• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.59-77
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• 2009

Microblog services are such new communication channels with some considerable potential to improve information sharing. The idea of sharing short messages using multiple access points seems to be appealing to people worldwide. Through the lens of flow and social identity, we explored factors that influence information sharing behaviors in microblog services. With an empirical study, we examined enjoyment and concentration(flow) and self-presentation(social identity) in microblog services like twitter can contribute to the user behaviors. Our aim was to gain insight into ways of creating an environment that facilitating voluntary sharing of information. Our findings suggested that enjoyment, concentration, and selfpresentation were crucial determinants of information sharing behaviors in microblog services. This study has important implications for academic researchers and practitioners who seek to understand why microblog service users share their information with other members in microblog services.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.727-739
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• 2018

This study evaluates the viewpoints of user focus incidents using microblog sentiment analysis, which has been actively researched in academia. Most existing works have adopted traditional supervised machine learning methods to analyze emotions in microblogs; however, these approaches may not be suitable in Chinese due to linguistic differences. This paper proposes a new microblog sentiment analysis method that mines associated microblog emotions based on a popular microblog through user-building combined with spectral clustering to analyze microblog content. Experimental results for a public microblog benchmark corpus show that the proposed method can improve identification accuracy and save manually labeled time compared to existing methods.

• The Journal of Information Systems
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• v.23 no.1
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• pp.73-91
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• 2014

Microblog is emerging as a new communication service because of its usefulness and real-time accessability. Recently, microblog services, such as twitter and me2day in Korea, are getting a great attention. Continuous use intention is critical to sustain the service. However, most recent studies are based on Technology Acceptance Model(TAM) and Expectation Confirmation Model(ECM). These models are only focused on individual factors and overlook social influence factors. Social influence has been indicated as a critical factor of technology adoption and diffusion in social context(Davis, 1989; Fulk et al., 1987). In this study, we explore factors related to social influence which effect on continuous use intention for 'me2day' that is one of the most famous microblog in Korea. The purpose of this study is to understand continuous use intention and examine the relationship among social influence factors, social presence, and continuous use intention. To understand the phenomenon of continuous use intention in microblog service, this study employed social influence theory and expanded it by adding personal network exposure and group norm as additional social influence factors. The results show that social identity, group norms, and social presence positively influences continuous use intention. Contrary to our expectation, personal network exposure does not influence on continuous use intention. Academically, this research can contribute to microblog research field through elucidating the relationship among social influence factors, social presence, and continuous use intention. Although there is not enough research which is considered social influence factors as major explanation for continuous use intention, this study can give novel point of view to understand continuous use intention of microblog. Practically, service providers could consider ways to encourage users to continually use microblog service by reinforcing social influence factors and social presence.

• Journal of KIISE
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• v.42 no.6
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• pp.781-790
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• 2015

In recent times, microblogs have become popular owing to the development of the Internet and mobile environments. Among the various types of microblog data, those containing location data are referred to as spatial social Web objects. General aggregations of such microblog data include data aggregation per user for a single piece of information. This study proposes a spatial aggregation algorithm that combines a general aggregation with spatial data and uses the Geohash and MapReduce operations to perform spatial social analysis, by using microblog data with the characteristics of a spatial social Web object. The proposed algorithm provides the foundation for a meaningful spatial social analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2627-2647
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• 2016

With the rapid growth of microblog, expert identification on microblog has been playing a crucial role in many applications. While most previous expert identification studies only assess global authoritativeness of a user, there is no way to differentiate the authoritativeness in a particular aspect of topics. In this paper, we propose a novel model, which jointly models text and following relationship in the same generative process. Furthermore, we integrate a similarity-based weight scheme into the model to address the popular bias problem, and use followee topic distribution as prior information to make user's topic distribution more precisely. Our empirical study on two large real-world datasets shows that our proposed model produces significantly higher quality results than the prior arts.

• ETRI Journal
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• v.38 no.1
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• pp.52-61
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• 2016

A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user's direct appeal, a user's influence region, and a user's global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms - PageRank, Betweeness Centrality, Closeness Centrality, Out-degree - using a new tweets propagation model - the Ignorants-Spreaders-Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.161-172
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• 2014

While innovative information technologies have been adopted quickly into our daily life, we often experience the discontinuance after being temporarily adopted. This paper aims to study the factors influencing discontinuance on Microblog to Chinese users who already temporarily adopted it. According to empirical analysis, complexity and security show up their influences into all perceived risks, and inconvenience innovation and subjective norm just effect social risks. And perceived risk shows influences in every reasons for discontinuance, especially lots influences come from complexity. So according to these results, it is needed that the complexity of Microblog should be reduced.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.853-864
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• 2019

Microblogging services (such as Twitter) are the representative information communication networks during the Web 2.0 era, which have gained remarkable popularity. Weibo has become a popular platform for information dissemination in online social networks due to its large number of users. In this study, a microblog information dissemination model is presented. Related concepts are introduced and analyzed based on the dynamic model of infectious disease, and new influencing factors are proposed to improve the susceptible-infective-removal (SIR) information dissemination model. Correlation analysis is conducted on the existing information dissemination risk and the rumor dissemination model of microblog. In this study, web hyper is used to model rumor dissemination. Finally, the experimental results illustrate the effectiveness of the method in reducing the rumor dissemination of microblogs.