research-article

Profession-Based Person Search in Microblogs: Using Seed Sets to Find Journalists

Authors:

Mossaab Bagdouri,

Douglas W. OardAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 593 - 602

https://doi.org/10.1145/2806416.2806466

Published: 17 October 2015 Publication History

Abstract

We introduce the problem of searching for professionals in microblogging platforms. We describe a study of how a group of professional journalists with some common characteristics (e.g., works in a specific language, belongs to certain region, or specializes in a particular media) can be found. Starting from seed sets of different sizes, social network features and profile content features are used to find additional journalists. The results show that combining the social network features of the reciprocated mentions and a bidirectional friend/follower graph provides a signal stronger than either of them taken independently, that both social network and profile content features are useful, and that profile content features are able to find larger numbers of less prominent journalists. We apply our methods to find the Twitter accounts of British and Arab journalists.

References

[1]

M. Bagdouri, W. Webber, D. D. Lewis, and D. W. Oard. Towards minimizing the annotation cost of certified text classification. In CIKM, 2013.

Digital Library

[2]

S. Bergsma, M. Dredze, B. V. Durme, T. Wilson, and D. Yarowsky. Broadly improving user classification via communication-based name and location clustering on Twitter. In NAACL, 2013.

[3]

S. Bergsma, P. McNamee, M. Bagdouri, C. Fink, and T. Wilson. Language identification for creating language-specific Twitter collections. In LSM, 2012.

Digital Library

[4]

P. Bhattacharya, S. Ghosh, J. Kulshrestha, M. Mondal, M. B. Zafar, N. Ganguly, and K. P. Gummadi. Deep Twitter diving: Exploring topical groups in microblogs at scale. In CSCW, 2014.

Digital Library

[5]

S. Bowman and C. Willis. We media: How audiences are shaping the future of news and information. hypergene.net/wemedia, 2003. Accessed: 2015-01-19.

[6]

C. Buckley and E. M. Voorhees. Retrieval evaluation with incomplete information. In SIGIR, 2004.

Digital Library

[7]

Z. Cheng, J. Caverlee, H. Barthwal, and V. Bachani. Who is the barbecue king of Texas?: A geo-spatial approach to finding local experts on Twitter. In SIGIR, 2014.

Digital Library

[8]

R. Cohen and D. Ruths. Classifying political orientation on Twitter: It's not easy! In ICWSM, 2013.

[9]

W. B. Croft and D. J. Harper. Using probabilistic models of document retrieval without relevance information. JDoc, 35(4), 1979.

[10]

D. Dailey and K. Starbird. Journalists as crowdsourcerers: Responding to crisis by reporting with a crowd. CSCW, 23(4--6), 2014.

Digital Library

[11]

T. El-Ganainy, W. Magdy, and A. Rafea. Hyperlink-extended pseudo relevance feedback for improved microblog retrieval. In SoMeRA, 2014.

Digital Library

[12]

C. Fink, J. Kopecky, and M. Morawski. Inferring gender from the content of tweets: A region specific example. In ICWSM, 2012.

[13]

G. P. C. Fung, J. X. Yu, H. Lu, and P. S. Yu. Text classification without negative examples revisit. TKDE, 18(1), 2006.

Digital Library

[14]

S. Ghosh, M. B. Zafar, P. Bhattacharya, N. Sharma, N. Ganguly, and K. Gummadi. On sampling the wisdom of crowds: Random vs. expert sampling of the Twitter stream. In CIKM, 2013.

Digital Library

[15]

B. Han, P. Cook, and T. Baldwin. Text-based Twitter user geolocation prediction. JAIR, 2014.

Digital Library

[16]

S. Han, D. He, J. Jiang, and Z. Yue. Supporting exploratory people search: A study of factor transparency and user control. In CIKM, 2013.

Digital Library

[17]

M. Hu, S. Liu, F. Wei, Y. Wu, J. Stasko, and K.-L. Ma. Breaking news on Twitter. In CHI, 2012.

Digital Library

[18]

W. Huang, I. Weber, and S. Vieweg. Inferring nationalities of Twitter users and studying international linking. In HT, 2014.

Digital Library

[19]

J.-H. Kang and K. Lerman. Using lists to measure homophily on Twitter. In ITWP, 2012.

[20]

B. Liu, W. S. Lee, P. S. Yu, and X. Li. Partially supervised classification of text documents. In ICML, 2002.

Digital Library

[21]

W. Meng, L. Lanfen, W. Jing, Y. Penghua, L. Jiaolong, and X. Fei. Improving short text classification using public search engines. In IUKM, volume 8032. Springer, 2013.

Digital Library

[22]

T. Miyanishi, K. Seki, and K. Uehara. Improving pseudo-relevance feedback via tweet selection. In CIKM, 2013.

Digital Library

[23]

E. Mohammady and A. Culotta. Using county demographics to infer attributes of Twitter users. In Joint Workshop on Social Dynamics and Personal Attributes in Social Media, 2014.

