• KIPS Transactions on Software and Data Engineering
• /
• v.1 no.1
• /
• pp.69-74
• /
• 2012

Many internet users attempt to focus on the issues which have posted on social network services in a very short time. When some social big issue or event occurred, it will affect the number of comments and retweet on that day in twitter. In this paper, we propose the method of extracting core events based on timeline analysis, sentiment feature and retweet information in twitter data. To validate our method, we have compared the methods using only the frequency of words, word frequency with sentiment analysis, using only chi-square method and using sentiment analysis with chi-square method. For justification of the proposed approach, we have evaluated accuracy of correct answers in top 10 results. The proposed method achieved 94.9% performance. The experimental results show that the proposed method is effective for extracting core events in twitter corpus.

• Journal of the Korea Convergence Society
• /
• v.9 no.8
• /
• pp.25-32
• /
• 2018

Recently, the size and type of disasters in Korea has been diversified. However, Korea has not been able to build various information support systems to predict these disasters.Many other organizations also provide relevant information. This information is mainly provided on the Web, but most of it is not real time information. In this study, we have paid attention to support information using big data to provide better quality real - time information together with information provided by institutions. Big data has a large amount of information with real-time property, and it can make customized service using it. Among them, SNS such as Twitter and Facebook can be used as a new information collection medium in case of disaster. However, it is very difficult to retrieve necessary information from too much information, and it is difficult to collect intuitive information. For this purpose, this study develops an information support system using Twitter. The system retrieves information using the Twitter hashtag. Also, information mapping is performed on the map so that intuitive information can be grasped. For system evaluation, information extraction, degree of mapping, and recommendation speed are evaluated.

• Asian Journal for Public Opinion Research
• /
• v.8 no.3
• /
• pp.393-406
• /
• 2020

The discourse on the lockdown in Indonesia is getting stronger due to the increasing number of positive cases of the coronavirus and the death rate. As of August 12, 2020, the confirmed number of COVID-19 cases in Indonesia reached 130,718. There were 85,798 victims who have recovered and 5,903 who have died. Data show a significant increase in cases of COVID-19 every day. For this reason, there needs to be an evaluation of the government policy of the Republic of Indonesia in dealing with the COVID-19 pandemic in Indonesia. An evaluation of policies for handling the pandemic must include public opinion to determine any weaknesses of this policy. The development of public opinion about the lockdown policy can be understood through social media. During the COVID-19 pandemic, measuring public opinion through traditional methods (surveys) was difficult. For this reason, we utilized big data on social media as research data. The main purpose of this study is to understand public opinion on the lockdown policy in overcoming the COVID-19 pandemic in Indonesia. The things observed included: volume of Twitter users, top influencers, top tweets, and communication networks between Twitter users. For the methodological development of future public opinion research, the researchers outline the obstacles faced in researching public opinion based on big data from Twitter. The research results show that the lockdown policy is an interesting issue, as evidenced by the number of active users (79,502) forming 133,209 networks. Posts about the lockdown on Twitter continued to increase after the implementation of the lockdown policy on April 10, 2020. The lockdown policy has caused various reactions, seen from the word analysis showing 14.8% positive sentiment, 17.5% negative, and 67.67% non-categorized words. Sources of information who have played the roles of top influencers regarding the lockdown policy include: Jokowi (the president of the Republic of Indonesia), online media, television media, government departments, and governors. Based on the analysis of the network structure, it shows that Jokowi has a central role in controlling the lockdown policy. Several challenges were found in this study: 1) choosing keywords for downloading data, 2) categorizing words containing public opinion sentiment, and 3) determining the sample size.

• Journal of Service Research and Studies
• /
• v.5 no.1
• /
• pp.79-89
• /
• 2015

Recently, with the introduction of social network services, studies that try to make use of them for the various purposes have been actively investigated. In order to proceed with the research that takes advantage of social network services, it is necessary to review the relevant literature and to identify trends in researches. However, the researches of social network are massive amount, so to review the huge amount of relevant research literature is a very difficult task. Therefore, in this study, we analyze systematically the tendency of research related to social network service focusing on Twitter. Especially, we use the SLR (Systematic Literature Review) technique for systematic literature survey and analysis. For the literature survey, we select korean literature resource sites and 243 studies of literature that are surveyed. Studies and analyzes on Twitter in a variety of research studies were also using Twitter data that way beyond the simple question directly.

