ORCID Profile
0000-0002-4569-0901
Current Organisation
Singapore university of technology and design
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: IEEE
Date: 04-2012
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 09-2012
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 11-12-2018
Publisher: Springer Science and Business Media LLC
Date: 24-06-2021
DOI: 10.1186/S40537-021-00482-2
Abstract: A key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.
Publisher: Elsevier BV
Date: 11-2023
Publisher: ACM
Date: 05-08-2013
Publisher: ACM
Date: 05-08-2013
Publisher: Springer Science and Business Media LLC
Date: 11-04-2022
DOI: 10.1186/S40537-022-00585-4
Abstract: Online social networking services like Twitter are frequently used for discussions on numerous topics of interest, which range from mainstream and popular topics (e.g., music and movies) to niche and specialized topics (e.g., politics). Due to the popularity of such services, it is a challenging task to automatically model and determine the numerous discussion topics given the large amount of tweets. Adding on this complexity is the need to identify these topics with the absence of prior knowledge about both the types and number of topics, while having the requirement of the relevant technical expertise to tune the numerous parameters for the various models. To address this challenge, we develop the Clustering-based Topic Modelling (ClusTop) algorithm that first constructs different types of word networks based on different types of n-grams co-occurrence and word embedding distances. Using these word networks, ClusTop is then able to automatically determine the discussion topics using community detection approaches. In contrast to traditional topic models, ClusTop does not require the tuning or setting of numerous parameters and instead uses community detection approaches to automatically determine the appropriate number of topics. The ClusTop algorithm is also able to capture the syntactic meaning in tweets via the use of bigrams, trigrams, other word combinations and word embedding techniques in constructing the word network graph, and utilizes edge weights based on word embedding. Using three Twitter datasets with labelled crises and events as topics, we show that ClusTop outperforms various traditional baselines in terms of topic coherence, pointwise mutual information, precision, recall and F-score.
Publisher: ACM
Date: 25-06-2012
Publisher: Springer Science and Business Media LLC
Date: 05-05-2018
Publisher: IEEE
Date: 12-2012
No related grants have been discovered for Kwan Hui Lim.