• Title/Summary/Keyword: Twitter

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Following Firms on Twitter: Determinants of Continuance and Word-of-Mouth Intentions (트위터를 통한 기업과 고객과의 소통: 지속적인 팔로윙과 구전 의도에 영향을 미치는 요인에 대한 연구)

  • Kim, Hongki;Son, Jai-Yeol;Suh, Kil-Soo
    • Asia pacific journal of information systems
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    • v.22 no.3
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    • pp.1-27
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    • 2012
  • Many companies have recently become interested in using social networking sites such as Twitter and Facebook as a new channel to communicate with their customers. For example, companies often offer "special deals" (e.g., coupons, discounts, free samples, etc.) to their customers who participate in promotions or events on social networking sites. Companies often make important announcements on their products or services on social networking sites. By doing so, customers are encouraged to continue to have relationships with companies on social networking sites and to recommend the companies' presence on social networking sites to other potential customers. Moreover, customers who keep close relationships with companies on social networking sites often provide the companies with valuable suggestions and feedback. For instance, Starbucks has more than 2 million followers on Twitter, and often receive suggestions and feedback for their product offerings and services from the followers on Twitter. Although companies realize potential benefits of using social networking sites as a channel to communicate with their customers, it appears that many companies have difficulty forging long-lasting relationships with customers on social networking sites. It is often reported that many customers who had followed companies on Twitter later stopped following them for various reasons. Therefore, it is an important issue to understand what motivates customers to continue to keep relationships with companies on social networking sites. Nonetheless, due attention has yet paid to this issue until recently. This study intends to contribute to our understanding on customers' intention to continue to follow companies on Twitter and to spread positive word-of-mouth about companies on Twitter. Specifically, we identify seven potential factors that customers perceive as important in evaluating their experience with companies on Twitter. The seven factors include similarity, receptivity, interactivity, ubiquitous connectivity, enjoyment, usefulness and transparency. We posit that the seven perception factors can affect the two types of satisfaction, emotional and cognitive, which can in turn influence on customers' intention to follow companies on Twitter and to spread positive word-of-mouth about companies on Twitter. Research hypotheses formulated in this study were tested with data collected from a questionnaire survey administered to customers who had been following companies on Twitter. The data was analyzed with the partial least square (PLS) approach to structural equation modeling. The results of data analysis based on 177 usable responses were generally supportive of our predictions for the effects of the seven factors identified and the two types of satisfaction. In particular, out results suggest that emotional satisfaction was strongly influenced by perceived similarity, perceived receptivity, perceived enjoyment, and perceived transparency. Cognitive satisfaction was significantly influenced by perceived similarity, perceived interactivity, perceived enjoyment, and perceived transparency. While cognitive satisfaction was found to have significant and positive effects on both continued following and word-of-mouth intentions, emotional satisfaction had a significant and positive effect only on word-of-mouth intention.

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Twitter and Retweet Context: User Characteristics and Message Attributes of Twitter for PR and Marketing (기업의 홍보 마케팅용 트위터의 리트윗 현황 분석: 이용자 특성과 콘텐츠 속성을 중심으로)

  • Cho, Tae-Jong;Yun, Hae-Jung;Lee, Choong-C.
    • 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.

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Follower classification system based on the similarity of Twitter node information (트위터 사용자정보의 유사성을 기반으로 한 팔로어 분류시스템)

  • Kye, Yong-Sun;Yoon, Youngmi
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.1
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    • pp.111-118
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    • 2014
  • Current friend recommendation system on Twitter primarily recommends the most influential twitter. However, this way of recommendation has drawbacks where it does not recommend the users of which attributes of interests are similar to theirs. Since users want other users of which attributes are similar, this study implements follower recommendation system based on the similarity of twitter node informations. The data in this study is from SNAP(Stanford Network Analysis Platform), and it consists of twitter node information of which number of followers is over 10,000 and twitter link information. We used the SNAP data as a training data, and generated a classifier which recommends and predicts the relation between followers. We evaluated the classifier by 10-Fold Cross validation. Once two twitter node informations are given, our model can recommend the relationship of the two twitters as one of following such as: FoFo(Follower Follower), FoFr(Follower Friend), NC(Not Connected).

Twitter's impact on the election of TV debates -18th presidential election TV debates- (TV토론회에서 트위터가 선거에 미치는 영향 -제18대 대통령 선거 TV토론회를 중심으로-)

  • Han, Chang-Jin;Kim, Kyoung-Soo
    • Journal of Digital Contents Society
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    • v.14 no.2
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    • pp.207-214
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    • 2013
  • It was the 18th presidential election TV debate Twitter participation of SNS. Began to diverge as the era of social media, combined with SNS through in the mass media, media web 2.0. Search tweets, retweets, while the formation of policy issues, the agenda of Twitter users to listen to the statements of the candidates using the Internet or a smartphone. The highest number of tweets immediately issue statements were made. Content during the progressive tweets core keywords you do not often discussed, followed by the negative information increases the number of tweets has become a policy issue. Top retweets was to evaluate the process of debate, regardless of the issue. Tweeter complements the TV so Twitter has made public opinion. Smart phones and SNS Twitter, combined with the TV and the participation and direct democracy, voters vote one instrument was realized. Should forward approval ratings, real-time Twitter subtitles on the TV screen in TV debate Twitter influence in the election will be greatly expanded.

