Detecting important life events on Twitter using frequent semantic and syntatic subgraphs

Thomas Dickinson, Miriam Fernandez, Lisa Thomas, Paul Mulholland, Pamela Briggs, Harith Alani

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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
Pages (from-to)23-37
JournalIADIS International Journal on WWW/INTERNET
Issue number2
Publication statusPublished - 29 Oct 2016
Event15th International Conference on WWW/INTERNET 2016 - Mannheim
Duration: 29 Oct 2016 → …


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