Discovering Social Events through Online Attention

Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy,...

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Main Authors: Kenett, Dror Y., Morstatter, Fred, Stanley, H. Eugene, Liu, Huan
Format: Online
Language:English
Published: Public Library of Science 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4116114/
id pubmed-4116114
recordtype oai_dc
spelling pubmed-41161142014-08-04 Discovering Social Events through Online Attention Kenett, Dror Y. Morstatter, Fred Stanley, H. Eugene Liu, Huan Research Article Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible “thermostats” of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011. Public Library of Science 2014-07-30 /pmc/articles/PMC4116114/ /pubmed/25076410 http://dx.doi.org/10.1371/journal.pone.0102001 Text en © 2014 Kenett et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Kenett, Dror Y.
Morstatter, Fred
Stanley, H. Eugene
Liu, Huan
spellingShingle Kenett, Dror Y.
Morstatter, Fred
Stanley, H. Eugene
Liu, Huan
Discovering Social Events through Online Attention
author_facet Kenett, Dror Y.
Morstatter, Fred
Stanley, H. Eugene
Liu, Huan
author_sort Kenett, Dror Y.
title Discovering Social Events through Online Attention
title_short Discovering Social Events through Online Attention
title_full Discovering Social Events through Online Attention
title_fullStr Discovering Social Events through Online Attention
title_full_unstemmed Discovering Social Events through Online Attention
title_sort discovering social events through online attention
description Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible “thermostats” of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.
publisher Public Library of Science
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4116114/
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