• Title/Summary/Keyword: 주제 중심 트윗

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Spatial Distribution Patterns of Twitter Data with Topic Modeling (토픽 모델링을 이용한 트위터 데이터의 공간 분포 패턴 분석)

  • Woo, Hyun Jee;Kim, Young Hoon
    • Journal of the Korean association of regional geographers
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    • v.23 no.2
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    • pp.376-387
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    • 2017
  • This paper attempts to analyze the geographical characters of Twitter data and presents analysis potentials for social network analysis in geography. First, this paper suggests a methodology for a topic modeling-based approach in order to identify the geographical characteristics of tweets, including an analysis flow of Twitter data sets, tweet data collection and conversion, textural pre-processing and structural analysis, topic discovery, and interpretation of tweets' topics. GPS coordinates referencing tweets(geotweets) were extracted among sampled Twitter data sets because it contains the tweet place where it was created. This paper identifies a correlated relationship between some specific topics and local places in Jeju. This correlation is closely associated with some place names and local sites in Jeju Island. We assume it is the intention of tweeters to record their tweet places and to share and retweet with other tweeters in some cases. A surface density map shows the hotspots of tweets, detecting around some specific places and sites such as Jeju airport, sightseeing sites, and local places in Jeju Island. The hotspots show similar patterns of the floating population of Jeju, especially the thirty-year age group. In addition, a topic modeling algorithm is applied for the geographical topic discovery and comparison of the spatial patterns of tweets. Finally, this empirical analysis presents that Twitter data, as social network data, provide geographical significance, with topic modeling approach being useful in analyzing the textural features reflecting the geographical characteristics in large data sets of tweets.

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Design of a Real-time Risk Analysis System for Ransomware Using Mining based on Social Network Service (소셜 네트워크 서비스 기반 마이닝을 이용한 실시간 랜섬웨어 위험도 분석 시스템 설계)

  • Na, Jaeho;Kim, Mihui
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.11a
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    • pp.254-256
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    • 2017
  • 본 논문에서는 소셜 네트워크 서비스 중 트위터를 마이닝하여 실시간으로 랜섬웨어 위험도 분석을 하는 시스템을 설계한다. 이를 위해 2017년 5월 12일에 가장 피해가 컸던 워너크라이 랜섬웨어를 중심으로 5월 10일에서 20일 사이의 트윗 데이터를 마이닝하고, 기존 시스템인 구글 트렌드와의 유사성을 비교 실험하여 트윗 데이터의 가치를 확인한다. 마지막으로 제안하는 시스템에 대한 향후 연구주제를 제시한다.

A Case Study of the Issue detected Analysis on Social Media Big Data (소셜 빅 데이터를 이용한 이슈 감지 사례분석)

  • Song, Eun-Jee;Kang, Min-Shik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2014.10a
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    • pp.682-683
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    • 2014
  • 최근 IT업체들은 온라인 상에서 소비자들이 평소에 쏟아내는 의견들을 수집, 축적해서, 원하는 키워드를 중심으로 내용을 분석함으로써, 특정 주제에 대해 어떤 여론이 형성되고 있으며, 여론이 어떻게 전파되고 있는지 경로를 파악할 수 있는 소셜 빅데이터 분석 툴을 경쟁적으로 개발하고 있다. 본 논문에서는 소셜 빅 데이터를 분석함에 있어 이슈를 감지하고 예측하는 기술을 실제 사례에 적용하여 분석한 결과를 고찰해 보고자 한다. 소셜 미디어 데이터 패턴을 비교 분석하고 부정이슈 감지를 위해 부정 여론을 확산시키는데 영향을 미치는 내용과 작성자를 독립변수로 하고, 평균 이슈 도달 시간 및 속도를 종속변수로 정의한다. 부정 여론 형성의 영향력은 트윗수, 리트윗 수를 기준으로 이슈 감지한다. 분석결과 전체 트윗 중 리트윗 메시지가 큰 비중 차지하고 이슈에 대한 버즈가 증가할수록 리트윗 비중이 증가하였으며 크게 확산될 때는 리트윗량이 크게 증가하여 짧은 시간 안에 넓게 확산하였다.

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An Analysis of IT Trends Using Tweet Data (트윗 데이터를 활용한 IT 트렌드 분석)

  • Yi, Jin Baek;Lee, Choong Kwon;Cha, Kyung Jin
    • Journal of Intelligence and Information Systems
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    • v.21 no.1
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    • pp.143-159
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    • 2015
  • Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

A phenomenon Study on Acceptance Universe of K-pop Audience : Focused on Group Aespa's Universe Case (K-pop 수용자의 세계관 수용 현상 연구 : 그룹 에스파의 세계관 사례를 중심으로)

  • Kim, Nakyung
    • Trans-
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    • v.12
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    • pp.173-222
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    • 2022
  • This thesis examines the 'universe' acceptance phenomenon, currently used as a content strategy in the K-pop field, from the perspective of K-pop audiences, and then attempts to identify their experience of acceptance and the meaning of the universe. For this, tweets related to the universe acceptance experience of Aespa, the group utilizing the universe as a content strategy the most actively, were collected, and this data was analyzed according to a phenomenological approach, an approach to explore the structure of personal experience and the essence of a phenomenon. As a result of analyzing using Moustakas' method, the semantic structure of the universe acceptance phenomenon of K-pop audiences was derived based on 21 thematic units. It was found that current K-pop audiences are experiencing active cultural consumption rather than unilateral or passive through acceptance of the universe. This means that K-pop audiences have the characteristics of active audiences that produce meaning, interact with other fans, and exert influence on outside of community. At the same time, these characteristics affect acceptance of the universe. Simultaneously, through active acceptance experience, it is found that K-pop audiences give a new meaning in the K-pop universe, as "marketing assets", "fandom community assets", and "K-pop industry expansion assets." Among them, the recognition of 'marketing assets' was reaffirmed as a basis for supporting related previous studies. In addition, it derived the new values of the universe in the K-pop field by discovering the meaning of "fandom's specific assets" and "assets of the K-pop industry for expansion". These meanings had not been found that previous studies from the producers' point of view. And then, for the purpose of expanding the value of the universe in the future, it was discussed the direction of the new meaning of the universe. Finally, this study is meaningful in that it revealed the semantic structure of the universe acceptance phenomenon and discovered a new meaning of the universe in the K-pop field. Additionally, it was intended to contribute to expanding the field of research by suggesting various follow-up studies from various perspectives.