Freddy Chong Tat Chua

Publications

Freddy Chong Tat Chua and Sitaram Asur. Automatic Summarization of Events from Social Media. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (ICWSM) 2013.
abstract
Social media services such as Twitter generate phenomenal volume of content for most real-world events on a daily basis. Digging through the noise and redundancy to understand the important aspects of the content is a very challenging task. We propose a search and summarization framework to extract relevant representative tweets from a time-ordered sample of tweets to generate a coherent and concise summary of an event. We introduce two topic models that take advantage of temporal correlation in the data to extract relevant tweets for summarization. The summarization framework has been evaluated using Twitter data on four real-world events. Evaluations are performed using Wikipedia articles on the events as well as using Amazon Mechanical Turk (MTurk) with human readers (MTurkers). Both experiments show that the proposed models outperform traditional LDA and lead to informative summaries.

bibtex
@inproceedings{freddy-icwsm13, author = {Chua, Freddy Chong Tat and Asur, Sitaram}, title = {Automatic Summarization of Events from Social Media}, booktitle = {Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (ICWSM) 2013}, series = {ICWSM '13}, year = {2013} }

Appendix The summaries given to Mechanical Turk workers for evaluation

Freddy Chong Tat Chua, Hady W. Lauw and Ee-Peng Lim. Generative Models for Item Adoptions Using Social Correlation. IEEE Transactions on Knowledge and Data Engineering, 05 July 2012.
abstract
Users face many choices on the Web when it comes to choosing which product to buy, which video to watch, etc. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: whether the items adopted by a user correlate to items adopted by her friends, and how to model item adoptions using social correlation. We propose a social correlation framework that considers a social correlation matrix representing the degrees of correlation from every user to the users friends, in addition to a set of latent factors representing topics of interests of individual users. Based on the framework, we develop two generative models, namely sequential and unified, and the corresponding parameter estimation approaches. From each model, we devise the social correlation only and hybrid methods for predicting missing adoption links. Experiments on LiveJournal and Epinions data sets show that our proposed models outperform the approach based on latent factors only (LDA).

bibtex
@ARTICLE{6235960, author={Chua, F. and Lauw, H. and Lim, E.}, journal={Knowledge and Data Engineering, IEEE Transactions on}, title={Generative Models for Item Adoptions Using Social Correlation}, year={2012}, month={ }, volume={PP}, number={99}, pages={1}, keywords={}, doi={10.1109/TKDE.2012.137}, ISSN={1041-4347},}

PDF Appendix

Freddy Chong Tat Chua, William W. Cohen, Justin Betteridge and Ee-Peng Lim. Community-Based Classification of Noun Phrases in Twitter. Proceedings of the 21st ACM International Conference on Information and Knowledge Management CIKM 2012, Maui, Hawaii, USA.
abstract
Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the author's behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base.

bibtex
@inproceedings{np_classification, author = {Chua, Freddy Chong Tat and Cohen, William W. and Betteridge, Justin and Lim, Ee-Peng}, title = {Community-Based Classification of Noun Phrases in Twitter}, booktitle = {Proceedings of the 21st ACM international conference on Information and knowledge management}, series = {CIKM '12}, year = {2012}, publisher = {ACM}, address = {New York, NY, USA}, }

PDF of Longer Version

Wei Gong, Ee-Peng Lim, Palakorn Achananuparp, Feida Zhu, David Lo and Freddy Chong Tat Chua. In-Game Action List Segmentation and Labeling in Real-Time Strategy Games. Proceedings of the IEEE Conference on Computational Intelligence and Games CIG 2012, Granada, Spain.
abstract
In-game actions of real-time strategy (RTS) games are extremely useful in determining the players’ strategies, analyzing their behaviors and recommending ways to improve their play skills. Unfortunately, unstructured sequences of in-game actions are hardly informative enough for these analyses. The inconsistency we observed in human annotation of in-game data makes the analytical task even more challenging. In this paper, we propose an integrated system for in-game action segmentation and semantic label assignment based on a Conditional Random Fields (CRFs) model with essential features extracted from the in-game actions. Our experiments demonstrate that the accuracy of our solution can be as high as 98.9%.

