Twitter-based analysis of the dynamics of collective attention to political parties

Young-Ho Eom, Michelangelo Puliga, Jasmina Smailović, Igor Mozetič, Guido Caldarelli

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Large-scale data from social media have a significant potential to describe complex phenomena in the real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the election outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.
LanguageEnglish
Article numbere0131184
Pages1-17
Number of pages17
JournalPLoS One
Volume10
Issue number7
DOIs
Publication statusPublished - 10 Jul 2015

Fingerprint

Social Media
Normal distribution
Normal Distribution
Brownian movement
Autocorrelation
Proxy
social networks
Elections
Research Personnel
Log Normal Distribution
Fluctuations
Geometric Brownian Motion
group behavior
Collective Behavior
Time Windows
autocorrelation
Logarithm
dynamic models
stakeholders
Correlate

Keywords

  • social media
  • collective behaviour
  • social trends
  • political parties
  • electrions
  • Twitter

Cite this

Eom, Y-H., Puliga, M., Smailović, J., Mozetič, I., & Caldarelli, G. (2015). Twitter-based analysis of the dynamics of collective attention to political parties. PLoS One, 10(7), 1-17. [e0131184]. https://doi.org/10.1371/journal.pone.0131184
Eom, Young-Ho ; Puliga, Michelangelo ; Smailović, Jasmina ; Mozetič, Igor ; Caldarelli, Guido. / Twitter-based analysis of the dynamics of collective attention to political parties. In: PLoS One. 2015 ; Vol. 10, No. 7. pp. 1-17.
@article{c3cd1a8182964ee5a50b30249e859c60,
title = "Twitter-based analysis of the dynamics of collective attention to political parties",
abstract = "Large-scale data from social media have a significant potential to describe complex phenomena in the real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the election outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.",
keywords = "social media, collective behaviour, social trends, political parties, electrions, Twitter",
author = "Young-Ho Eom and Michelangelo Puliga and Jasmina Smailović and Igor Mozetič and Guido Caldarelli",
year = "2015",
month = "7",
day = "10",
doi = "10.1371/journal.pone.0131184",
language = "English",
volume = "10",
pages = "1--17",
journal = "PLOS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

Eom, Y-H, Puliga, M, Smailović, J, Mozetič, I & Caldarelli, G 2015, 'Twitter-based analysis of the dynamics of collective attention to political parties' PLoS One, vol. 10, no. 7, e0131184, pp. 1-17. https://doi.org/10.1371/journal.pone.0131184

Twitter-based analysis of the dynamics of collective attention to political parties. / Eom, Young-Ho; Puliga, Michelangelo; Smailović, Jasmina; Mozetič, Igor; Caldarelli, Guido.

In: PLoS One, Vol. 10, No. 7, e0131184, 10.07.2015, p. 1-17.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Twitter-based analysis of the dynamics of collective attention to political parties

AU - Eom, Young-Ho

AU - Puliga, Michelangelo

AU - Smailović, Jasmina

AU - Mozetič, Igor

AU - Caldarelli, Guido

PY - 2015/7/10

Y1 - 2015/7/10

N2 - Large-scale data from social media have a significant potential to describe complex phenomena in the real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the election outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.

AB - Large-scale data from social media have a significant potential to describe complex phenomena in the real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the election outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.

KW - social media

KW - collective behaviour

KW - social trends

KW - political parties

KW - electrions

KW - Twitter

UR - http://journals.plos.org/plosone/

U2 - 10.1371/journal.pone.0131184

DO - 10.1371/journal.pone.0131184

M3 - Article

VL - 10

SP - 1

EP - 17

JO - PLOS One

T2 - PLOS One

JF - PLOS One

SN - 1932-6203

IS - 7

M1 - e0131184

ER -