Local variation of collective attention in hashtag spike trains

Céyda Sanli Cakir; Renaud Lambiotte

Local variation of collective attention in hashtag spike trains

Céyda Sanli Cakir, Renaud Lambiotte

Namur Institute for Complex Systems

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

26 Downloads (Pure)

Abstract

In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.

Original language	English
Title of host publication	AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media
Subtitle of host publication	Papers from the 2015 ICWSM Workshop
Publisher	AI Access Foundation
Pages	8-12
Number of pages	5
Volume	WS-15-17
ISBN (Print)	9781577357353
Publication status	Published - 22 Apr 2015
Event	9th International Conference on Web and Social Media, ICWSM 2015 - Oxford, United Kingdom Duration: 26 May 2015 → 29 May 2015

Conference

Conference	9th International Conference on Web and Social Media, ICWSM 2015
Country/Territory	United Kingdom
City	Oxford
Period	26/05/15 → 29/05/15

Keywords

Twitter
data mining
online social media
behavior
statistical signal processing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

SanliAccepted author manuscript, 3.73 KB

Cite this

@inproceedings{d039ddf0d14e46cca0ba2c85f6144b51,

title = "Local variation of collective attention in hashtag spike trains",

abstract = "In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.",

keywords = "Twitter, data mining, online social media, behavior, statistical signal processing",

author = "{Sanli Cakir}, C{\'e}yda and Renaud Lambiotte",

year = "2015",

month = apr,

day = "22",

language = "English",

isbn = "9781577357353",

volume = "WS-15-17",

pages = "8--12",

booktitle = "AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media",

publisher = "AI Access Foundation",

note = "9th International Conference on Web and Social Media, ICWSM 2015 ; Conference date: 26-05-2015 Through 29-05-2015",

}

Sanli Cakir, C & Lambiotte, R 2015, Local variation of collective attention in hashtag spike trains. in AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media: Papers from the 2015 ICWSM Workshop. vol. WS-15-17, AI Access Foundation, pp. 8-12, 9th International Conference on Web and Social Media, ICWSM 2015, Oxford, United Kingdom, 26/05/15.

Local variation of collective attention in hashtag spike trains. / Sanli Cakir, Céyda; Lambiotte, Renaud.
AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media: Papers from the 2015 ICWSM Workshop. Vol. WS-15-17 AI Access Foundation, 2015. p. 8-12.

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Local variation of collective attention in hashtag spike trains

AU - Sanli Cakir, Céyda

AU - Lambiotte, Renaud

PY - 2015/4/22

Y1 - 2015/4/22

N2 - In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.

AB - In this paper, we propose a methodology quantifying temporal patterns of nonlinear hashtag time series. Our approach is based on an analogy between neuron spikes and hashtag diffusion. We adopt the local variation, originally developed to analyze local time delays in neuron spike trains. We show that the local variation successfully characterizes nonlinear features of hashtag spike trains such as burstiness and regularity. We apply this understanding in an extreme social event and are able to observe temporal evaluation of online collective attention of Twitter users to that event.

KW - Twitter

KW - data mining

KW - online social media

KW - behavior

KW - statistical signal processing

UR - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10631

M3 - Conference contribution

AN - SCOPUS:84964659873

SN - 9781577357353

VL - WS-15-17

SP - 8

EP - 12

BT - AAAI Workshop - Technical Report: Ninth International AAAI Conference on Web and Social Media

PB - AI Access Foundation

T2 - 9th International Conference on Web and Social Media, ICWSM 2015

Y2 - 26 May 2015 through 29 May 2015

ER -

Local variation of collective attention in hashtag spike trains

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Local Variation of Hashtag Spike Trains and Popularity in Twitter

Cite this