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Articles 91 - 120 of 249
Full-Text Articles in Social Media
The Role Of Knowledge Sharing Via Organizational Social Media In The Workplace, Murad A. Moqbel, Fiona Fui-Hoon Nah
The Role Of Knowledge Sharing Via Organizational Social Media In The Workplace, Murad A. Moqbel, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Although a plethora of studies have been recently conducted on organizational social media, little research has specifically examined how organizational social media can contribute to organizational workers’ performance through knowledge sharing. The objective of this research is to fill this gap by investigating the role of knowledge sharing in organizational social media use and its effect on in-role and innovative performance through the lens of social capital and social cognitive theories. Hypotheses were developed and a survey study is proposed.
Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell
Generating Cultural Personas From Social Data: A Perspective Of Middle Eastern Users, Salminen Joni, Sercan Sengün, Haewoon Kwak, Bernard Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D. Fox Harrell
Research Collection School Of Computing and Information Systems
We conduct a mixed-method study to better understand the content consumption patterns of Middle Eastern social media users and to explore new ways to present online data by using automatic persona generation. First, we analyze millions of content interactions on YouTube to dynamically generate personas describing behavioral patterns of different demographic groups. Second, we analyze interview data on social media users in the Middle Eastern region to generate additional insights into the dynamically generated personas. Our findings provide insights into social media users in the Middle East, as well as present a novel methodology of using computational analysis and qualitative …
A Domain Based Approach To Social Relation Recognition, Qianru Sun, Bernt Schiele, Mario Fritz
A Domain Based Approach To Social Relation Recognition, Qianru Sun, Bernt Schiele, Mario Fritz
Research Collection School Of Computing and Information Systems
Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is …
The Role Of Different Tie Strength In Disseminating Different Topics On A Microblog, Felicia Natali, Kathleen M. Carley, Feida Zhu, Binxuan Huang
The Role Of Different Tie Strength In Disseminating Different Topics On A Microblog, Felicia Natali, Kathleen M. Carley, Feida Zhu, Binxuan Huang
Research Collection School Of Computing and Information Systems
The study of information flow typically does not distinguish the choices of tie strength on which the information flows. All receivers of the information are assumed to have the same potential to pass on the information. Modifying the SEIZ (susceptible, exposed, infected, skeptic) model, we discover that people choose to retweet strong or weak ties based on the topic. We made two modifications in the model. In the first modification (Model I), we assume that the contact rates of agents in different compartment and the probability of an agent transitioning from one compartment to another are different for strong ties …
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Demographics Of News Sharing In The U.S. Twittersphere, Julio C.S. Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Benevenuto Fabrıcio.
Research Collection School Of Computing and Information Systems
The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to …
Culturally-Grounded Analysis Of Everyday Creativity In Social Media: A Case Study In Qatari Context, D. Fox Harrell, Sarah Vieweg, Haewoon Kwak, Chong-U Lim, Sercan Sengun, Ali Jahanian, Pablo. Ortiz
Culturally-Grounded Analysis Of Everyday Creativity In Social Media: A Case Study In Qatari Context, D. Fox Harrell, Sarah Vieweg, Haewoon Kwak, Chong-U Lim, Sercan Sengun, Ali Jahanian, Pablo. Ortiz
Research Collection School Of Computing and Information Systems
In deploying social media and other information technologies often not designed with MENA (the Middle East and North Africa) cultures in mind, users generate creative approaches to self-representation using virtual identities while preserving their cultural values. To understand and further empower such approaches, we present a mixed-method of computational and qualitative study, focusing on Qatar as a case of such communities in the MENA region. We analyzed a dataset of over 42,000 publicly available social media profiles using computational approaches (archetypal analysis) and qualitatively analyzed a separate set of 255 profiles. We augmented our descriptions with semi-structured interviews. As a …
Understanding Music Track Popularity In A Social Network, Jing Ren, Robert J. Kauffman
Understanding Music Track Popularity In A Social Network, Jing Ren, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
