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Articles 1 - 13 of 13
Full-Text Articles in Social Media
Who Is Missing? Characterizing The Participation Of Different Demographic Groups In A Korean Nationwide Daily Conversation Corpus, Haewoon Kwak, Jisun An, Kunwoo Park
Who Is Missing? Characterizing The Participation Of Different Demographic Groups In A Korean Nationwide Daily Conversation Corpus, Haewoon Kwak, Jisun An, Kunwoo Park
Research Collection School Of Computing and Information Systems
A conversation corpus is essential to build interactive AI applications. However, the demographic information of the participants in such corpora is largely underexplored mainly due to the lack of individual data in many corpora. In this work, we analyze a Korean nationwide daily conversation corpus constructed by the National Institute of Korean Language (NIKL) to characterize the participation of different demographic (age and sex) groups in the corpus.
An Analysis Of Rumor And Counter-Rumor Messages In Social Media, Dion Hoe-Lian Goh, Alton Y. K. Chua, Hanyu Shi, Wenju Wei, Haiyan Wang, Ee-Peng Lim
An Analysis Of Rumor And Counter-Rumor Messages In Social Media, Dion Hoe-Lian Goh, Alton Y. K. Chua, Hanyu Shi, Wenju Wei, Haiyan Wang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social media platforms are one of the fastest ways to disseminate information but they have also been used as a means to spread rumors. If left unchecked, rumors have serious consequences. Counter-rumors, messages used to refute rumors, are an important means of rumor curtailment. The objective of this paper is to examine the types of rumor and counter-rumor messages generated in Twitter in response to the falsely reported death of a politician, Lee Kuan Yew, who was Singapore’s first Prime Minister. Our content analysis of 4321Twitter tweets about Lee’s death revealed six categories of rumor messages, four categories ofcounter-rumor messages …
An Unsupervised Multilingual Approach For Online Social Media Topic Identification, Siaw Ling Lo, Raymond Chiong, David Cornforth
An Unsupervised Multilingual Approach For Online Social Media Topic Identification, Siaw Ling Lo, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
Social media data can be valuable in many ways. However, the vast amount of content shared and the linguistic variants of languages used on social media are making it very challenging for high-value topics to be identified. In this paper, we present an unsupervised multilingual approach for identifying highly relevant terms and topics from the mass of social media data. This approach combines term ranking, localised language analysis, unsupervised topic clustering and multilingual sentiment analysis to extract prominent topics through analysis of Twitter’s tweets from a period of time. It is observed that each of the ranking methods tested has …
Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang
Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang
Research Collection School Of Computing and Information Systems
Social influence has attracted significant attention owing to the prevalence of social networks (SNs). In this paper, we study a new social influence problem, called personalized social influential tags exploration (PITEX), to help any user in the SN explore how she influences the network. Given a target user, it finds a size-k tag set that maximizes this user’s social influence. We prove the problem is NP-hard to be approximated within any constant ratio. To solve it, we introduce a sampling-based framework, which has an approximation ratio of 1−ǫ 1+ǫ with high probabilistic guarantee. To speedup the computation, we devise more …
Hashtag Recommendation With Topical Attention-Based Lstm, Yang Li, Ting Liu, Jing Jiang, Liang Zhang
Hashtag Recommendation With Topical Attention-Based Lstm, Yang Li, Ting Liu, Jing Jiang, Liang Zhang
Research Collection School Of Computing and Information Systems
Microblogging services allow users to create hashtags to categorize their posts. In recent years,the task of recommending hashtags for microblogs has been given increasing attention. However,most of existing methods depend on hand-crafted features. Motivated by the successful use oflong short-term memory (LSTM) for many natural language processing tasks, in this paper, weadopt LSTM to learn the representation of a microblog post. Observing that hashtags indicatethe primary topics of microblog posts, we propose a novel attention-based LSTM model whichincorporates topic modeling into the LSTM architecture through an attention mechanism. Weevaluate our model using a large real-world dataset. Experimental results show that …
From Footprint To Evidence: An Exploratory Study Of Mining Social Data For Credit Scoring, Guangming Guo, Feida Zhu, Enhong Chen, Qi Liu, Le Wu, Chu Guan
From Footprint To Evidence: An Exploratory Study Of Mining Social Data For Credit Scoring, Guangming Guo, Feida Zhu, Enhong Chen, Qi Liu, Le Wu, Chu Guan
