Open Access. Powered by Scholars. Published by Universities.®
Numerical Analysis and Scientific Computing Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Social media (28)
- Twitter (20)
- Sentiment analysis (10)
- Data mining (6)
- Social networks (5)
-
- Facebook (4)
- Machine learning (4)
- Microblogging (4)
- Natural language processing (4)
- Analytics (3)
- Data visualization (3)
- Demographics (3)
- Opinion mining (3)
- Privacy (3)
- Singapore (3)
- Social network (3)
- Text mining (3)
- Classification (2)
- Data Mining and Knowledge Discovery (2)
- Direct antagonistic community (2)
- Feature extraction (2)
- Influence (2)
- Machine Learning (2)
- Mining maximal bi-cliques (2)
- Model (2)
- NLP (2)
- Natural Language Processing (2)
- Networks (2)
- News sharing (2)
- Predictive modeling (2)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (89)
- Dissertations and Theses Collection (Open Access) (5)
- Graduate Theses and Dissertations (2)
- Research Collection Lee Kong Chian School Of Business (2)
- Asian Management Insights (1)
-
- College of Graduate Studies: Theses & Dissertations (1)
- Computer Science and Computer Engineering Undergraduate Honors Theses (1)
- Conference Papers (1)
- Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research (1)
- Electronic Theses and Dissertations (1)
- Library Philosophy and Practice (e-journal) (1)
- Research Collection School of Social Sciences (1)
- The African Journal of Information Systems (1)
- Publication Type
Articles 91 - 107 of 107
Full-Text Articles in Numerical Analysis and Scientific Computing
Hypergraph Index: An Index For Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng
Hypergraph Index: An Index For Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng
Research Collection School Of Computing and Information Systems
Social network has been touted as the No. 2 innovation in a recent IEEE Spectrum Special Report on “Top 11 Technologies of the Decade”, and it has cemented its status as a bona fide Internet phenomenon. With more and more people starting using social networks to share ideas, activities, events, and interests with other members within the network, social networks contain a huge amount of content. However, it might not be easy to navigate social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to …
The Impact Of Smartphone Adoption On Consumers’ Switching Behavior In Broadband And Cable Tv Services, Gwangjae Jung
The Impact Of Smartphone Adoption On Consumers’ Switching Behavior In Broadband And Cable Tv Services, Gwangjae Jung
Research Collection School Of Computing and Information Systems
The emergence of smartphones has brought a technology disruption to the telecom business. Due to the various services offered in association with smartphones, people can surf the web or watch TV. This research is related to telecom services overall, and has the goal of finding the impact of smartphone adoption to consumers’ switching behavior in broadband and cable TV services. This research adopts a quasi-experimental design and investigates the causal effect of smartphone service adoption on broadband and cable TV service choices. The data collection involves five years of consumer service subscriptions in a Singaporean telecommunications company. I tested for …
A Survey Of Recommender Systems In Twitter, Su Mon Kywe, Ee Peng Lim, Feida Zhu
A Survey Of Recommender Systems In Twitter, Su Mon Kywe, Ee Peng Lim, Feida Zhu
Research Collection School Of Computing and Information Systems
Twitter is a social information network where short messages or tweets are shared among a large number of users through a very simple messaging mechanism. With a population of more than 100M users generating more than 300M tweets each day, Twitter users can be easily overwhelmed by the massive amount of information available and the huge number of people they can interact with. To overcome the above information overload problem, recommender systems can be introduced to help users make the appropriate selection. Researchers have began to study recommendation problems in Twitter but their works usually address individual recommendation tasks. There …
Influentials, Novelty, And Social Contagion: The Viral Power Of Average Friends, Close Communities, And Old News, Nicholas Harrigan, Palakorn Achananuparp, Ee Peng Lim
Influentials, Novelty, And Social Contagion: The Viral Power Of Average Friends, Close Communities, And Old News, Nicholas Harrigan, Palakorn Achananuparp, Ee Peng Lim
Research Collection School Of Computing and Information Systems
What is the effect of (1) popular individuals, and (2) community structures on the retransmission of socially contagious behavior? We examine a community of Twitter users over a five month period, operationalizing social contagion as ‘retweeting’, and social structure as the count of subgraphs (small patterns of ties and nodes) between users in the follower/following network. We find that popular individuals act as ‘inefficient hubs’ for social contagion: they have limited attention, are overloaded with inputs, and therefore display limited responsiveness to viral messages. We argue this contradicts the ‘law of the few’ and ‘influentials hypothesis’. We find that community …
Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim
Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneousy captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) …
Topic Discovery From Tweet Replies, Bingtian Dai, Ee Peng Lim, Philips Kokoh Prasetyo
Topic Discovery From Tweet Replies, Bingtian Dai, Ee Peng Lim, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness.