Thousands of music tracks are uploaded to the Internet every day through websites and social networks that focus on music. While some content has been popular for decades, some tracks that have just been released have been ignored. What makes a music track popular? Can the duration of a music track’s popularity be explained and predicted? By analysing data on the performance of a music track on the ranking charts, coupled with the creation of machine-generated music semantics constructs and a variety of other track, artist and market descriptors, this research tests a model to assess how track popularity and …
Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen
Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online …
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 …
Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim
Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we …
Fusing Social Media And Mobile Analytics For Urban Sense-Making, Archan Misra
Fusing Social Media And Mobile Analytics For Urban Sense-Making, Archan Misra
Research Collection School Of Computing and Information Systems
The project was motivated by the observation that urban environments are increasingly characterized by a variety of non-traditional “sensors”, whose data streams can be harnessed to infer a variety of latent events and urban context. For example, users spontaneously generate huge amounts of content (text, images and video) on social network channels, whereas GPS & other sensors on taxis and buses increasingly provide near-real time traces of their movement throughout the city. Similarly, advances in Wi-Fi based sensing allow us to passively capture the individual and collective movement of visitors across various public spaces, such as college campuses, museums and …
Collective Entity Linking In Tweets Over Space And Time, Wen Haw Chong, Ee-Peng Lim, William Cohen
Collective Entity Linking In Tweets Over Space And Time, Wen Haw Chong, Ee-Peng Lim, William Cohen
Research Collection School Of Computing and Information Systems
We propose collective entity linking over tweets that are close in space and time. This exploits the fact that events or geographical points of interest often result in related entities being mentioned in spatio-temporal proximity. Our approach directly applies to geocoded tweets. Where geocoded tweets are overly sparse among all tweets, we use a relaxed version of spatial proximity which utilizes both geocoded and non-geocoded tweets linked by common mentions. Entity linking is affected by noisy mentions extracted and incomplete knowledge bases. Moreover, to perform evaluation on the entity linking results, much manual annotation of mentions is often required. To …
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as …
Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim
Now You See It, Now You Don't! A Study Of Content Modification Behavior In Facebook, Fuxiang Chen, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social media, as a major platform to disseminate information, has changed the way users and communities contribute content. In this paper, we aim to study content modifications on public Facebook pages operated by news media, community groups, and bloggers. We also study the possible reasons behind them, and their effects on user interaction. We conducted a detailed study of Content Censorship (CC) and Content Edit (CE) in Facebook using a detailed longitudinal dataset consisting of 57 public Facebook pages over 3 weeks covering 145,955 posts and 9,379,200 comments. We detected many CC and CE activities between 28% and 56% of …
Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim
Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content …
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Research Collection School Of Computing and Information Systems
Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly …
Discovering Burst Patterns Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang
Discovering Burst Patterns Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang
Research Collection School Of Computing and Information Systems
Twitter has become one of largest social networks for users to broadcast burst topics. There have been many studies on how to detect burst topics. However, mining burst patterns in burst topics has not been solved by the existing works. In this paper, we investigate the problem of mining burst patterns of burst topic in Twitter. A burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, hierarchical clustering is applied to cluster burst topics and reveal burst patterns from the macro …
Harnessing Twitter To Support Serendipitous Learning Of Developers, Abhabhisheksh Sharma, Yuan Tian, Agus Sulistya, David Lo, Aiko Yamashita
Harnessing Twitter To Support Serendipitous Learning Of Developers, Abhabhisheksh Sharma, Yuan Tian, Agus Sulistya, David Lo, Aiko Yamashita
Research Collection School Of Computing and Information Systems
