Detecting life events from twitter based on temporal semantic features

The wide adoption of social networking and microblogging platforms by a large number of users across the globe has provided a rich source of unstructured information for understanding users’ behaviors, interests and opinions at both micro and macro levels. An active area in this space is the detecti...

Full description

Saved in:
Bibliographic Details
Published in:Knowledge-based systems 2018-05, Vol.148, p.1-16
Main Authors: Khodabakhsh, Maryam, Kahani, Mohsen, Bagheri, Ebrahim, Noorian, Zeinab
Format: Article
Language:English
Subjects:
Blogs
Event detection
Personal life events
Semantics
Social networks
Social web
Technology adoption
Unstructured data
User behavior
User generated content
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The wide adoption of social networking and microblogging platforms by a large number of users across the globe has provided a rich source of unstructured information for understanding users’ behaviors, interests and opinions at both micro and macro levels. An active area in this space is the detection of important real-world events from user-generated social content. The works in this area identify instances of events that impact a large number of users. However, a more nuanced form of an event, known as life event, is also of high importance, which in contrast to real-world events, does not impact a large number of users and is limited to at most a few people. For this reason, life events, such as marriage, travel, and career change, among others, are more difficult to detect for several reasons: i) they are specific to a given user and do not have a wider reaching reflection; ii) they are often not reported directly and need to be inferred from the content posted by individual users; and iii) many users do not report their life events on social platforms, making the problem highly class-imbalanced. In this paper, we propose a semantic approach based on word embedding techniques to model life events. We then use word mover’s distance to measure the similarity of a given tweet to different types of life events, which are used as input features for a multi-class classifier. Furthermore, we show that when a sequence of tweets that have appeared before and after a given tweet of interest (temporal stacking) are considered, the performance of the life event detection task improves significantly.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.02.021