Research Collection School Of Computing and Information Systems
With the booming popularity of online social networks like Twitter and Weibo, online user footprints are accumulating rapidly on the social web. Simultaneously, the question of how to leverage the large-scale user-generated social media data for personal credit scoring comes into the sight of both researchers and practitioners. It has also become a topic of great importance and growing interest in the P2P lending industry. However, compared with traditional financial data, heterogeneous social data presents both opportunities and challenges for personal credit scoring. In this article, we seek a deep understanding of how to learn users’ credit labels from social …
Social Signal Processing For Real-Time Situational Understanding: A Vision And Approach, Kasthuri Jeyarajah, Shuchao Yao, Raghava Muthuraju, Archan Misra, Geeth De Mel, Julie Skipper, Tarek Abdelzaher, Michael Kolodny
Social Signal Processing For Real-Time Situational Understanding: A Vision And Approach, Kasthuri Jeyarajah, Shuchao Yao, Raghava Muthuraju, Archan Misra, Geeth De Mel, Julie Skipper, Tarek Abdelzaher, Michael Kolodny
Research Collection School Of Computing and Information Systems
The US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper …
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results …
Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong
Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong
Research Collection School Of Computing and Information Systems
Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of …
Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu
Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu
Research Collection School Of Computing and Information Systems
Influential user can play a crucial role in online social networks. This paper documents an empirical study aiming at exploring the effects of influential users in the context of music social network. To achieve this goal, music diffusion graph is developed to model how information propagates over network. We also propose a heuristic method to measure users' influences. Using the real data from Last. fm, our empirical test demonstrates key effects of influential users and reveals limitations of existing influence identification/characterization schemes.
Social Media For Supply Chain Risk Management, Xiuju Fu, Rick S. M. Goh, J. C. Tong, Loganathan Ponnanbalam, Xiaofeng Yin, Zhaoxia Wang, H. Y. Xu, Sifei Lu
Social Media For Supply Chain Risk Management, Xiuju Fu, Rick S. M. Goh, J. C. Tong, Loganathan Ponnanbalam, Xiaofeng Yin, Zhaoxia Wang, H. Y. Xu, Sifei Lu
Research Collection School Of Computing and Information Systems
With the rapid increase of online social network users worldwide, social media feeds have become a rich and valuable information resource and attract great attention across diversified domains. In social media data, there are abundant contents of two-way and interactive communication about products, demand, customer services and supply. This makes social media a valuable channel for listening to the voices from the market and measuring supply chain risks and new market trends for companies. In this study, we surveyed the potential value of social media in supply chain risk management (SCRM) and examined how they can be applied to SCRM …
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social network analysis has received much attention from corporations recently. Corporations are trying to utilize social media platforms such as Twitter, Facebook and Sina Weibo to expand their own markets. Our system is an online tool to assist these corporations to 1) find potential customers, and 2) track a list of users by specific events from social networks. We employ both textual and network information, and thus produce a keyword-based relevance score for each user in pre-defined dimensions, which indicates the probability of the adoption of a product. Based on the score and its trend, out tool is able to …
A Probabilistic Graphical Model For Topic And Preference Discovery On Social Media, Lu Liu, Feida Zhu, Lei Zhang, Shiqiang Yang
A Probabilistic Graphical Model For Topic And Preference Discovery On Social Media, Lu Liu, Feida Zhu, Lei Zhang, Shiqiang Yang
Research Collection School Of Computing and Information Systems
Many web applications today thrive on offering services for large-scale multimedia data, e.g., Flickr for photos and YouTube for videos. However, these data, while rich in content, are usually sparse in textual descriptive information. For example, a video clip is often associated with only a few tags. Moreover, the textual descriptions are often overly specific to the video content. Such characteristics make it very challenging to discover topics at a satisfactory granularity on this kind of data. In this paper, we propose a generative probabilistic model named Preference-Topic Model (PTM) to introduce the dimension of user preferences to enhance the …