Dickinson, Thomas; Fernández, Miriam; Thomas, Lisa A.; Mulholland, Paul; Briggs, Pam and Alani, Harith
(2015).
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| URL: | http://dl.acm.org/citation.cfm?id=2815845 |
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| DOI (Digital Object Identifier) Link: | https://doi.org/10.1145/2815833.2815845 |
| Google Scholar: | Look up in Google Scholar |
Abstract
Social media is a common place for people to post and share digital reflections of their life events, including major events such as getting married, having children, graduating, etc. Although the creation of such posts is straightforward, the identification of events on online media remains a challenge. Much research in recent years focused on extracting major events from Twitter, such as earthquakes, storms, and floods. This paper however, targets the automatic detection of personal life events, focusing on five events that psychologists found to be the most prominent in people lives. We define a variety of features (user, content, semantic and interaction) to capture the characteristics of those life events and present the results of several classification methods to automatically identify these events in Twitter. Our proposed classification methods obtain results between 0.84 and 0.92 F1-measure for the different types of life events. A novel contribution of this work also lies in a new corpus of tweets, which has been annotated by using crowdsourcing and that constitutes, to the best of our knowledge, the first publicly available dataset for the automatic identification of personal life events from Twitter.
| Item Type: | Conference or Workshop Item |
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| ISBN: | 1-4503-3849-6, 978-1-4503-3849-3 |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) Centre for Policing Research and Learning (CPRL) |
| Item ID: | 46129 |
| Depositing User: | Tom Dickinson |
| Date Deposited: | 21 Apr 2016 09:06 |
| Last Modified: | 05 Oct 2016 10:16 |
| URI: | http://oro.open.ac.uk/id/eprint/46129 |
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