• Journal of KIISE
• /
• v.45 no.2
• /
• pp.165-174
• /
• 2018

An online community is a virtual group whose members share their interests and hobbies anonymously with nicknames unlike Social Network Services. However, there are malicious user problems such as users who write offensive contents and there may exist data fragmentation problems in which the data of the same user exists in different nicknames. In addition, nicknames are frequently changed in the online community, so it is difficult to identify them. Therefore, in this paper, to remedy these problems we propose a behavior pattern feature vectors for users considering online community characteristics, propose a new implicit behavior pattern called relationship pattern, and identify the nickname of the same user based on Random Forest classifier. Also, Experimental results with the collected real world online community data demonstrate that the proposed behavior pattern and classifier can identify the same users at a meaningful level.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2016.07a
• /
• pp.233-234
• /
• 2016

본 프로젝트는 편안하고 안전하게 세상과 소통하고 관심사를 공유할 수 있는 커뮤니티 사이트이다. 게시글에 수명을 설정하여 자동으로 글이 삭제되는 기능을 통해 사용자의 보안성을 향상시키며, 기본적으로 닉네임을 사용하여 안전한 익명성을 보장한다. 즉, 자동으로 게시글이 삭제되는 커뮤니티 웹 사이트로 사용자들의 일상, 관심사, 정보 공유 등을 게시글을 통해 공유하고 소통할 수 있고, 커뮤니티 활동에 따라 마일리지를 누적하여 게시글의 기본 수명을 연장 할 수도 있다.

• Smart Media Journal
• /
• v.11 no.2
• /
• pp.18-24
• /
• 2022

Problems caused by malicious comments occur on many social media. In particular, YouTube, which has a strong character as a medium, is getting more and more harmful from malicious comments due to its easy accessibility using mobile devices. In this paper, we designed and implemented a YouTube malicious comment detection system to identify malicious comments in YouTube contents through LSTM-based natural language processing and to visually display the percentage of malicious comments, such commentors' nicknames and their frequency, and we evaluated the performance of the system. By using a dataset of about 50,000 comments, malicious comments could be detected with an accuracy of about 92%. Therefore, it is expected that this system can solve the social problems caused by malicious comments that many YouTubers faced by automatically generating malicious comments statistics.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.775-778
• /
• 2021

악성 댓글은 언어폭력이며 사이버 범죄의 일종으로 인터넷상에서 상대방이 올린 글에 비방이나 험담을 하는 악의적인 댓글을 말한다. 악성 댓글을 단순히 차단하는 다른 프로그램들과는 달리 해당 영상의 악성 댓글의 비율을 알려주고 악플러들의 닉네임과 그 빈도를 나타내주는 것으로 차별화를 두었다. 따라서 많은 유튜버들이 겪는 악성 댓글 문제들을 탐지하여 유튜브에 달리는 악성 댓글들을 탐지하고 시각화하여 제공한다.

• Journal of Digital Convergence
• /
• v.12 no.6
• /
• pp.277-287
• /
• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.3
• /
• pp.879-886
• /
• 2018

The basic search engine on Twitter shows not only tweets that contain search keywords, but also all articles written by users with nicknames containing search keywords. Since the tweets unrelated to the search keyword are exposed as search results, it is inconvenient to many users who want to search only tweets that include the keyword. To solve this inconvenience, this study improved the search function of Twitter by developing an algorithm that searches only tweets that contain search keywords. The improved functionality is implemented as a Web service using ASP.NET MVC5 and is available to many users. We used a powerful collection method in C# to retrieve the results of an object, and it was also possible to output them according to the number of 'retweets' or 'favorites'. If the number of retrieved numbers is less than a given number, we also added an exclusion filter function. Thus, sorting search results by the number of retweets or favorites, user can quickly search for opinions that are of interest to many users. It is expected that many users and data analysts will find the developed function convenient to search on Twitter.