The Open UniversitySkip to content
 

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

Burel, Gregoire; Saif, Hassan and Alani, Harith (2017). Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media. In: The Semantic Web – ISWC 2017.

Full text available as:
[img]
Preview
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (698kB) | Preview
Google Scholar: Look up in Google Scholar

Abstract

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of
statistical and non-semantic deep learning models.

Item Type: Conference or Workshop Item
Project Funding Details:
Funded Project NameProject IDFunding Body
COMRADESNot SetEC (European Commission): FP(inc.Horizon2020, H2020, ERC)
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 51726
Depositing User: Gregoire Burel
Date Deposited: 20 Oct 2017 15:15
Last Modified: 10 Sep 2018 22:18
URI: http://oro.open.ac.uk/id/eprint/51726
Share this page:

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU