Tan, Leonard
(2020)
Event prediction through structural intelligence in online social networks.
[Thesis (PhD/Research)]
Abstract
The Internet today is a platform of information exchange between real people across the globe. Event prediction is an emerging and highly complex topic of interest which enjoys wide ranging applications in fintech, medical, security, etc. Some of these implementations include time sequenced methods, pattern recognition techniques, multiple instance learning, topic based approach, etc. While they have been adequate at handling predictions of events from past discrete occurances, they fall short of the capability to predict events from a continuous stream of social information exchange.
Furthermore, many of these approaches lack the presentative power of describing and tracking events through time. Relational flux and turbulence in Online Social Networks (OSNs) can be defined as the complex evolution of social communication patterns staged over important topic contexts which have the potential to cause abberations of relational states. They play very important roles in determining tasks like recognition, prediction, detection, etc. across applications like recommendation, clustering, community, privacy, security, knowledge representation, etc. For example, an essential research question for Knowledge Representation Learning (KRL) is how to explictly embed implict real-life relational states between entities structured in a Knowledge Graph (KG).
Most current studies today however, do not have the capability to effectively generalize relationships across heterogeneous architectures. Indeed, an important challenge to address is that latent communication patterns in local and global contexts of social opinions cannot be fully captured. Thus, event prediction is challenging for two reasons: its generalized, temporal, evolving nature and drifting contexts. In addition, many current approaches however, lack the capacity of describing and tracking general events over time. To tackle these issues, this study develops a novel RFT model which leverages on the mechanics of Relational Flux and Turbulence to model dynamic communicative behaviors between actors within social networks. To the best knowledge offered by existing literature, there has not been a similar model and / or method of approach which effectively predicts events from a computationally cognitive perspective.
To surmise the milestones achieved by this research endeavour, extensive experiments were conducted on large-scale datasets from Twitter, Googlefeed and Livejournal. From the experimental results, it was shown that RFT is able to identify and predict relational turbulence in a social flux which mirrors real life relational state transitions in a social topic context. The following demonstration from the F1-scores and k-fold cross validation results proves that the model performs comparably better by more than 10% to well-known predictors such as the Hybrid Probabilistic Markovian (HPM)
predictive method [1] and other state-of-the-art baselines in predicting events. Importantly, this research development proves that event prediction methods which account for relational features between actors of social networks perform much better than conventional mainstream approaches like vector regression, random walk, markovian logic networks, etc. that are widely used today.
| Item Type: |
Thesis (PhD/Research)
|
| Item Status: |
Live Archive |
| Additional Information: |
Doctor of Philosophy (PhD) thesis. |
| Faculty/School / Institute/Centre: |
Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021) |
| Faculty/School / Institute/Centre: |
Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021) |
| Supervisors: |
Zhang, Ji; Tao, Xiaohui |
| Date Deposited: |
12 Oct 2020 01:55 |
| Last Modified: |
05 Oct 2021 04:40 |
| Uncontrolled Keywords: |
artificial intelligence, knowledge engineering, decision engineering, information behavior, machine learning, evolutionary computing |
| Fields of Research (2008): |
08 Information and Computing Sciences > 0806 Information Systems > 080602 Computer-Human Interaction
08 Information and Computing Sciences > 0807 Library and Information Studies > 080707 Organisation of Information and Knowledge Resources
08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080102 Artificial Life
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
08 Information and Computing Sciences > 0807 Library and Information Studies > 080703 Human Information Behaviour
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0806 Information Systems > 080607 Information Engineering and Theory
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080101 Adaptive Agents and Intelligent Robotics
08 Information and Computing Sciences > 0805 Distributed Computing > 080504 Ubiquitous Computing
08 Information and Computing Sciences > 0807 Library and Information Studies > 080702 Health Informatics
08 Information and Computing Sciences > 0807 Library and Information Studies > 080709 Social and Community Informatics |
| Fields of Research (2020): |
46 INFORMATION AND COMPUTING SCIENCES > 4608 Human-centred computing > 460806 Human-computer interaction
46 INFORMATION AND COMPUTING SCIENCES > 4610 Library and information studies > 461008 Organisation of information and knowledge resources
46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460902 Decision support and group support systems
46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460201 Artificial life and complex adaptive systems
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing
46 INFORMATION AND COMPUTING SCIENCES > 4610 Library and information studies > 461002 Human information behaviour
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460510 Recommender systems
46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460912 Knowledge and information management
46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4608 Human-centred computing > 460809 Pervasive computing
42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems
46 INFORMATION AND COMPUTING SCIENCES > 4610 Library and information studies > 461010 Social and community informatics |
| Identification Number or DOI: |
doi:10.26192/asxq-ax90 |
| URI: |
http://eprints.usq.edu.au/id/eprint/39862 |
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