skip to main content
10.1145/2492517.2492621acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

The social media genome: modeling individual topic-specific behavior in social media

Published: 25 August 2013 Publication History

Abstract

Information propagation in social media depends not only on the static follower structure but also on the topic-specific user behavior. Hence novel models incorporating dynamic user behavior are needed. To this end, we propose a model for individual social media users, termed a genotype. The genotype is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We demonstrate that user genotypes remain invariant within a topic by adopting them for classification of new information spread in large-scale real networks. Furthermore, we extract topic-specific influence backbone structures based on information adoption and show that they differ significantly from the static follower network. When employed for influence prediction of new content spread, our genotype model and influence backbones enable more than 20% improvement, compared to purely structural features. We also demonstrate that knowledge of user genotypes and influence backbones allow for the design of effective strategies for latency minimization of topic-specific information spread.

References

[1]
O. Tsur and A. Rappoport, "What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities," in WSDM, 2012, pp. 643--652.
[2]
D. Kempe, J. Kleinberg, and E. Tardos, "Maximizing the spread of influence through a social network," in SIGKDD, 2003, pp. 137--146.
[3]
M. Kimura, K. Saito, R. Nakano, and H. Motoda, "Extracting influential nodes on a social network for information diffusion," DMKD, 2010.
[4]
H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, a social network or a news media?" in WWW, 2010, pp. 591--600.
[5]
D. M. Romero, B. Meeder, and J. Kleinberg, "Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter," in WWW, 2011.
[6]
N. Friedkin, A Structural Theory of Social Influence. Cambridge University Press, 2006, vol. 13.
[7]
M. E. J. Newman, "The structure and function of complex networks," SIAM Review, vol. 45, pp. 167--256, 2003.
[8]
P. Dodds, K. Harris, I. Kloumann, C. Bliss, and C. Danforth, "Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter," PLoS One, vol. 6, no. 12, p. e26752, 2011.
[9]
J. Yang and J. Leskovec, "Patterns of temporal variation in online media," in WSDM, 2011, pp. 177--186.
[10]
R. Bandari, S. Asur, and B. A. Huberman, "The pulse of news in social media: Forecasting popularity," in ICWSM, 2012.
[11]
J. Weng, E.-P. Lim, J. Jiang, and Q. He, "Twitterrank: finding topic-sensitive influential Twitterers," in WSDM, 2010, pp. 261--270.
[12]
C. X. Lin, Q. Mei, Y. Jiang, J. Han, and S. Qi, "Inferring the diffusion and evolution of topics in social communities," SNMA, 2011.
[13]
B. Suh, L. Hong, P. Pirolli, and E. Chi, "Want to be retweeted? large scale analytics on factors impacting retweet in Twitter network," in SocialCom, 2010, pp. 177--184.
[14]
D. Ramage, S. Dumais, and D. Liebling, "Characterizing microblogs with topic models," in ICWSM 2010, vol. 5, no. 4, 2010, pp. 130--137.
[15]
M. Gomez Rodriguez, J. Leskovec, and B. Schölkopf, "Structure and dynamics of information pathways in online media," in WSDM, 2013.
[16]
E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, "Everyone's an influencer: quantifying influence on Twitter," in WSDM, 2011.
[17]
A. Pal and S. Counts, "Identifying topical authorities in microblogs," in WSDM, 2011, pp. 45--54.
[18]
P. Bogdanov, M. Busch, J. Moehlis, A. K. Singh, and B. K. Szymanski, "The social media genome: Modeling individual topic-specific behavior in social media, (supplemental material), http://cs.ucsb.edu/%7edbl/papers/genomeappendix.pdf," 2013.
[19]
R. Ribiero, "25 small-business Twitter hashtags to follow," http://www.biztechmagazine.com/article/2012/06/25-small-business-twitter-hashtags-follow, 2012.
[20]
A. K. McCallum, "MALLET: A machine learning for language toolkit," http://mallet.cs.umass.edu, 2002.
[21]
S. Brin and L. Page, "The anatomy of a large-scale hypertextual web search engine," Computer networks and ISDN systems, vol. 30, no. 1, pp. 107--117, 1998.

Cited By

View all
  • (2023)Dijital Flörtleşme: Post-Dijital Sorunlara Post-Dijital ÇözümlerDigital Flirting: Post-Digital Solutions to Post-Digital Problemsİstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi10.17336/igusbd.98436910:1(447-465)Online publication date: 31-Mar-2023
  • (2022)Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-EncodersProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532072(1728-1738)Online publication date: 6-Jul-2022
  • (2022)Modeling Online User Relationships: A Review2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)10.1109/CTISC54888.2022.9849818(1-5)Online publication date: 22-Apr-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2013
1558 pages
ISBN:9781450322409
DOI:10.1145/2492517
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2013

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Funding Sources

Conference

ASONAM '13
Sponsor:
ASONAM '13: Advances in Social Networks Analysis and Mining 2013
August 25 - 28, 2013
Ontario, Niagara, Canada

Acceptance Rates

Overall Acceptance Rate 116 of 549 submissions, 21%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)21
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Dijital Flörtleşme: Post-Dijital Sorunlara Post-Dijital ÇözümlerDigital Flirting: Post-Digital Solutions to Post-Digital Problemsİstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi10.17336/igusbd.98436910:1(447-465)Online publication date: 31-Mar-2023
  • (2022)Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-EncodersProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532072(1728-1738)Online publication date: 6-Jul-2022
  • (2022)Modeling Online User Relationships: A Review2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)10.1109/CTISC54888.2022.9849818(1-5)Online publication date: 22-Apr-2022
  • (2021)Characterizing Topics in Social Media Using Dynamics of ConversationEntropy10.3390/e2312164223:12(1642)Online publication date: 7-Dec-2021
  • (2021)Attribute Driven Temporal Active Online Community SearchIEEE Access10.1109/ACCESS.2021.30933689(93976-93989)Online publication date: 2021
  • (2021)Classification of Social Media Users Based on Temporal Behaviors and InterestsCommunication and Intelligent Systems10.1007/978-981-16-1089-9_72(935-944)Online publication date: 29-Jun-2021
  • (2021)Modeling Topic Specific Credibility in Twitter Based on Structural and Attribute PropertiesHybrid Intelligent Systems10.1007/978-3-030-73050-5_57(580-589)Online publication date: 17-Apr-2021
  • (2020)Attribute driven temporal active local online community detectionProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381442(619-622)Online publication date: 7-Dec-2020
  • (2020)Query-Oriented Temporal Active Intimate Community SearchDatabases Theory and Applications10.1007/978-3-030-39469-1_17(206-215)Online publication date: 21-Jan-2020
  • (2019)Topical Alignment in Online Social SystemsFrontiers in Physics10.3389/fphy.2019.000587Online publication date: 17-Apr-2019
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media