research-article

Recommending targeted strangers from whom to solicit information on social media

Authors:

Michelle X. Zhou,

Nimrod Megiddo,

Jeffrey Nichols,

Clemens DrewsAuthors Info & Claims

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

Pages 37 - 48

https://doi.org/10.1145/2449396.2449403

Published: 19 March 2013 Publication History

Abstract

We present an intelligent, crowd-powered information collection system that automatically identifies and asks targeted strangers on Twitter for desired information (e.g., current wait time at a nightclub). Our work includes three parts. First, we identify a set of features that characterize one's willingness and readiness to respond based on their exhibited social behavior, including the content of their tweets and social interaction patterns. Second, we use the identified features to build a statistical model that predicts one's likelihood to respond to information solicitations. Third, we develop a recommendation algorithm that selects a set of targeted strangers using the probabilities computed by our statistical model with the goal to maximize the over-all response rate. Our experiments, including several in the real world, demonstrate the effectiveness of our work.

References

[1]

WarriorForum - Internet Marketing Forums, http://www.warriorforum.com/

[2]

Slickdeals, http://slickdeals.net/forums/forumdisplay.php?f=19

[3]

Quora, http://www.quora.com/

[4]

Yahoo Answers, http://www.answers.yahoo.com

[5]

Facebook Questions, http://www.facebook.com

[6]

WEKA. http://www.cs.waikato.ac.nz/ml/weka/

[7]

Crowdflower. http://crowdflower.com/

[8]

Avrahami, D. and Hudson, Scott E. 2006. Responsiveness in Instant Messaging: Predictive Models Supporting Inter-Personal Communication, In Proc. CHI 2006.

Digital Library

[9]

Begole, J., Tang, J. C., Smith, R. E., and Yankelovich, N. 2002. Work rhythms: Analyzing visualizations of awareness histories of distributed groups. In Proc. CSCW'02.

Digital Library

[10]

Bouguessa, M. Dumoulin, B., and Wang, S. 2008. Identifying authoritative actions in question-answering forums: the case of Yahoo! answers: In Proc. KDD 2008.

Digital Library

[11]

Boyd, D. Golder, S., Lotan, G. 2010. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. In Proc. HICSS 2010.

Digital Library

[12]

Chakrabarti, D., Punera, K. 2011, Event Summarization using Tweets, In Proc. ICWSM 2011.

[13]

Cheng, Z., Caverlee, J., and Lee, K. 2010. You Are Where You Tweet: A Content-Based Approach to Geo-locating Twitter Users. In Proc. CIKM 2010.

Digital Library

[14]

Castillo, C., Mendoza, M., and Poblete, B. 2011, Information Credibility on Twitter. In Proc. WWW 2011.

Digital Library

[15]

Costa, P. T., Jr. & McCrae, R. R. 1992. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) manual. Odessa, FL: Psychological Assessment Resources.

[16]

Fast, Lisa A.; Funder, David C. 2008., Personality as manifest in word use: Correlations with self-report, acquaintance report, and behavior. Journal of Personality and Social Psychology, Vol 94(2), 2008.

[17]

Gazan, R. (2011). Social Q&A. J. of the Amer. Soc. for Info Science & Technology, 62(12), 2301--2312.

Digital Library

[18]

Gill, A. J., Nowson, S., Oberlander, J. 2009. What Are They Blogging About? Personality, Topic and Motivation in Blogs, In Proc. ICWSM 2009.

[19]

Golbeck, J. Robles, C., Edmondson, M., Turner, K. 2011. Predicting Personality from Twitter. In Proc. IEEE SocialCom.

[20]

Guo, J., Xu, S., Bao, S., and Yu, Y. 2008. Tapping on the potential of Q&A community by recommending answer providers. In Proc. CIKM 2008.

Digital Library

[21]

Hirsh, J. B. and Peterson, J. B., 2009, Personality and language use in self-narratives Journal of Research in Personality.

[22]

Lee, K., Eoff, B. D. and Caverlee, J.2011. Seven months with the devils: A long term study of content polluters on Twitter. In Proc. ICWSM 2011.

[23]

Liu, X., Croft, W. B., and Koll, M. 2005. Finding experts in community-based question-answering services, In Proc. CIKM'05, 315--316.