• 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.

• Journal of Internet Computing and Services
• /
• v.16 no.3
• /
• pp.105-116
• /
• 2015

The Sewol ferry catastrophe that took place on April 16 2014 was unprecedented in terms of its sociopolitical implications, which had reverberated throughout the Korean nation. Mindful of such distinct characteristics of the Sewol ferry catastrophe, this thesis looks into the salience of the agendas portrayed in Twitter and Portal News coverage on the disaster and the correlation between the attribute-specific agendas of the foregoing mediums by making use of the agenda rank order correlation method. Extraction and analysis of big data revealed that first, while the hypothesis that there were little difference in terms of salience among the main agendas between Twitter and Portal News was dismissed, the rank order correlation proved to be high as regards the main agendas on Twitter and Portal News. This signifies that Twitter agendas exert influence over those on Portal News. Next, and regarding the five main agendas on the incident, there existed differences in salience between the attribute-specific agendas of the two mediums, with low figures for corresponding rank order correlations. Such results signify that Twitter and Portal News have little influence over each other as regards their agenda rank order correlation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.1051-1053
• /
• 2015

Twitter is using Tweets to post 140 characters at a time to interact with different people around the world. In addition, Twitter will also provide speed, such as instant messaging by providing the follow feature. This was used for increasing the number of users because of the tweeter, a portion of the life was due to the popularity of smart phones. However, because of the large amount of data of the tweeter has a disadvantage similar to the user information or user information is not recommended. In this paper, in order to compensate for this problem to establish a ranking filter the similarity information based on a user's system, we propose that the user or the like similar to the user information. The system proposed in this paper consists of the collected data and modules to collect data using a user account in the filtering and the like to the tweeter module. These modules use the Open API and Mahout designed and implemented.

• Spatial Information Research
• /
• v.22 no.3
• /
• pp.1-8
• /
• 2014

While SNS services such as Twitter or Facebook are rapidly growing, research for the SNS analysis has been concerned. Especially, twitter reacts to social issues in real-time so that it is used to get useful experimental data for researchers of social science or information retrieval. However, it is still lack of research on the methodology to collect data. Therefore, this paper suggests the tweet acquisition system by considering tendency of twitter user oriented location-based event and political social event. First the system acquires tweets including information of location and keyword about event and secure IDs for acquisition of political social event. Then we plan ID-analyzer to classify the tendency of users. In addition for measuring reliability of ID-analyzer, it acquires and analyzes the tweet by using high-ranked ID. In analyses result, top-ranked ID shows 88.8% reliability, 2nd-ranked ID shows 76.05% and ID-analyzer shows 77.5%, it shortens collection time by using minority ID.

• Journal of Creative Information Culture
• /
• v.5 no.3
• /
• pp.215-223
• /
• 2019

The wave of the information age gradually accelerates, and fusion analysis solutions that can utilize these knowledge data according to accumulation of various forms of big data such as large capacity texts, sounds, movies and the like are increasing, Reduction in the cost of storing data accordingly, development of social network service (SNS), etc. resulted in quantitative qualitative expansion of data. Such a situation makes possible utilization of data which was not trying to be existing, and the potential value and influence of the data are increasing. Research is being actively made to present future-oriented education systems by applying these fusion analysis systems to the improvement of the educational system. In this research, we conducted a big data analysis on Twitter, analyzed the natural language of the data and frequency analysis of the word, quantitative measure of how domestic windows education problems and outcomes were done in it as a solution.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.2
• /
• pp.505-513
• /
• 2017

In this study, we collected 200,578,703 geo-tweets and removed the twitter bots. Using the concept of activity radius, Twitter users are classified. Users are also divided first into domestic and overseas, and again domestic ones are divided into locals and non-locals. Statistical characteristics of activity strength and active area of Twitter users are described according to activity radius and home region, and the geographical distribution is presented visually. Through a case study of Las Vegas, we have identified the difference in activity strength and active area by the user's home residence. We expect to derive theories about human mobility by analyzing various cities with the method proposed in this study.