Developers often rely on various online resources, such as blogs, to keep themselves up-to-date with the fast pace at which software technologies are evolving. Singer et al. found that developers tend to use channels such as Twitter to keep themselves updated and support learning, often in an undirected or serendipitous way, coming across things that they may not apply presently, but which should be helpful in supporting their developer activities in future. However, identifying relevant and useful articles among the millions of pieces of information shared on Twitter is a non-trivial task. In this work to support serendipitous discovery of …
Fine-Grained Sentiment Analysis Of Social Media With Emotion Sensing, Zhaoxia Wang, Chee Seng Chong, Landy Lan, Yinping Yang, Beng-Seng Ho, Joo Chuan Tong
Fine-Grained Sentiment Analysis Of Social Media With Emotion Sensing, Zhaoxia Wang, Chee Seng Chong, Landy Lan, Yinping Yang, Beng-Seng Ho, Joo Chuan Tong
Research Collection School Of Computing and Information Systems
Social media is arguably the richest source of human generated text input. Opinions, feedbacks and critiques provided by internet users reflect attitudes and sentiments towards certain topics, products, or services. The sheer volume of such information makes it effectively impossible for any group of persons to read through. Thus, social media sentiment analysis has become an important area of work to make sense of the social media talk. However, most existing sentiment analysis techniques focus only on the aggregate level, classifying sentiments broadly into positive, neutral or negative, and lack the capabilities to perform fine-grained sentiment analysis. This paper describes …
Viewed By Too Many Or Viewed Too Little: Using Information Dissemination For Audience Segmentation, Bernard J. Jansen, Soon-Gyu Jung, Joni Salminen, Jisun An, Haewoon Kwak
Viewed By Too Many Or Viewed Too Little: Using Information Dissemination For Audience Segmentation, Bernard J. Jansen, Soon-Gyu Jung, Joni Salminen, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
The identification of meaningful audience segments, such as groups of users, consumers, readers, audience, etc., has important applicability in a variety of domains, including for content publishing. In this research, we seek to develop a technique for determining both information dissemination and information discrimination of online content in order to isolate audience segments. The benefits of the technique include identification of the most impactful content for analysis. With 4,320 online videos from a major news organization, a set of audience attributes, and more than 58 million interactions from hundreds of thousands of users, we isolate the key pieces of content …
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 …
‘Tweetboard’ – A Case Study Of Developing A Micro-Blogging Platform For Higher Education, Shao Cheh Joyce Hsu, Gan, Benjamin, Jin Lee, Shu Hui Sheryl Lim, Xie Yan Jeremy Lim, Thomas Menkhoff, Si Xian Sherman Tan, Charles Jason Woodard, Qiu Cheng Yap
‘Tweetboard’ – A Case Study Of Developing A Micro-Blogging Platform For Higher Education, Shao Cheh Joyce Hsu, Gan, Benjamin, Jin Lee, Shu Hui Sheryl Lim, Xie Yan Jeremy Lim, Thomas Menkhoff, Si Xian Sherman Tan, Charles Jason Woodard, Qiu Cheng Yap
Research Collection School Of Computing and Information Systems
This paper reports experiences made at an Asian university in developing a social media platform based on Twitter in the context of a final year capstone project where information systems management students get an opportunity to solve ‘a real-world problem for a real client’. In this case study, the challenge was provided by a faculty member’s request for an interactive social media application which engages less outspoken students in class via a social medium they are familiar with: Twitter. We reconstruct the project’s evolution; describe the main features of the application called ‘TweetBoard’ and share lessons learned in developing a …
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 …
Efficient Online Summarization Of Large-Scale Dynamic Networks, Qiang Qu, Siyuan Liu, Feida Zhu, Christian S. Jensen
Efficient Online Summarization Of Large-Scale Dynamic Networks, Qiang Qu, Siyuan Liu, Feida Zhu, Christian S. Jensen
Research Collection School Of Computing and Information Systems
Information diffusion in social networks is often characterized by huge participating communities and viral cascades of high dynamicity. To observe, summarize, and understand the evolution of dynamic diffusion processes in an informative and insightful way is a challenge of high practical value. However, few existing studies aim to summarize networks for interesting dynamic patterns. Dynamic networks raise new challenges not found in static settings, including time sensitivity, online interestingness evaluation, and summary traceability, which render existing techniques inadequate. We propose dynamic network summarization to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or …
