Identifying important life events from Twitter using semantic and syntactic patterns

Thomas Dickinson, Miriam Fernandez, Lisa A. Thomas, Paul Mulholland, Pam Briggs, Harith Alani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


Identifying global events from social media has been the focus of much research in recent years. However, the identification of personal life events poses new requirements and challenges that have received relatively little research attention. In this paper we explore a new approach for life event identification, where we expand social media posts into both semantic, and syntactic networks of content. Frequent graph patterns are mined from these networks and used as features to enrich life-event classifiers. Results show that our approach significantly outperforms the best performing baseline in accuracy (by 4.48% points) and F-measure (by 4.54% points) when used to identify five major life events identified from the psychology literature: Getting Married, Having Children, Death of a Parent, Starting School, and Falling in Love. In addition, our results show that, while semantic graphs are effective at discriminating the theme of the post (e.g. the topic of marriage), syntactic graphs help identify whether the post describes a personal event (e.g. someone getting married).

Original languageEnglish
Title of host publicationProceedings of the 15th International Conference WWW/Internet 2016
EditorsLuis Rodrigues, Pedro Isaias
PublisherInternational Association for the Development of the Information Society
Number of pages8
ISBN (Electronic)9789898533579
Publication statusPublished - 1 Jan 2016
Event15th International Conference WWW/Internet 2016 - Mannheim, Germany
Duration: 28 Oct 201630 Oct 2016

Publication series

NameProceedings of the 15th International Conference WWW/Internet 2016


Conference15th International Conference WWW/Internet 2016


Dive into the research topics of 'Identifying important life events from Twitter using semantic and syntactic patterns'. Together they form a unique fingerprint.

Cite this