Visualizing Media Bias Through Twitter, Jisun An, Meeyoung Cha, Gummadi, Krishna, Jon Crowcroft, Daniele Queria
Visualizing Media Bias Through Twitter, Jisun An, Meeyoung Cha, Gummadi, Krishna, Jon Crowcroft, Daniele Queria
Research Collection School Of Computing and Information Systems
Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Therefore, tracking bias in everyday news and building a platform where people can receive balanced news information is important. We propose a model that maps the news media sources along a dimensional dichotomous political spectrum using the co-subscriptions relationships inferred by Twitter links. By analyzing 7 million follow links, we show that the political dichotomy naturally arises on Twitter when we only consider direct media subscription. Furthermore, we demonstrate a real-time Twitter-based application …
Mining Diversity On Social Media Networks, Lu Liu, Feida Zhu, Meng Jiang, Jiawei Han, Lifeng Sun, Shiqiang Yang
Mining Diversity On Social Media Networks, Lu Liu, Feida Zhu, Meng Jiang, Jiawei Han, Lifeng Sun, Shiqiang Yang
Research Collection School Of Computing and Information Systems
The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube, MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia and industry. However, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. …
Mining Direct Antagonistic Communities In Explicit Trust Networks, David Lo, Didi Surian, Zhang Kuan, Ee Peng Lim
Mining Direct Antagonistic Communities In Explicit Trust Networks, David Lo, Didi Surian, Zhang Kuan, Ee Peng Lim
Research Collection School Of Computing and Information Systems
There has been a recent increase of interest in analyzing trust and friendship networks to gain insights about relationship dynamics among users. Many sites such as Epinions, Facebook, and other social networking sites allow users to declare trusts or friendships between different members of the community. In this work, we are interested in extracting direct antagonistic communities (DACs) within a rich trust network involving trusts and distrusts. Each DAC is formed by two subcommunities with trust relationships among members of each sub-community but distrust relationships across the sub-communities. We develop an efficient algorithm that could analyze large trust networks leveraging …
On Modeling Virality Of Twitter Content, Tuan Anh Hoang, Ee Peng Lim, Palakorn Achananuparp, Jing Jiang, Feida Zhu
On Modeling Virality Of Twitter Content, Tuan Anh Hoang, Ee Peng Lim, Palakorn Achananuparp, Jing Jiang, Feida Zhu
Research Collection School Of Computing and Information Systems
Twitter is a popular microblogging site where users can easily use mobile phones or desktop machines to generate short messages to be shared with others in realtime. Twitter has seen heavy usage in many recent international events including Japan earthquake, Iran election, etc. In such events, many tweets may become viral for different reasons. In this paper, we study the virality of socio-political tweet content in the Singapore’s 2011 general election (GE2011). We collected tweet data generated by about 20K Singapore users from 1 April 2011 till 12 May 2011, and the follow relationships among them. We introduce several quantitative …
Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng
Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng
Research Collection School Of Computing and Information Systems
Social networking has grown rapidly over the last few years, and social networks contain a huge amount of content. However, it can be not easy to navigate the social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to retrieve the nearest node to the query node that matches the context specified. CANN considers both the structure of the social network, and the profile information of the nodes. We design ahyper-graph based index structure to support approximated CANN search efficiently.
Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li
Topical Keyphrase Extraction From Twitter, Xin Zhao, Jing Jiang, Jing He, Yang Song, Palakorn Achananuparp, Ee Peng Lim, Xiaoming Li
Research Collection School Of Computing and Information Systems
Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to …
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Research Collection School Of Computing and Information Systems
Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework …
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Extracting Common Emotions From Blogs Based On Fine-Grained Sentiment Clustering, Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people’s collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people’s collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In …
Twitterrank: Finding Topic-Sensitive Influential Twitterers, Jianshu Weng, Ee Peng Lim, Jing Jiang, Qi He
Twitterrank: Finding Topic-Sensitive Influential Twitterers, Jianshu Weng, Ee Peng Lim, Jing Jiang, Qi He
Research Collection School Of Computing and Information Systems
This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can …
Social Network Discovery By Mining Spatio-Temporal Events, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Social Network Discovery By Mining Spatio-Temporal Events, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Research Collection School Of Computing and Information Systems
Knowing patterns of relationship in a social network is very useful for law enforcement agencies to investigate collaborations among criminals, for businesses to exploit relationships to sell products, or for individuals who wish to network with others. After all, it is not just what you know, but also whom you know, that matters. However, finding out who is related to whom on a large scale is a complex problem. Asking every single individual would be impractical, given the huge number of individuals and the changing dynamics of relationships. Recent advancement in technology has allowed more data about activities of individuals …