[24]

F. Mordelet and J. P. Vert. A bagging SVM to learn from positive and unlabeled examples. PRL, 2014.

Digital Library

[25]

D. Nguyen, R. Gravel, D. Trieschnigg, and T. Meder. "How old do you think I am?" a study of language and age in Twitter. In ICWSM, 2013.

[26]

J. H. Parmelee. Political journalists and Twitter: Influences on norms and practices. JMP, 14(4), 2013.

[27]

M. Pennacchiotti and A.-M. Popescu. A machine learning approach to Twitter user classification. In ICWSM, 2011.

[28]

S. Phuvipadawat and T. Murata. Breaking news detection and tracking in Twitter. In WI-IAT, 2010.

Digital Library

[29]

Q. Qiu, Y. Zhang, J. Zhu, and W. Qu. Building a text classifier by a keyword and Wikipedia knowledge. In ADMA, 2009.

Digital Library

[30]

D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta. Classifying latent user attributes in Twitter. In SMUC, 2010.

Digital Library

[31]

K. Sadamitsu, K. Saito, K. Imamura, and G. Kikui. Entity set expansion using topic information. In HLT, 2011.

Digital Library

[32]

T. Sakai. Alternatives to bpref. In SIGIR, 2007.

Digital Library

[33]

E. Zak. 4 questions with Liz Heron, the New York Times social media editor. adweek.com/fishbowlny/-/250679, 2012. Accessed: 2015-01-13.

Cited By

Ermakova LNurbakova DOvchinnikova I(2021)Analysis of Users Engaged in Online Discussions about Controversial Covid-19 TreatmentsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3462239(162-166)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3462239
Varol OUluturk I(2019)Journalists on Twitter: self-branding, audiences, and involvement of botsJournal of Computational Social Science10.1007/s42001-019-00056-63:1(83-101)Online publication date: 25-Sep-2019
https://doi.org/10.1007/s42001-019-00056-6
Wihbey JJoseph KLazer D(2018)The social silos of journalism? Twitter, news media and partisan segregationNew Media & Society10.1177/146144481880713321:4(815-835)Online publication date: 25-Oct-2018
https://doi.org/10.1177/1461444818807133
Show More Cited By

Index Terms

Profession-Based Person Search in Microblogs: Using Seed Sets to Find Journalists
1. Information systems
  1. Information retrieval

Recommendations

Identifying fact-checkable microblogs during disasters: a classification-ranking approach
ICDCN '19: Proceedings of the 20th International Conference on Distributed Computing and Networking

Microblogging sites are increasingly playing an important role in real-time disaster management. However, rumors and fake news often spread on such platforms, which if not detected, can derail the rescue operations. Therefore, it becomes imperative to ...
Summarizing the differences from microblogs
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

With the rapid growth of social media websites, microblogging has become a popular way to spread instant news and events. Due to the dynamic and social nature of microblogs, extracting useful information from microblogs is more challenging than from the ...
Using Arabic Microblogs Features in Determining Credibility
ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

The increased usage of Twitter as a medium for reporting news and sharing information between people has caught the attention of researchers from different disciplines. One of the research directions is the analysis of online information from the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

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 the author(s) 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: 17 October 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Qatar National Research Fund

Conference

CIKM'15

Sponsor:

CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

Acceptance Rates

CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
256
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 31 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ermakova LNurbakova DOvchinnikova I(2021)Analysis of Users Engaged in Online Discussions about Controversial Covid-19 TreatmentsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3462239(162-166)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3462239
Varol OUluturk I(2019)Journalists on Twitter: self-branding, audiences, and involvement of botsJournal of Computational Social Science10.1007/s42001-019-00056-63:1(83-101)Online publication date: 25-Sep-2019
https://doi.org/10.1007/s42001-019-00056-6
Wihbey JJoseph KLazer D(2018)The social silos of journalism? Twitter, news media and partisan segregationNew Media & Society10.1177/146144481880713321:4(815-835)Online publication date: 25-Oct-2018
https://doi.org/10.1177/1461444818807133
Su TFang AMcCreadie RMacdonald COunis I(2018)On Refining Twitter Lists as Ground Truth Data for Multi-community User ClassificationAdvances in Information Retrieval10.1007/978-3-319-76941-7_74(765-772)Online publication date: 1-Mar-2018
https://doi.org/10.1007/978-3-319-76941-7_74
Liu LYu SWei XNing Z(2017)An improved Apriori–based algorithm for friends recommendation in microblogInternational Journal of Communication Systems10.1002/dac.345331:2Online publication date: 6-Nov-2017
https://doi.org/10.1002/dac.3453
Belobordov ABraslavski P(2016)Does everybody lie? characterizing answerers in health-related CQA2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)10.1109/FRUCT.2016.7584763(1-6)Online publication date: 4-Sep-2016
https://doi.org/10.1109/FRUCT.2016.7584763

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten