ALRashdi, Reem (2022) Tweet Classification for Crisis Response. PhD thesis, University of York.
Abstract
Tweet classification for crisis response is a text classification task that aims at identifying whether a tweet is related to a specific crisis event or not. Humanitarian organisations that intend to respond to people in need in the early hours of a crisis suffer from monitoring the massive number of tweets posted in real time. Therefore, the main objective of tweet classification models for crisis response is to filter the crisis-related tweets to simplify the work for these organisations. Still, crisis events have different characteristics, which prevents current models trained on past events from generalising in identifying tweets from new disasters, which is infeasible to be manually labelled at the crisis onset. This thesis introduces frameworks under the umbrella of distant supervision and domain adaptation to minimize the gap or maximize the similarities between training and testing data from disaster events. The contributions demonstrate the effectiveness of using automatically labelled training data from past or emerging events in tweet classification tasks for English and Arabic crisis tweets. To this end, we propose an automatically labelling framework that utilises distant supervision via an external knowledge base. Then, we introduce an approach that unifies our framework and adaptation techniques which automatically labels incoming tweets from an emerging incident. This approach can be seen as a robust method to classify unseen English tweets from current events. However, it has its restrictions when applied to tweets from other languages, especially if the language comes with limited resources, different text structures, and different people’s behavior in posting tweets such as Arabic. Hence, we adapt our framework with significant changes to suit Arabic user-generated posts. Our results for both English and Arabic tweets show that our original and adaptive approaches continuously improve the classifier’s performance compared with existing labelling techniques in different adaptation methods.
Metadata
Supervisors: | O'Keefe, Simon |
---|---|
Keywords: | Text classification;Crisis response;Twitter data; Domain adaptation;Distant supervision |
Awarding institution: | University of York |
Academic Units: | The University of York > Computer Science (York) |
Identification Number/EthosID: | uk.bl.ethos.875082 |
Depositing User: | Mrs Reem ALRashdi |
Date Deposited: | 15 Feb 2023 12:06 |
Last Modified: | 21 Apr 2023 09:53 |
Download
Examined Thesis (PDF)
Filename: Reem AlRashdi_Tweet Calssification for Crisis Response.pdf
Description: PhD thesis
Licence:
This work is licensed under a Creative Commons Attribution NonCommercial NoDerivatives 4.0 International License
Export
Statistics
You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.