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

• Journal of Korea Society of Industrial Information Systems
• /
• v.26 no.5
• /
• pp.79-94
• /
• 2021

Despite the importance of the supply chain risk due to the negative word-of-mouth (NWOM) in social media, related research is insufficient. Thus, this study analyzes how the NWOM of the product is distributed through social media and the characteristics of the distributor based on social exchange theory. For this purpose, we collected information on car recalls from four companies using Twitter from the National Highway Traffic Safety Administration (NHTSA). Based on the Seed Tweet, a Re-Tweet (RT) network was constructed to examine the distribution and spread of NWOM, and regression analysis was performed to test the hypothesis. As a result, it was confirmed that NWOM is a small world network structure that spreads around hub users connected to many users. Moreover, it was found that the more interactive and reciprocal relations the first distributor has, the greater the speed and scale of distribution of NWOM.

• Journal of KIISE
• /
• v.41 no.12
• /
• pp.1136-1144
• /
• 2014

Twitter is a social network service that generates about 140 million contents a day. Contents of Twitter contain a variety of information and many researchers research those in various fields. In this research, we propose a method for evaluating the importance of content based on characteristics of Twitter. We have found that number of follower means user's popularity and Re-tweet that means the popularity of content. We perform experiments about proposed method using real Twitter data for proving effectiveness of proposed method. Also, we found information providers in Twitter are public user who represent a company or a representative of a specific group.

• Journal of Korea Society of Industrial Information Systems
• /
• v.19 no.6
• /
• pp.7-13
• /
• 2014

Twitter is a microblogging service that allows users to post short messages on a variety of topics in real-time. In this work, we analyze Twitter messages posted during the 2012 elections and find those implications. This study uses Twitter messages related to the 2012 South Korean presidential campaign. The three main candidates are represented by the abbreviations A, M, and P. According to the statistical analysis, the number of tweets and re-tweets for candidate P was relatively stable over the entire campaign period. Candidate P had the highest percentage of terms related to elections pledges, and candidates A and M were judged to be a little bit poorer with respect to campaign promises. The positive terms ratio for candidate P was higher than those for the other two candidates. The negative terms ratio in the Twitter messages of P was considerably smaller than those of candidates A and M. After considering all these results, it is suggested cautiously that Twitter messages posted during an election campaign could be correlated with the outcome of the election.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.107-130
• /
• 2012

In Twitter, a user can post a message below 140 characters on his/her account, and can also repost a message of other users who the user follows. The message posted by the user in turn can be seen and reposted by other users who follow the user, which is called Re-tweet (RT). While some messages spread widely, other messages have relatively less or no RT. What factors cause these quantity variances of RT originated from original messages? How can the messages become influential in online social networks? As an effort to answer the above questions, we focused on information vividness, message characteristics, and originator characteristics. In perspective of managerial implication, we expect that the findings of this paper will provide corporations with helpful insight on the Word-of-Mouth (WOM) effect for efficient and effective advertisements and communications when they send a message of new products or services through Social Network Services. In perspective of academic implication, we identify the effect of contents of a message on WOM, which has been dealt with by few social network researches.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.987-999
• /
• 2017

Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.