• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.252-255
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• 2011

Growing demand for smart phones and data communication diminishes the constraints of Twitter and Facebook than a smartphone has become a subject of interest. On the other hand facebook users in their relationships to obtain the consent of the other, twitter is a relatively simple procedure for the information ripple effect is excellent. Twitter is beyond a simple social networking services(SNS) located in one of the popular media and powerful have the upper retweet. Retweet to the top of his sympathy with the ability th send tweets to their subscriber information can spread quickly. In this paper, we propose the smart retweet that system actively extend the existing retweet. In order to realize the smart retweet and additional criteria for determining the destination of the information is required. Based on tweet generated regional or an local information mentioned to tweet, to determine the destination. Smart retweet of the speed and scope of information transmission through the scale is expected.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.960-966
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• 2012

It is prevalent to gather the location information from GPS, WiFi and etc, and therefore LBSNS (Location-Based SNS) has increased rapidly (such as location-augmented Twitter services). The message created from LBSNS include the specific area of interests which the message is created in or mentions. It is easy to propagate the location-based information of LBSNS by adapting the retweet function which is efficient way to propagate the message in tweeter. In this paper, we have defined the smart retweet as a automatic retweet function for efficient propagating the messages which is geo-tagging the location of interests. We have designed the smart retweet system based on the tweeter system. The user could specify the area of interests and build the social networking among the users which have interested in common area. The smart retweet system have been implemented by mesh-up services based on Open-API of trweeter and google map. It is expected that the smart retweet service proposed in this paper makes easy sharing of the location-based interesting information.

• ETRI Journal
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• v.36 no.3
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• pp.418-428
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• 2014

In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3499-3511
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• 2014

This study examined what determinants affect the intention of retweeting on Twitter from the perspectives of motivations and social psychology. The primary theoretical foundations are the theory of reasoned action (TRA), motivation theory and social capital theory. An online survey was administrated to collect the data. The data collected was analyzed using the structural equation model (SEM). The findings showed that both the attitude toward the retweeting behavior and subjective norm have significant effects on the intention to retweet. The results also showed that the attitude toward the retweeting behaviors was influenced by the individual intrinsic motivation and the norm of reciprocity. Social trust also had a significant influence on the intention to retweet. This study discusses the implications of these findings.

• 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.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.131-140
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• 2013

Assuming that roles of traditional mass media are also shown in Twitter services, the study aims at exploring Twitter users' motives and rationales in re-tweet behavior. Based on the laddering interview method, the study gathers data on (1) message attributes (what kinds of messages do you re-tweet?); (2) consequences (what kinds of consequences are you expecting when you re-tweet?); and (3) values (what are the ultimate values in your re-tweet behavior?). The most repetitive value occurring in participants' retweet was feeling "sympathy" and "sharing" rationales. For such rationales, participants oftentimes utilize messages with "agenda" and "information" that are relative to themselves. Messages with "helping" to help others also frequently showed up in their retweet rationales. Known as liberalists' rationales, "communal consciousness", and "calling for others' action" are also shown, but not as frequent as "feeling sympathy and sharing. A total of 48 items from the analyses were used in a subsequent study as variables to identify factors (dimensions) of retweet motivation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.98-101
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• 2011

Recently with the advent of Web 2.0 mashups are Web services, a new concept is in the spotlight. Mashups with Web services, such as a new concept for the purposes of an individual's social networking community type social network service web service coming seeping deep into our lives was a huge pillars. The structure of the network of one-way, tweeter and horizontal moves are also suitable for biased information. People from twitter's vast social network that matches their area of interest and share information with people who are wanted. In this paper, we develop the smart retweet mashup service that matching areas of interest for the purpose of sharing information between people. To selectively share information and consideration, additional information is required. Information derived from user-specified area of interest by region and share information is selectively targeted.

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

• The Journal of Society for e-Business Studies
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• v.17 no.2
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• pp.73-85
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• 2012

Recently, as Social Network Services(SNS), such as Twitter, Facebook, are becoming more popular, much research has been doing actively. However, since SNS has been launched recently, related researches are also infant level. Especially, search engines serviced in web potals simply show the postings in order of upload time. Searching the postings in Twitter should be different from web search, which is based on traditional TF-IDF. In this paper, we present the new method of searching and ranking the interesting postings in Twitter. In proposed method, we utilize the frequency of retweets as a major factor for estimating the quality of postings. It can be an important criteria since users tend to retweet the valuable postings. Experimental results show that proposed method can be applied successfully in Twitter search system.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.137-147
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• 2014

The study aims at exploring motivation, rationale, and values in twitter users' retweet behavior. It proposes that diffusion of message is based on the complex interactional relationships among attributes of original message, user's rationale, and values. Based on a pilot study, we constructed a total of 34 questions asking message attributes, motivation, and values of retweeting. Then, twitter users participated in an online survey, in which they evaluate their own 5 retweet messages based on the constructed questions(5 messages ${\times}$ 34 questions = 170). Then, a factor analysis is done in order to see the dimensions of the concepts in retweet behavior; and understand how message attributes, motivations, and values are inter-related with each other. The main factors extracted were: (1) public fairness, (2) fun and playfulness, (3) communal help, (4) news and information, etc. Factor 2 and 4 show the traditional journalism characteristics; while factor 1 and 3 do alternative journalistic values. The latter may work as a rectifying factors for traditional journalism; however, backfiring mechanism for group polarization. In addition, (1) users' internal identities, (2) communal unity and (3) belongness were identified as rationales and values for retweet behavior.