• Journal of Multimedia Information System
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• v.2 no.3
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• pp.287-294
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• 2015

We are living in epoch of information when Internet touches all aspects of our lives. Therefore, it provides a plenty of services each of which benefits people in different ways. Electronic Mail (E-mail), File Transfer Protocol (FTP), Voice/Video Communication, Search Engines are bright examples of Internet services. Between them Social Network Services (SNS) continuously gain its popularity over the past years. Most popular SNSs like Facebook, Weibo and Twitter generate millions of data every minute. Twitter is one of SNS which allows its users post short instant messages. They, 100 million, posted 340 million tweets per day (2012)[1]. Often big amount of data contains lots of noisy data which can be defined as uninteresting and unclassifiable data. However, researchers can take advantage of such huge information in order to analyze and extract meaningful and interesting features. The way to collect SNS data as well as tweets is handled by crawlers. Twitter crawler has recently emerged as a great tool to crawl Twitter data as well as tweets. In this project, we develop Twitter Crawler system which enables us to extract Twitter data. We implemented our system in Java language along with MySQL. We use Twitter4J which is a java library for communicating with Twitter API. The application, first, connects to Twitter API, then retrieves tweets, and stores them into database. We also develop crawling strategies to efficiently extract tweets in terms of time and amount.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.217-223
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• 2024

The current study aimed to evaluate the effectiveness of using Support Vector Machine (SVM) for political affiliation classification. The system was designed to analyze the political tweets collected from Twitter and classify them as positive, negative, and neutral. The performance analysis of the SVM classifier was based on the calculation of metrics such as accuracy, precision, recall, and f1-score. The results showed that the classifier had high accuracy and f1-score, indicating its effectiveness in classifying the political tweets. The implementation of SVM in this study is based on the principle of Structural Risk Minimization (SRM), which endeavors to identify the maximum margin hyperplane between two classes of data. The results indicate that SVM can be a reliable classification approach for the analysis of political affiliations, possessing the capability to accurately categorize both linear and non-linear information using linear, polynomial or radial basis kernels. This paper provides a comprehensive overview of using SVM for political affiliation analysis and highlights the importance of using accurate classification methods in the field of political analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.597-604
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• 2011

The various Smartphone platforms that are used currently make difficult to build efficient applications("apps") for Smartphone. Introduction of middleware in the Smartphone environment is being studied to solve this problem. By enhancing interoperability between server systems and Smartphone platforms as introducing this middleware supports efficiently for Smartphone apps to be developed and managed. Thus, the development of this middleware for Smartphone has become essential for the purpose of responding actively to the rapidly expanding Smartphone market. In this research, we designed and implemented Smartphone middleware which optimizes the cost and time for developing new application service and maintaining it. In order to test this implemented middleware's performance and its capabilities, we also developed university Smartphone apps and activated campus twitter.

• Genomics & Informatics
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• v.19 no.3
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• pp.21.1-21.5
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• 2021

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present. However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don't generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.90-93
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• 2013

최근 IT Service 에는 SNS 즉, Social Network Service가 주를 이루고 있다. Facebook, Twitter 등 자신의 일상을 친구들과 공유하는 웹 기반 Service가 사람들에게 많이 이용되고 인기를 끌고 있다. SNS system이 일반화되어 가면서, 보다 전문적이고 국소적인 분야에 특성화되어 발전되어 나갈 것으로 보이며, 이러한 SNS 시스템을 최적화 하기 위해 SNS 프로토타입을 직접 개발해 볼 필요가 있다. 본 논문에서는 학교 캠퍼스 정도 규모의 서비스를 위한 웹 기반의 SNS들이 어떠한 구조로 이루어져 있으며, 어떠한 원리로 동작하는지 알아보고, Server, DataBase, PHP 를 이용하여 웹 기반 SNS 시스템을 직접 구현하고 시연해 봄으로써 SNS System에 대한 이해를 해보고, SNS system에 대한 향후 방향을 모색해 보도록 한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.76-81
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• 2018

The Internet of Things (IoT), which is emerging as a future economic growth engine, has been actively introduced in areas close to our daily lives. However, there are still IoT security threats that need to be resolved. In particular, with the spread of smart homes and smart cities, an explosive amount of closed-circuit televisions (CCTVs) have been installed. The Internet protocol (IP) information and even port numbers assigned to CCTVs are open to the public via search engines of web portals or on social media platforms, such as Facebook and Twitter; even with simple tools these pieces of information can be easily hacked. For this reason, a big-data analytics system is needed, capable of supporting quick responses against data, that can potentially contain risk factors to security or illegal websites that may cause social problems, by assisting in analyzing data collected by search engines and social media platforms, frequently utilized by Internet users, as well as data on illegal websites.