• Journal of Contemporary Eastern Asia
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• v.17 no.2
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• pp.147-175
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• 2018

Under authoritarian regimes, citizen-led NGOs such as environmental NGOs (ENGOs) often operate under close scrutiny of the government. While this presents a challenge to a single ENGO, we propose here - in line with existing research on network effects - that there are opportunities for multiple ENGOs to coordinate and thus work in ways that supersede government controls, affect public opinion, and contribute to policy revision and/or creation. In this paper, we specifically examine the possibility that the gamut of citizen-based ENGOs in China are coordinating. Based on network analysis of ENGOs web pages as well as interviews with more than a dozen ENGO leaders between 2014 and 2016, we find that ENGOs have few direct and public connections to each other, but social media sites and personal connections offline provide a crucial function in creating bridges. A closer examination of these bridges reveals, however, that they can be substantive to the environmental discussion or functional to the dissemination of web page information but typically not both. In short, ENGOs in China are not directly connected but rather are connected in a way that responds to the available social media and the government's censorship practices.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.29-37
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• 2011

Recently, many user-participating web services have been used widely as the evolution of internet web technology has rapidly been developed. Users share various content and opinion on line using a site like ‘Social bookmarking.’ Users can share others’ bookmarking history and create tags while bookmarking web sites; we call it collaborative tagging. In this paper, we studied empirical analysis for widely used social bookmarking and collaborative tagging which the result shows minority of users is actively using the bookmarking and a few sites and tags are used by majority of the users. 24% users tagged 80%, 75% sites and 81% tags were tagged below than 3 times. Types of bookmarking activities were found different by users and early appointed tags get more frequency by majority. We also identified relative proportions of tags on certain sites are becoming convergence gradually. We expect the result of this paper will give opportunities to help further developing social bookmarking system.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• International Journal of Costume and Fashion
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• v.13 no.1
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• pp.1-10
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• 2013

This study examines existing literature and uses case studies to identify the scope of fashion blogging and the evolving role of the personal style bloggers in the fashion business. Information on six popular personal style bloggers has been gathered to demonstrate the evolving business of these bloggers and their scope of operations that are relevant to fashion. The case of these six bloggers were drawn from popular media publications such as CNN, Wall Street Journal (WSJ), Time, Women's Wear Daily (WWD) and The New York Times. The case study of these six bloggers reveals that these personal style bloggers have used their reach and influence on the blog audience to procure and access business opportunities to grow their enterprise. The findings indicate that affiliation, partnership or collaboration with brands or established designers adds more value to a personal style bloggers resume. However, the findings also indicate that the popular fashion bloggers provide a new opportunity for marketing and promoting fashion brands and products to the younger generation.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.5-22
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• 2017

This study discusses the library professionals' perception of the ICT Applications in engineering college libraries in Tamil Nadu. The relevant data was collected from the library professionals in the self financing engineering colleges situated in Kanchipuram and Thiruvallur district of Tamil Nadu. 625 questionnaires were distributed, 504 replied with a response rate of 80.64%. It was found that the respondents with experience 'Below 5 years'gave 'Lack of infrastructure' as the first priority. 'Lack of interest on the part of users' and 'No support from administration in training library professionals' were the second and third preferences indicated by the respondents. The least preferences were given for 'Fear of ICT application'. Similarly, respondents with experience '6-10 years' indicated 'No support from administration in training library professionals' as the first priority. The least preference was given for 'Inadequate training in ICT applications' by the above respondents. It can be inferred that the professionals accepted and need the training in ICT applications.

• Iberoamérica
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• v.22 no.2
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• pp.99-122
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• 2020

The design and implementation of the so-called Mexican Fourth Transformation(Cuarta Transformacion, 4T), as an alternative form of government, faces daily the attack and violence of conservatism through a permanent campaign that seeks to instill fear in society, based on a web of lies that tries to create the image of a situation of political ungovernance in the public opinion. This work is a documentary analysis that accounts for the types of harassment of the conservative elite against the 4T; the objective is to show the forms of violence that threaten democracy. In conclusion, this study attempts to argue that the violence that conservative neoliberals caused against the 4T has its origin in that they feel threatened, because the reform policy of AMLO goes against their class interests.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.51-66
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• 2018

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.421-433
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• 2015

Recently, there have been fast-growing social network services based on the Internet environment and web technology development, the prevalence of smartphones, etc. Social networks also allow the users to convey the information and news so that they have a great influence on the public opinion formed by social interaction among users as well as the spread of information. On the other hand, these social networks also serve as perfect environments for rampant malware. Malware is rapidly being spread because relationships are formed on trust among the users. In this paper, an effective patch strategy is proposed to deal with malicious worms based on social networks. A graph is formed to analyze the structure of a social network, and subgroups are formed in the graph for the distributed patch strategy. The weighted directions and activities between the nodes are taken into account to select reliable key nodes from the generated subgroups, and the Incremental PageRanking algorithm reflecting dynamic social network features (addition/deletion of users and links) is used for deriving the high influential key nodes. With the patch based on the derived key nodes, the proposed method can prevent worms from spreading over social networks.

• The Journal of Information Systems
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• v.32 no.2
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• pp.177-190
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• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.559-568
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• 2014

Previous polarity classification using sentiment analysis utilizes a sentence rule by product reviews based rating points. It is difficult to be applied to blogs which have not rating of product reviews and is possible to fabricate product reviews by comment part-timers and managers who use web site so it is not easy to understand a product and store reviews which are reliability. Considering to these problems, if we analyze blogs which have personal and frank opinions and classify polarity, it is possible to understand rightly opinions for the product, store. This paper suggests that we extract high frequency vocabularies in blogs by several domains and choose topic words. Then we apply a technique of sentiment analysis and classify polarity about contents of blogs. To evaluate performances of sentiment analysis, we utilize the measurement index that use Precision, Recall, F-Score in an information retrieval field. In a result of evaluation, using suggested sentiment analysis is the better performances to classify polarity than previous techniques of using the sentence rule based product reviews.