bibtex

Minh-Duc Luu, Ee-Peng Lim, Tuan-Anh Hoang, Freddy Chong Tat Chua. Modeling Diffusion in Social Networks using Network Properties. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM) 2012.
abstract
Diffusion of items occurs in social networks due to spreading of items through word of mouth and exogenous factors. These items may be news, products, videos, advertisements or contagious viruses. When a user purchases or consumes one of such items, we say that she adopts the item and she becomes an item adopter. Previous research has studied diffusion process at both the macro and micro levels. The former models the number of item adopters in the diffusion process while the latter determines which individuals adopt item. Both macro and micro level models have their merits and limitations. In this paper, we establish a general probabilistic framework, which can be used to derive macro-level diffusion models, including the well known Bass Model (BM). Using this framework, we develop several other models considering the social network’s degree distribution coupled with the assumption of linear influence by neighboring adopters in the diffusion process. Through some evaluation on synthetic data, this paper shows that degree distribution actually changes during the diffusion process. We therefore introduce a multi-stage diffusion model to cope with variable degree distribution. By conducting experiments on both synthetic and real datasets, we show that our proposed diffusion models can recover the diffusion parameters from the observed diffusion data, which allows us to model diffusion with high accuracy.

bibtex
@inproceedings{mdluu-12, author = {Duc Minh Luu and Ee-Peng Lim and Tuan-Anh Hoang and Freddy Chong Tat Chua}, title = {Modeling Diffusion in Social Networks Using Network Properties}, booktitle = {Proceedings of the Sixth International Conference on Weblogs and Social Media, Dublin, Ireland, June 4-7, 2012}, series = {ICWSM '12}, year = {2012} }

Freddy Chong Tat Chua, Hady W. Lauw and Ee-Peng Lim. Mining Social Dependencies in Dynamic Interaction Networks. Proceedings of the SIAM International Conference on Data Mining, SDM 2012, April 26 - 28, 2012, Anaheim, California, USA. SIAM 2012
abstract
User-to-user interactions have become ubiquitous in Web 2.0. Users exchange emails, post on newsgroups, tag web pages, co-author papers, etc. Through these interactions, users co-produce or co-adopt content items (e.g., words in emails, tags in social bookmarking sites). We model such dynamic interactions as a user interaction network, which relates users, interactions, and content items over time. After some interactions, a user may produce content that is more similar to those produced by other users previously. We term this effect social dependency, and we seek to mine from such networks the degree to which a user may be socially dependent on another user over time. We propose a Decay Topic Model to model the evolution of a user’s preferences for content items at the topic level, as well as a Social Dependency Metric that quantifies the extent of social dependency based on interactions and content changes. Our experiments on two user interaction networks induced from real-life datasets show the effectiveness of our approach.

bibtex
@inproceedings{freddy-sdm2012, author = {Chua, Freddy Chong Tat and Lauw, Hady W. and Lim, Ee-Peng}, title = {Mining Social Dependencies in Dynamic Interaction Networks}, booktitle = {SDM '12: Proceedings of the SIAM International Conference on Data Mining}, year = {2012}, location = {Anaheim, California, USA}, publisher = {SIAM}, address = {Philadelphia, PA, USA}, }

Bing Tian Dai, Freddy Chong Tat Chua and Ee-Peng Lim. Structural Analysis in Multi-Relational Social Networks. Proceedings of the SIAM International Conference on Data Mining, SDM 2012, April 26 - 28, 2012, Anaheim, California, USA. SIAM 2012
abstract
Modern social networks often consist of multiple relations among individuals. Understanding the structure of such multi-relational network is essential. In sociology, one way of structural analysis is to identify different positions and roles using blockmodels. In this paper, we generalize stochastic blockmodels to Generalized Stochastic Blockmodels (GSBM) for performing positional and role analysis on multi-relational networks. Our GSBM generalizes many different kinds of Multivariate Probability Distribution Function (MVPDF) to model different kinds of multi-relational networks. In particular, we propose to use multi-variate Poisson distribution for multi-relational social networks.

bibtex
@inproceedings{btdai-sdm2012, author = {Dai, Bing Tian and Chua, Freddy Chong Tat and Lim, Ee-Peng}, title = {Structural Analysis in Multi- Relational Social Networks}, booktitle = {SDM '12: Proceedings of the SIAM International Conference on Data Mining}, year = {2012}, location = {Anaheim, California, USA}, publisher = {SIAM}, address = {Philadelphia, PA, USA} }

Freddy Chong Tat Chua and Ee-Peng Lim. Modeling Bipartite Graphs Using Hierarchical Structures, In ASONAM ' 11: International Conference on Advances in Social Networks Analysis and Mining.
abstract
Bipartite networks are often used to capture the relationships between different classes of objects. To model the structure of bipartite networks, we propose a new hierarchical model based on a hierarchical random graph model originally designed for one-mode networks. The new model can better preserve the network fidelity as well as the assortative and disassortative structures of bipartite networks. We apply the proposed model on some paper-author networks in DBLP to find their optimal hierarchical structures. Using the optimal bipartite hierarchical structure, we regenerate networks that exhibit the similar network properties and degree distribution as the observed networks.