Disaster Events Detection using Twitter Data

  • Yun, Hong-Won
    • Journal of information and communication convergence engineering
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    • v.9 no.1
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    • pp.69-73
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    • 2011
  • Twitter is a microblogging service that allows its user to share short messages called tweets with each other. All the tweets are visible on a public timeline. These tweets have the valuable geospatial component and particularly time critical events. In this paper, our interest is in the rapid detection of disaster events such as tsunami, tornadoes, forest fires, and earthquakes. We describe the detection system of disaster events and show the way to detect a target event from Twitter data. This research examines the three disasters during the same time period and compares Twitter activity and Internet news on Google. A significant result from this research is that emergency detection could begin using microblogging service.

Spammer Detection using Features based on User Relationships in Twitter (관계 기반 특징을 이용한 트위터 스패머 탐지)

  • Lee, Chansik;Kim, Juntae
    • Journal of KIISE
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    • v.41 no.10
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    • pp.785-791
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    • 2014
  • Twitter is one of the most famous SNS(Social Network Service) in the world. Twitter spammer accounts that are created easily by E-mail authentication deliver harmful content to twitter users. This paper presents a spammer detection method that utilizes features based on the relationship between users in twitter. Relationship-based features include friends relationship that represents user preferences and type relationship that represents similarity between users. We compared the performance of the proposed method and conventional spammer detection method on a dataset with 3% to 30% spammer ratio, and the experimental results show that proposed method outperformed conventional method in Naive Bayesian Classification and Decision Tree Learning.

Effects of Self-Presentation and Privacy Concern on an Individual's Self-Disclosure : An Empirical Study on Twitter (자기표현욕구와 개인정보노출우려가 자기노출의도에 미치는 영향 : 트위터를 중심으로)

  • Lee, Sae-Bom;Fan, Liu;Lee, Sang-Chul;Suh, Yung-Ho
    • Korean Management Science Review
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    • v.29 no.2
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    • pp.1-20
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    • 2012
  • While feeling anxious about the risk of exposure of personal information and privacy, users of microblogs and social network services are continuously using them. This study aims to develop a model to investigate this phenomenon. Specifically, this study explores the relationship between personal characteristics (represented by privacy concern and self-presentation) and an individual's self-disclosure. An individual's personal belief (represented by perceived risk and perceived trust) is also tested as an mediator between the relationship. Through a questionnaire survey to 183 twitter users in Korea, the results indicate that self-presentation has a direct influence on self-disclosure as well as an indirect influence through perceived trust. In contrast, privacy concern has not a direct but an indirect negative influence on self-disclosure through perceived risk. In conclusion, self-presentation has a stronger influence on self-disclosure then privacy concern to Twitter users. An individual who has a higher propensity for self-presentation will form a stronger perceived trust on Twitter, which in turn, affects the individual's self-disclosure. On the other hand, an individual who is more concerned with personal privacy will feel more serious about perceived risk, which in turn, negatively influences one's perception of the trust in Twitter as well as his desire for self-disclosure.

A Study on Efficient Market Hypothesis to Predict Exchange Rate Trends Using Sentiment Analysis of Twitter Data

  • Komariah, Kokoy Siti;Machbub, Carmadi;Prihatmanto, Ary S.;Sin, Bong-Kee
    • Journal of Korea Multimedia Society
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    • v.19 no.7
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    • pp.1107-1115
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    • 2016
  • Efficient Market Hypothesis (EMH), states that at any point in time in a liquid market security prices fully reflect all available information. This paper presents a study of proving the hypothesis through daily Twitter sentiments using the hybrid approach of the lexicon-based approach and the naïve Bayes classifier. In this research we analyze the currency exchange rate movement of Indonesia Rupiah vs US dollar as a way of testing the Efficient Market Hypothesis. In order to find a correlation between the prediction sentiments from Twitter data and the actual currency exchange rate trends we collect Twitter data every day and compute the overall sentiment to label them as positive or negative. Experimental results have shown 69% correct prediction of sentiment analysis and 65.7% correlation with positive sentiments. This implies that EMH is semi-strong Efficient Market Hypothesis, and that public information provide by Twitter sentiment correlate with changes in the exchange market trends.

An Evaluation of Twitter Ranking Using the Retweet Information (재전송 정보를 활용한 트위터 랭킹의 정확도 평가)

  • Chang, Jae-Young
    • 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.

Automatic Retrieval of SNS Opinion Document Using Machine Learning Technique (기계학습을 이용한 SNS 오피니언 문서의 자동추출기법)

  • Chang, Jae-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.5
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    • pp.27-35
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    • 2013
  • Recently, as Social Network Services(SNS) are becoming more popular, much research has been doing on analyzing public opinions from SNS. One of the most important tasks for solving such a problem is to separate opinion(subjective) documents from others(e.g. objective documents) in SNS. In this paper, we propose a new method of retrieving the opinion documents from Twitter. The reason why it is not easy to search or classify the opinion documents in Twitter is due to a lack of publicly available Twitter documents for training. To tackle the problem, at first, we build a machine-learned model for sentiment classification using the external documents similar to Twitter, and then modify the model to separate the opinion documents from Twitter. Experimental results show that proposed method can be applied successfully in opinion classification.