment analysis also known as opinion mining. Aim of this research is develop the tool that allow user to automatic detect the sentiment comment on social media account. RAD model methodology is chosen since its have several phases could produce more activities and output. Soppy tool will be develop using Microsoft Visual. In order to generate an accurate sentiment detection, the functionality testing will be use to find the effectiveness of this Soppy tool. This proposed automated Soppy Tool would be able to provide a platform to measure the impact of the customer sentiment over the postings on their social media site. The results and findings from the impact measurement could then be use as a recommendation in the developing or reviewing to enhance the capability and the profit to their personal online retailing company.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.29-39
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• 2020

Online communities, such as Internet portal sites and social media, have become popular since they allow users to share opinions and to obtain information anytime, anywhere. Accordingly, an increasing number of opinions are manipulated to the advantage of particular groups or individuals, and these opinions include falsified product reviews and political propaganda. Existing detection systems are built upon the characteristics of manipulated opinions for one particular time period. However, manipulation tactics change over time to evade detection systems and to more efficiently spread information, so detection systems should also evolve according to the changes. We therefore propose a system that helps observe and trace changes in manipulation tactics. This system classifies opinions into clusters that represent different tactics, and changes in these clusters reveal evolving tactics. We evaluated the system with over a million opinions collected during three election campaigns and found various changes in (i) the times when manipulations frequently occur, (ii) the methods to manipulate recommendation counts, and (iii) the use of multiple user IDs. We suggest that the operators of online communities perform regular audits with the proposed system to identify evolutions and to adjust detection systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.493-504
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• 2017

Recently, tremendous amounts of unstructured text data that is distributed through news, blogs, and social media has gained much attention from many researchers and practitioners as this data contains abundant information about various consumers' opinions. However, as the usefulness of text data is increasing, more and more attempts to gain profits by distorting text data maliciously or nonmaliciously are also increasing. This increase in spam text data not only burdens users who want to obtain useful information with a large amount of inappropriate information, but also damages the reliability of information and information providers. Therefore, efforts must be made to improve the reliability of information and the quality of analysis results by detecting and removing spam data in advance. For this purpose, many studies to detect spam have been actively conducted in areas such as opinion spam detection, spam e-mail detection, and web spam detection. In this study, we introduce core concepts and current research trends of spam detection and propose a methodology to detect the spam tag of a blog as one of the challenging attempts to improve the reliability of blog information.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.113-120
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• 2018

Today, the development of information and communication technology is rapidly increasing the number of users of network-based service, and enables real-time information sharing among users on the Internet. There are various methods in the information sharing process, and information sharing based on portal service is generally used. However, the process of information sharing serves as a cause of illegal activities in order to amplify the social interest of the relevant stakeholders. Public opinion attack using macro function can distort normal public opinion, so security measures are urgent. Therefore, security measures are urgently needed. Macro attacks are generally defined as attacks in which illegal users acquire multiple IP or ID to manipulate public opinion on the content of a particular web page. In this paper, we analyze network path information based on traceback for macro attack of a specific user, and then detect multiple access of the user. This is a macro attack when the access path information for a specific web page and the user information are matched more than once. In addition, when multiple ID is accessed for a specific web page in the same region, it is not possible to distort the overall public opinion on a specific web page by analyzing the threshold count value.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.57-69
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• 2020

As more people share their opinions in online communities, such as Internet portals and social networking services, more opinions are manipulated for the benefit of particular individuals and groups. In particular, when manipulations occur for political purposes, they influence election results as well as government policies and the quality of life. This type of manipulation has targeted the general public, and their analysis and detection has also focused on such manipulation. However, to more efficiently spread propaganda, recent manipulations have targeted common interest groups(e.g., a group of those interested in real estate) and propagated information whose content and style are customized to those groups. This work characterizes such manipulations on common interest groups and proposes method to detect manipulations. To this end, we collected and analyzed opinions posted on 10 common interest groups before and after an election. As a result, we found that manipulations on common interest groups indeed occurred and were gradually increasing toward the election date. We also proposed a detection system that examines individual opinions, their authors, and their collaborators. Using the collected opinions, we demonstrated that the proposed system can accurately classify more than 90% of manipulated opinions and that many of these opinions were posted by multiple collaborators. We believe that regular audits of opinions using the proposed system can quickly isolate manipulations and decrease their impact. Moreover, the proposed features can be used to identify manipulations in domains other than politics.