• Information Systems Review
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• v.14 no.1
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• pp.21-35
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• 2012

The rapid growth and popularity of Twitter have been one of the most influential phenomena in the era of social network system and the mobile internet, which also opens up opportunities for new business strategies; in particular, PR and marketing area. This study analyzed use of Twitter in terms of user characteristics and message attributes. Actual field data from the Twitter for PR and Marketing of a representative Korean IT company (Company "K") was used for this analysis. Research findings show that overall corporate twitter users show passive attitude in retweet behavior. Also, users who have relatively small network size (less than 1,000) are more active in retweet than power twitterians that have big network size(over than 10,000). It is showed that the rate of retweet is higher in the order of recruiting, promotional event, IT information, and general PR message. In the conclusion section, practical implications based on the research finding are thoroughly discussed.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.80-85
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• 2021

This study is to analyze the structure of the networks of tourism marketing on Twitter, identifying the most prominent users, the flow of information about tourism marketing, and the interaction between the users posting tweets. This study employs NodeXL pro as a visualization software package for social network analysis. The number of vertices or nodes is 171, and the number of the unique edges or links is 128, but there are 101 edges with duplicates, so the total links are 229, which means that there are fewer Twitter accounts in the social network on tourism marketing, but they have a few close relationships by sharing information. The research can map the social network of communicators of tourism marketing using Twitter data. The network has a complicated pattern, including one independent network and some connected networks. Some mediators connect each network and can control the information flow of tourism marketing. More communicators are getting the information than the ones providing it, which means that there is likely to be the dependence of information among communicators that can cause an obstacle and distortion of the information flow system, especially in the independent network.

• Korean journal of communication and information
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• v.60
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• pp.150-171
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• 2012

According to the result of the online survey of SNS users, SNS users' use motivations consist of 'information', 'participation', and 'interaction'. SNS use motivations explain characteristics of an individual SNS very well. SNS users that aim to collect information use much more the SNS for communication like 'Twitter' and 'Me2day' than other SNS. SNS users that aim to participate in communication through SNS use much more 'Cyworld' that is joined by the most subscriber. And SNS users that aim to interact with other users use much more the SNS for network like 'Facebook' and 'Cyworld'. This tendency can also be seen in the use hours and access times of SNS by SNS use motivations. Meanwhile, the SNS Users that aim to collect information and interact with other users positively rate SNS messages. On the other hand, the SNS Users that aim to participate in communication through SNS negatively rate those. This confirms that SNS use motivations affect SNS users' recognition of SNS messages.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.63-72
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• 2014

Twitter has been a popular social media platform where people post short messages of 140 characters or less via the web. A hashtag is a word or acronym created by Twitter users to open a discussion about certain topics and issues that have a very high percentage of trending. Since the hashtag posts are sorted by time, not relevancy, people who firstly use Twitter have had difficulty understanding their context. In this paper, we propose a HBase-based automatic summary system in order to reduce the difficulty of understanding. The proposed system combines an automatic summary method with a fuzzy system after storing the streaming data provided by Twitter API to the HBase. Throughout this procedure, we have eliminated the duplicate of contents in the hashtag posts and have computed scores between posts so that the users can access to the trending topics with relevancy.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.87-95
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• 2011

Recently, as smartphones have rapidly come into wide use and various social networking services have grown, people perform a variety of interactions through the virtual and real world based on them. Usually, such services focus on easy formation of social links among users, supporting the exchange of simple messages among users on the networks. Twitter, one of such services used worldwide, supports short message service named Tweet and has over 200 million members signed up. n this paper, we propose techniques for supporting real-time group messaging based on the social network of Twitter and describe a smartphone group messaging system developed with the techniques. The system automatically forms a group that include a Twitter user and the followers of the user, supporting real-time group messaging among the group members. The developed system is composed of an XMPP protocol-based messaging server and smartphone client applications which perform real-time messaging based on the social network of Twitter. Twitter users can easily use the system utilizing Tweet messages, exchanging real-time messages with their followers within the groups instantly established on the XMPP server.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.81-91
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• 2019

Recently, social network service (SNS) is actively used by public. Among them, Twitter has a lot of tweets including sentiment and it is convenient to collect data through open Aplication Programming Interface (API). In this paper, we analyze social issues and suggest the possibility of using them in marketing through sentimental information of users. In this paper, we collect twitter text about social issues and classify as positive or negative by sentiment classifier to provide qualitative analysis. We provide a quantitative analysis by analyzing the correlation between the number of like and retweet of each tweet. As a result of the qualitative analysis, we suggest solutions to attract the interest of the public or consumers. As a result of the quantitative analysis, we conclude that the positive tweet should be brief to attract the users' attention on the Twitter. As future work, we will continue to analyze various social issues.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.79-100
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• 2018

Twitter is one of the most popular social platforms for online users to share trendy information and views on any event. Twitter reports an event faster than any other medium and contains enormous information and views regarding an event. Consequently, Twitter topic summarization is one of the most convenient ways to get instant gist of any event. However, the information shared on Twitter is often full of nonstandard abbreviations, acronyms, out of vocabulary (OOV) words and with grammatical mistakes which create challenges to find reliable and useful information related to any event. Undoubtedly, Twitter event summarization is a challenging task where traditional text summarization methods do not work well. In last decade, various research works introduced different approaches for automatic Twitter topic summarization. The main aim of this survey work is to make a broad overview of promising summarization approaches on a Twitter topic. We also focus on automatic evaluation of summarization techniques by surveying recent evaluation methodologies. At the end of the survey, we emphasize on both current and future research challenges in this domain through a level of depth analysis of the most recent summarization approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.185-190
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• 2014

With the fast increment of smart phone users and extension of wireless internet the number of SNS user is also increasing. Twitter among lots of SNS takes the lead in SNS market. Twiter users express their thinking and feelings. In this paper, by analyzing twitts near the distribution enterprise using opinion mining. And by analyzing concern degree using the number of twitts and positive, neutral, negative degree we deliver marketing message to marketer. As the result, we propose that marketing and management of this distribution enterprise can reflect the demand of customer who is near there.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.9-15
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• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.