The meme ranking problem: Maximizing microblogging virality

Dino Ienco, Francesco Bonchi, Carlos Castillo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Citations (Scopus)

Abstract

Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a "meme" of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough a social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages328-335
Number of pages8
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: 14 Dec 201017 Dec 2010

Other

Other10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
CountryAustralia
CitySydney, NSW
Period14/12/1017/12/10

Fingerprint

Communication

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ienco, D., Bonchi, F., & Castillo, C. (2010). The meme ranking problem: Maximizing microblogging virality. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 328-335). [5693317] https://doi.org/10.1109/ICDMW.2010.127

The meme ranking problem : Maximizing microblogging virality. / Ienco, Dino; Bonchi, Francesco; Castillo, Carlos.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. p. 328-335 5693317.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ienco, D, Bonchi, F & Castillo, C 2010, The meme ranking problem: Maximizing microblogging virality. in Proceedings - IEEE International Conference on Data Mining, ICDM., 5693317, pp. 328-335, 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010, Sydney, NSW, Australia, 14/12/10. https://doi.org/10.1109/ICDMW.2010.127
Ienco D, Bonchi F, Castillo C. The meme ranking problem: Maximizing microblogging virality. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. p. 328-335. 5693317 https://doi.org/10.1109/ICDMW.2010.127
Ienco, Dino ; Bonchi, Francesco ; Castillo, Carlos. / The meme ranking problem : Maximizing microblogging virality. Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. pp. 328-335
@inproceedings{7c65eadbd20d404aaac1a72424280530,
title = "The meme ranking problem: Maximizing microblogging virality",
abstract = "Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a {"}meme{"} of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough a social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.",
author = "Dino Ienco and Francesco Bonchi and Carlos Castillo",
year = "2010",
month = "12",
day = "1",
doi = "10.1109/ICDMW.2010.127",
language = "English",
isbn = "9780769542577",
pages = "328--335",
booktitle = "Proceedings - IEEE International Conference on Data Mining, ICDM",

}

TY - GEN

T1 - The meme ranking problem

T2 - Maximizing microblogging virality

AU - Ienco, Dino

AU - Bonchi, Francesco

AU - Castillo, Carlos

PY - 2010/12/1

Y1 - 2010/12/1

N2 - Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a "meme" of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough a social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.

AB - Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a "meme" of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough a social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.

UR - http://www.scopus.com/inward/record.url?scp=79951737598&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79951737598&partnerID=8YFLogxK

U2 - 10.1109/ICDMW.2010.127

DO - 10.1109/ICDMW.2010.127

M3 - Conference contribution

AN - SCOPUS:79951737598

SN - 9780769542577

SP - 328

EP - 335

BT - Proceedings - IEEE International Conference on Data Mining, ICDM

ER -