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Enhancement of Twitter event detection using news streams

Published online by Cambridge University Press:  24 January 2022

Samaneh Karimi ,
Azadeh Shakery  and
Rakesh M. Verma
Samaneh Karimi
Affiliation:
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box: 14395-515, Tehran, Iran Computer Science Department, University of Houston, Houston, TX 77204-5008, USA
Azadeh Shakery*
Affiliation:
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box: 14395-515, Tehran, Iran Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran
Rakesh M. Verma
Affiliation:
Computer Science Department, University of Houston, Houston, TX 77204-5008, USA
*
*Corresponding author. E-mail: shakery@ut.ac.ir
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Abstract

A new framework for improving event detection is proposed that employs joint information in news media content and social networks, such as Twitter, to leverage detailed coverage of news media and the timeliness of social media. Specifically, a short text clustering method is employed to detect events from tweets, then the language model representations of the detected events are expanded using another set of events obtained from news articles published simultaneously. The expanded representations of events are employed as a new initialization of the clustering method to run another iteration and consequently enhance the event detection results. The proposed framework is evaluated using two datasets: a tweet dataset with event labels and a news dataset containing news articles published during the same time interval as the tweets. Experimental results show that the proposed framework improves the event detection results in terms of F1 measure compared to the results obtained from tweets only.

Keywords

News event detectionLanguage modelBipartite graph matchingShort text clustering

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Type
Article
Information
Natural Language Engineering , Volume 29 , Issue 2 , March 2023 , pp. 181 - 200
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© The Author(s), 2022. Published by Cambridge University Press

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