Interactional and Informational Attention on Twitter
Márton Karsai
- Fonction : Auteur
- PersonId : 2001
- IdHAL : marton-karsai
- ORCID : 0000-0001-5382-8950
- IdRef : 138471541
Camille Roth
- Fonction : Auteur
- PersonId : 1042429
- IdHAL : camille-roth
- ORCID : 0000-0003-3925-7957
Résumé
Twitter may be considered to be a decentralized social information processing platform whose users constantly receive their followees’ information feeds, which they may in turn dispatch to their followers. This decentralization is not devoid of hierarchy and heterogeneity, both in terms of activity and attention. In particular, we appraise the distribution of attention at the collective and individual level, which exhibits the existence of attentional constraints and focus effects. We observe that most users usually concentrate their attention on a limited core of peers and topics, and discuss the relationship between interactional and informational attention processes—all of which, we suggest, may be useful to refine influence models by enabling the consideration of differential attention likelihood depending on users, their activity levels, and peers’ positions.
Domaines
Méthodes et statistiques Sociologie Informatique Adaptation et Systèmes auto-organisés [nlin.AO] Machine Learning [stat.ML]Format du dépôt | Notice |
---|---|
Type de dépôt | Article dans une revue |
Résumé |
en
Twitter may be considered to be a decentralized social information processing platform whose users constantly receive their followees’ information feeds, which they may in turn dispatch to their followers. This decentralization is not devoid of hierarchy and heterogeneity, both in terms of activity and attention. In particular, we appraise the distribution of attention at the collective and individual level, which exhibits the existence of attentional constraints and focus effects. We observe that most users usually concentrate their attention on a limited core of peers and topics, and discuss the relationship between interactional and informational attention processes—all of which, we suggest, may be useful to refine influence models by enabling the consideration of differential attention likelihood depending on users, their activity levels, and peers’ positions.
|
Titre |
en
Interactional and Informational Attention on Twitter
|
Auteur(s) |
Agathe Baltzer
1
, Márton Karsai
1, 2
, Camille Roth
3, 4
1
DANTE -
Dynamic Networks : Temporal and Structural Capture Approach
( 1079003 )
- 46 Allée d'Italie 69364 Lyon France
- France
2
CEU -
Central European University [Budapest, Hongrie]
( 350563 )
- Nador u. 9
1051 Budapest.
- Hongrie
3
CAMS -
Centre d'Analyse et de Mathématique sociales
( 1318 )
- 54 boulevard Raspail 75006 Paris
- France
4
CMB -
Centre Marc Bloch
( 84774 )
- Friedrichstr. 191 D-10117 Berlin
- Allemagne
|
Langue du document |
Anglais
|
Comité de lecture |
Oui
|
Vulgarisation |
Non
|
Nom de la revue |
|
Page/Identifiant |
1-16
|
Numéro |
8
|
Volume |
10
|
Date de publication |
2019-08
|
Audience |
Internationale
|
Domaine(s) |
|
Projet(s) ANR |
|
Projet(s) Européen(s) |
|
arXiv Id | 1907.07962 |
DOI | 10.3390/info10080250 |
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