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Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Numerical Analysis and Scientific Computing
Singapore Management University
Research Collection School Of Computing and Information Systems
- Keyword
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- Social media (2)
- Behavior studies (1)
- Bot profiling (1)
- Classification (1)
- Data mining (1)
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- Demographics (1)
- Domain classification (1)
- Event Detection (1)
- Feature extraction (1)
- Hashtags (1)
- Human behaviors (1)
- Information Fusion (1)
- Intention mining (1)
- Mobility pattern (1)
- Multi-Modal Sensing (1)
- New York (1)
- Online social networks (1)
- Pattern clustering (1)
- Singapore haze (1)
- Social media analyses (1)
- Social media text understanding (1)
- Text classification (1)
- Twitter (1)
- Urban planning (1)
- User intent identification (1)
Articles 1 - 7 of 7
Full-Text Articles in Social and Behavioral Sciences
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 …
#Greysanatomy Vs. #Yankees: Demographics And Hashtag Use On Twitter, Jisun An, Ingmar Weber
#Greysanatomy Vs. #Yankees: Demographics And Hashtag Use On Twitter, Jisun An, Ingmar Weber
Research Collection School Of Computing and Information Systems
Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also …
Mining And Clustering Mobility Evolution Patterns From Social Media For Urban Informatics, Chien-Cheng Chen, Meng-Fen Chiang, Wen-Chih Peng
Mining And Clustering Mobility Evolution Patterns From Social Media For Urban Informatics, Chien-Cheng Chen, Meng-Fen Chiang, Wen-Chih Peng
Research Collection School Of Computing and Information Systems
In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are …
Context-Aware Advertisement Recommendation For High-Speed Social News Feeding, Yuchen Li, Dongxiang Zhang, Ziquan Lan, Kian-Lee Tan
Context-Aware Advertisement Recommendation For High-Speed Social News Feeding, Yuchen Li, Dongxiang Zhang, Ziquan Lan, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Social media advertising is a multi-billion dollar market and has become the major revenue source for Facebook and Twitter. To deliver ads to potentially interested users, these social network platforms learn a prediction model for each user based on their personal interests. However, as user interests often evolve slowly, the user may end up receiving repetitive ads. In this paper, we propose a context-aware advertising framework that takes into account the relatively static personal interests as well as the dynamic news feed from friends to drive growth in the ad click-through rate. To meet the real-time requirement, we first propose …
Ontology-Aided Feature Correlation For Multi-Modal Urban Sensing, Archan Misra, Zaman Lantra, Kasthuri Jayarajah
Ontology-Aided Feature Correlation For Multi-Modal Urban Sensing, Archan Misra, Zaman Lantra, Kasthuri Jayarajah
Research Collection School Of Computing and Information Systems
The paper explores the use of correlation across features extracted from different sensing channels to help in urban situational understanding. We use real-world datasets to show how such correlation can improve the accuracy of detection of city-wide events by combining metadata analysis with image analysis of Instagram content. We demonstrate this through a case study on the Singapore Haze. We show that simple ontological relationships and reasoning can significantly help in automating such correlation-based understanding of transient urban events.
A Study On Singapore Haze, Bingtian Dai, Kasthuri Jayarajah, Ee-Peng Lim, Archan Misra, Shriguru Nayak
A Study On Singapore Haze, Bingtian Dai, Kasthuri Jayarajah, Ee-Peng Lim, Archan Misra, Shriguru Nayak
Research Collection School Of Computing and Information Systems
In 2015, Singaporean have experienced one of the worse air pollution crises in history. With datasets from a well-known photo sharing social network, we analyze how this haze affects Singaporean's daily life. We will share our preliminary results in this paper.