research-article

Aggregating content and network information to curate twitter user lists

Authors:

Derek Greene,

Gavin Sheridan,

Barry Smyth,

Pádraig CunninghamAuthors Info & Claims

RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web

Pages 29 - 36

https://doi.org/10.1145/2365934.2365941

Published: 09 September 2012 Publication History

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Abstract

Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process.

References

[1]

M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi. Measuring user influence in twitter: The million follower fallacy. In Proc. 4th International AAAI Conference on Weblogs and Social Media (ICWSM'10), pages 10--17, 2010.

Crossref

Google Scholar

[2]

M. D. Conover, J. Ratkiewicz, M. Francisco, B. Goncalves, A. Flammini, and F. Menczer. Political polarization on twitter. In Proc. 5th International Conference on Weblogs and Social Media (ICWSM'11). AAAI, 2011.

Google Scholar

[3]

E. Daly, W. Geyer, and D. Millen. The network effects of recommending social connections. In Proc. 4th ACM Conference on Recommender Systems (RecSys'10), pages 301--304, New York, NY, USA, 2010. ACM.

Digital Library

Google Scholar

[4]

J. Hannon, M. Bennett, and B. Smyth. Recommending twitter users to follow using content and collaborative filtering approaches. In Proc. 4th ACM Conference on Recommender Systems (RecSys'10), pages 199--206. ACM, 2010.

Digital Library

Google Scholar

[5]

L. Hubert and P. Arabie. Comparing partitions. Journal of Classification, pages 193--218, 1985.

Google Scholar

[6]

H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In Proc. 19th International Conference on World Wide Web (WWW'10), pages 591--600. ACM, 2010.

Digital Library

Google Scholar

[7]

J. Ratkiewicz, M. Conover, M. Meiss, B. Goncalves, A. Flammini, and F. Menczer. Detecting and tracking political abuse in social media. In Proc. 5th International Conference on Weblogs and Social Media (ICWSM'11). AAAI, 2011.

Google Scholar

[8]

D. Shamma, L. Kennedy, and E. Churchill. Tweet the debates: Understanding community annotation of uncollected sources. In Proc. 1st SIGMM Workshop on Social media, pages 3--10. ACM, 2009.

Digital Library

Google Scholar

[9]

J. Weng, E. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In Proc. 3rd ACM International Conference on Web search and data mining, pages 261--270. ACM, 2010.

Digital Library

Google Scholar

[10]

H. White and C. Griffith. Author Cocitation: A Literature Measure of Intellectual Structure. Journal of the American Society for Information Science, 32(3):163--171, 1981.

Crossref

Google Scholar

[11]

G. Wu, D. Greene, and P. Cunningham. Merging multiple criteria to identify suspicious reviews. In Proc. 4th ACM Conference on Recommender Systems (RecSys'10), pages 241--244. ACM, 2010.

Digital Library

Google Scholar

Cited By

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Yeşiltaş SŞen AArslan BAltuğ S(2022)A Twitter-Based Economic Policy Uncertainty Index: Expert Opinion and Financial Market Dynamics in an Emerging Market EconomyFrontiers in Physics10.3389/fphy.2022.86420710Online publication date: 30-May-2022
https://doi.org/10.3389/fphy.2022.864207
Kumar TVaidyanathan SAnanthapadmanabhan HParthasarathy SRavindran B(2020)Hypergraph clustering by iteratively reweighted modularity maximizationApplied Network Science10.1007/s41109-020-00300-35:1Online publication date: 20-Aug-2020
https://doi.org/10.1007/s41109-020-00300-3
Kumar TVaidyanathan SAnanthapadmanabhan HParthasarathy SRavindran B(2019)A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective ClusteringComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_24(286-297)Online publication date: 26-Nov-2019
https://doi.org/10.1007/978-3-030-36687-2_24
Show More Cited By

Index Terms

Aggregating content and network information to curate twitter user lists
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

RSWeb '12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web

September 2012

68 pages

ISBN:9781450316385

DOI:10.1145/2365934

Program Chairs:
Bamshad Mobasher
DePaul University, Chicago, USA
,
Dietmar Jannach
TU Dortmund, Germany
,
Werner Geyer
IBM Research, Cambridge, USA
,
Andreas Hotho
Universität Würzburg, Germany

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2012

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Research-article

Conference

RecSys '12

Sponsor:

SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9, 2012

Dublin, Ireland

Acceptance Rates

RSWeb '12 Paper Acceptance Rate 8 of 13 submissions, 62%;

Overall Acceptance Rate 8 of 13 submissions, 62%

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Cited By

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Yeşiltaş SŞen AArslan BAltuğ S(2022)A Twitter-Based Economic Policy Uncertainty Index: Expert Opinion and Financial Market Dynamics in an Emerging Market EconomyFrontiers in Physics10.3389/fphy.2022.86420710Online publication date: 30-May-2022
https://doi.org/10.3389/fphy.2022.864207
Kumar TVaidyanathan SAnanthapadmanabhan HParthasarathy SRavindran B(2020)Hypergraph clustering by iteratively reweighted modularity maximizationApplied Network Science10.1007/s41109-020-00300-35:1Online publication date: 20-Aug-2020
https://doi.org/10.1007/s41109-020-00300-3
Kumar TVaidyanathan SAnanthapadmanabhan HParthasarathy SRavindran B(2019)A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective ClusteringComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_24(286-297)Online publication date: 26-Nov-2019
https://doi.org/10.1007/978-3-030-36687-2_24
Rizos GPapadopoulos SKompatsiaris Y(2017)Multilabel user classification using the community structure of online networksPLOS ONE10.1371/journal.pone.017334712:3(e0173347)Online publication date: 9-Mar-2017
https://doi.org/10.1371/journal.pone.0173347
Thejaswi MVijayaraghavan SDas AThilagam P(2015)Dynamics of Multi-Campaign Propagation in Online Social NetworksProceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201510.1145/2808797.2809338(1571-1572)Online publication date: 25-Aug-2015
https://dl.acm.org/doi/10.1145/2808797.2809338
Kieu BIchise RPham S(2015)Predicting the Popularity of Social CurationKnowledge and Systems Engineering10.1007/978-3-319-11680-8_33(413-424)Online publication date: 2015
https://doi.org/10.1007/978-3-319-11680-8_33
O'Callaghan DPrucha NGreene DConway MCarthy JCunningham PJia YRokne JWu XEster MXu G(2014)Online social media in the Syria conflictProceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3191835.3191918(409-416)Online publication date: 17-Aug-2014
https://dl.acm.org/doi/10.5555/3191835.3191918
KIMURA A(2014)Large-scale cross-media analysis and mining from socially curated contentsProgress in Informatics10.2201/NiiPi.2014.11.4(19)Online publication date: Mar-2014
https://doi.org/10.2201/NiiPi.2014.11.4
O'Callaghan DPrucha NGreene DConway MCarthy JCunningham P(2014)Online social media in the Syria conflict: Encompassing the extremes and the in-betweens2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)10.1109/ASONAM.2014.6921619(409-416)Online publication date: Aug-2014
https://doi.org/10.1109/ASONAM.2014.6921619
Greene DCunningham PDavis HHalpin HPentland ABernstein MAdamic LAlani HMonnin ARogers R(2013)Producing a unified graph representation from multiple social network viewsProceedings of the 5th Annual ACM Web Science Conference10.1145/2464464.2464471(118-121)Online publication date: 2-May-2013
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