Knowledge-Based Systems, Volume (148), Year (2018-2) , Pages (1-16)

Title : ( Detecting life events from twitter based on temporal semantic features )

Authors: Maryam Khodabakhsh , Mohsen Kahani , Zeinab Noorian ,

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Abstract

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.

Keywords

, Social web, Social networks, Event detection, Personal life events
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@article{paperid:1067523,
author = {Khodabakhsh, Maryam and Kahani, Mohsen and Zeinab Noorian},
title = {Detecting life events from twitter based on temporal semantic features},
journal = {Knowledge-Based Systems},
year = {2018},
volume = {148},
month = {February},
issn = {0950-7051},
pages = {1--16},
numpages = {15},
keywords = {Social web; Social networks; Event detection; Personal life events},
}

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%0 Journal Article
%T Detecting life events from twitter based on temporal semantic features
%A Khodabakhsh, Maryam
%A Kahani, Mohsen
%A Zeinab Noorian
%J Knowledge-Based Systems
%@ 0950-7051
%D 2018

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