Anjum, Usman
(2022)
LOCALIZATION OF EVENTS USING UNDERDEVELOPED MICROBLOGGING DATA.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Event localization is the task of finding the location of an event. Events are defined as significant one-time occurrences that show notable deviation from expected or normal behavior. Event localization has been studied in many domains including medical data, internet-of-things (IoT), sensor data, and microblogging/social media domain.
In this dissertation, we focus on event localization in the microblogging domain. The data in the microblogging presents a unique challenge in that it is underdeveloped. Underdeveloped data has low reliability and sporadic delivery slate. Since, microblogging data is underdeveloped it provides subjective and incomplete information, which is unsuitable for event localization. We propose enrichment methods for underdeveloped data that would make the data more suitable for event localization. Our enrichment methods include disaggregation, semantic filtering, and data generation using top-down and bottom-up approaches. Once the data is enriched, we identify event signatures that are specific to an event. We find both explicit and latent event signatures within the enriched data. Using these signatures an event can be efficiently localized. We use generated data and data collected from Twitter to test our enrichment methods and implement event localization strategies.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
17 January 2022 |
| Date Type: |
Publication |
| Defense Date: |
6 August 2021 |
| Approval Date: |
17 January 2022 |
| Submission Date: |
11 October 2021 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
117 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Information Sciences > Telecommunications |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Event Localization, Event Detection, Datafusion, Disaggregation, AI, Agent-Based Modelling, Data Augmentation |
| Date Deposited: |
17 Jan 2022 14:59 |
| Last Modified: |
17 Jan 2022 14:59 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/41846 |
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