Domain Identification For Intention Posts On Online Social Media, Thai Le Luong, Quoc Tuan Truong, Hai-Trieu Dang, Xuan Hieu Phan
Domain Identification For Intention Posts On Online Social Media, Thai Le Luong, Quoc Tuan Truong, Hai-Trieu Dang, Xuan Hieu Phan
Research Collection School Of Computing and Information Systems
Today, more and more Internet users are willing to share their feeling, activities, and even their intention about what they plan to do on online social media. We can easily see posts like "I plan to buy an apartment this year", or "We are looking for a tour for 3 people to Nha Trang" on online forums or social networks. Recognizing those user intents on online social media is really useful for targeted advertising. However fully understanding user intents is a complicated and challenging process which includes three major stages: user intent filtering, intent domain identification, and intent parsing and …
On Profiling Bots In Social Media, Richard J. Oentaryo, Arinto Murdopo, Philips K. Prasetyo, Ee Peng Lim
On Profiling Bots In Social Media, Richard J. Oentaryo, Arinto Murdopo, Philips K. Prasetyo, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling …
Spiteful, One-Off, And Kind: Predicting Customer Feedback Behavior On Twitter, Agus Sulistya, Abhishek Sharma, David Lo
Spiteful, One-Off, And Kind: Predicting Customer Feedback Behavior On Twitter, Agus Sulistya, Abhishek Sharma, David Lo
Research Collection School Of Computing and Information Systems
Social media provides a convenient way for customers to express their feedback to companies. Identifying different types of customers based on their feedback behavior can help companies to maintain their customers. In this paper, we use a machine learning approach to predict a customer’s feedback behavior based on her first feedback tweet. First, we identify a few categories of customers based on their feedback frequency and the sentiment of the feedback. We identify three main categories: spiteful, one-off, and kind. Next, we build a model to predict the category of a customer given her first feedback. We use profile and …
Tracking Virality And Susceptibility In Social Media, Tuan Anh Hoang, Ee-Peng Lim
Tracking Virality And Susceptibility In Social Media, Tuan Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
In social media, the magnitude of information propagation hinges on the virality and susceptibility of users spreading and receiving the information respectively, as well as the virality of information items. These users' and items' behavioral factors evolve dynamically at the same time interacting with one another. Previous works however measure the factors statically and independently in a restricted case: each user has only a single adoption on each item, and/or users' exposure to items are observable. In this work, we investigate the inter-relationship among the factors and users' multiple adoptions on items to propose both new static and temporal models …
When A Friend Online Is More Than A Friend In Life: Intimate Relationship Prediction In Microblogs, Yunshi Lan, Mengqi Zhang, Feida Zhu, Jing Jiang, Ee-Peng Lim
When A Friend Online Is More Than A Friend In Life: Intimate Relationship Prediction In Microblogs, Yunshi Lan, Mengqi Zhang, Feida Zhu, Jing Jiang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Microblogging services such as Twitter and Sina Weibo have been an important, if not indespensible, platform for people around the world to connect to one another. The rich content and user interactions on these platforms reveal insightful information about each user that are valuable for various real-life applications. In particular, user offline relationships, especially those intimate ones such as family members and couples, offer distinctive value for many business and social settings. In this study, we focus on using Sina Weibo to discover intimate offline relationships among users. The problem is uniquely interesting and challenging due to the difficulty in …
Detecting Community Pacemakers Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang
Detecting Community Pacemakers Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang
Research Collection School Of Computing and Information Systems
Twitter has become one of largest social networks for users to broad-cast burst topics. Influential users usually have a large number of followers and play an important role in the diffusion of burst topic. There have been many studies on how to detect influential users. However, traditional influential users detection approaches have largely ignored influential users in user community. In this paper, we investigate the problem of detecting community pacemakers. Community pacemakers are defined as the influential users that promote early diffusion in the user community of burst topic. To solve this problem, we present DCPBT, a framework that can …