bibtex
@inproceedings{5992568, author={Chua, F.C.T. and Ee-Peng Lim}, booktitle={Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on}, title={Modeling Bipartite Graphs Using Hierarchical Structures}, year={2011}, month={july}, volume={}, number={}, pages={94 -101}, keywords={bipartite graphs;bipartite networks;disassortative structures;hierarchical random graph model;network fidelity;optimal bipartite hierarchical structure;graph theory;network theory (graphs);social networking (online);}, doi={10.1109/ASONAM.2011.45}, ISSN={}, }

Freddy Chong Tat Chua, Hady W. Lauw and Ee-Peng Lim. Predicting Item Adoption Using Social Correlation. Proceedings of the SIAM International Conference on Data Mining, SDM 2011, April 28 - 30, 2011, Mesa, Arizona, USA. SIAM 2011
abstract
Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to incorporate social correlation in order to improve prediction of unobserved item adoptions. We propose the Social Correlation model based on Latent Dirichlet Allocation (LDA) that decomposes the adoption graph into a set of latent factors reflecting user preferences, and a social correlation matrix reflecting the degree of correlation from one user to another. This matrix is learned (rather than pre-assigned), has probabilistic interpretation, and preserves the underlying social network structure. We further devise a Hybrid model that combines a user’s own latent factors with her friends’ for adoption prediction. Experiments on Epinions and LiveJournal data sets show that our proposed models outperform the approach based on latent factors only (LDA).

bibtex
@inproceedings{freddy-sdm, author = {Chua, Freddy Chong Tat and Lauw, Hady W. and Lim, Ee-Peng}, title = {Predicting Item Adoption Using Social Correlation}, booktitle = {SDM '11: Proceedings of the SIAM International Conference on Data Mining}, year = {2011}, location = {Mesa, Arizona, USA}, publisher = {SIAM}, address = {Philadelphia, PA, USA}, }

Freddy Chong Tat Chua and Ee-Peng Lim. Trust network inference for online rating data using generative models. In KDD 2010: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2010. ACM.
abstract
In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF) Model, a novel probabilistic model that generate ratings based on a number of rater's and contributor's factors. We demonstrate that parameters of the model can be learnt by Collapsed Gibbs Sampling. We then apply the model to predict trust and distrust between raters and review contributors using a real data-set. Our experiments have shown that the proposed model is capable of predicting both trust and distrust in a unified way. The model can also determine user factors which otherwise cannot be observed from the rating and trust data.

bibtex
@inproceedings{1835917, author = {Chua, Freddy Chong Tat and Lim, Ee-Peng}, title = {Trust network inference for online rating data using generative models}, booktitle = {KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining}, year = {2010}, isbn = {978-1-4503-0055-1}, pages = {889--898}, location = {Washington, DC, USA}, doi = {http://doi.acm.org/10.1145/1835804.1835917}, publisher = {ACM}, address = {New York, NY, USA}, }

Byung-Won On, Ee-Peng Lim, Jing Jiang, Freddy Chong Tat Chua, Viet-An Nguyen, Loo-Nin Teow, Messaging Behavior Modeling in Mobile Social Networks, International Symposium on Social Intelligence and Networking (SIN), Minneapolis, Minnesota, USA, August 20-22, 2010.
abstract
Mobile social networks are gaining popularity with the pervasive use of mobile phones and other handheld devices. In these networks, users maintain friendship links, exchange short messages and share content with one another. In this paper, we study the user behaviors in mobile messaging and friendship linking using the data collected from a large mobile social network service known as myGamma (m.mygamma.com). We distinguish two types of user behaviors: soliciting active responses for an initiated message (or link) and responding to an incoming message (or link). We propose various models for the two behaviors also known as activeness and responsiveness in both messaging and friendship networks. Our experiments show that the two behaviors are quite distinct from each other although they may be correlated. We also show that active and responsive users enjoy more friendships. Finally, we show that the active and responsive users participate more in messaging about major topics.

bibtex
@article{10.1109/SocialCom.2010.68, author = {Byung-Won On and Ee-Peng Lim and Jing Jiang and Freddy Chong Tat Chua and Viet-An Nguyen and Loo-Nin Teow}, title = {Messaging Behavior Modeling in Mobile Social Networks}, journal ={Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust, 2010 IEEE International Conference on}, volume = {0}, isbn = {978-0-7695-4211-9}, year = {2010}, pages = {425-430}, doi = {http://doi.ieeecomputersociety.org/10.1109/SocialCom.2010.68}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA} }

Freddy Chong Tat Chua

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