Digital Library

[24]

Jurczyk, P., and Agichtein, E., 2007, Discovering authorities in question-answering communities by using link analysis. In Proc. CIKM 2007.

Digital Library

[25]

Mairesse, F., Walker, M. 2006. Words Mark the Nerds: Computational Models of Personality Recognition through Language, In Proc. of CogSci 2006.

[26]

Morris, M., Teevan, J., and Panovich, K. 2010. What Do People Ask Their Social Networks, and Why? A Survey Study of Status Message Q&A Behavior. In Proc. CHI, 2010.

Digital Library

[27]

Nichols, Jeffrey, Kang, Jeon-Hyung, 2012, Asking Questions of Targeted Strangers on Social Networks, In Proc. CSCW2012.

Digital Library

[28]

Nichols, J., Mahmud, J., Drews, C., 2012, Summarizing Sporting Events from Twitter., In Proc. IUI2012.

Digital Library

[29]

Norman, W. T. 1963. Toward an adequate taxonomy of personality attributes: replicated factor structure in peer nomination personality rating. Journal of Abnormal and social Psychology, 66:574--583.

[30]

O'Brien, T. and DeLongis, A. The interactional context of problem-, emotion-, and relationship-focused coping: The role of the big five personality factors. Journal of personality, 64(4):775--813, 1996.

[31]

Pal, A., Farzan, R., Konstan, Joseph A. and Kraut, Robert E., 2011, Early Detection of Potential Experts in Question Answering Communities, In Proc. UMAP 2011.

Digital Library

[32]

Paul, S. A., Hong, L., and Chi, E. H., 2011. "Is Twitter a Good Place for Asking Questions? A Characterization Study", In Proc. ICWSM'11 Posters.

[33]

Pennacchiotti, M., Popescu, A. M. 2011. A Machine Learning Approach to Twitter User Classification, In Proc. ICWSM 2011.

[34]

Pennebaker, J. W., Francis, M. E., Booth, R. J., 2001, Linguistic Inquiry and Word Count. Erlbaum Publishers, Mahwah, NJ.

[35]

Rao, D., Yarowsky, D., Shreevats, A., Gupta, M. 2010, Classifying Latent User Attributes in Twitter, In Proc. of SMUC'10.

Digital Library

[36]

Sakaki, T., Okazaki, M., Matsuo, Y. 2010, Earthquake shakes twitter users: real-time event detection by social sensors, In Proc. of WWW 2010.

Digital Library

[37]

Tausczik, Yla R. and Pennebaker, James W., 2010. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods, In. Journal of Language and Social Psychology 29(1) 24--54

[38]

Teevan, J., Morris, M., and Panovich, K. 2011. Factors Affecting Response Quantity, Quality, and Speed for Questions Asked via Social Network Status Messages. In Proc. ICWSM 2011.

[39]

Yarkoni, Tal. 2010. Personality in 100,000 words: A largescale analysis of personality and word usage among bloggers. Journal of Research in Personality.

[40]

Zhang, J., Ackerman, M. S., and Adamic, L. 2007. Expertise networks in online communities: structure and algorithms. In Proc. WWW, 2007.

Digital Library

[41]

Zhou, Y., Cong, Z., Cui, B., Jensen, C. S., and Yao, J. Routing Questions to the Right Users in Online Communities. In Proc. ICDE 2009.

Digital Library

Cited By

Azeez NFadhal E(2023)Classification of Virtual Harassment on Social Networks Using Ensemble Learning TechniquesApplied Sciences10.3390/app1307457013:7(4570)Online publication date: 4-Apr-2023
https://doi.org/10.3390/app13074570
Zhou MYang HMark GHu MMahumd JPal A(2021)Building Personalized Trust: Discovering What Makes One Trust and Act on Facebook PostsACM Transactions on Social Computing10.1145/34689774:3(1-28)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3468977
Kamisaka DIshikawa Y(2021)Personality Prediction with Cross-Modality Feature ProjectionProceedings of the 2021 International Conference on Multimodal Interaction10.1145/3462244.3479948(758-762)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3462244.3479948
Show More Cited By

Index Terms

Recommending targeted strangers from whom to solicit information on social media
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Uses and gratifications of social networking sites for bridging and bonding social capital

Applying uses and gratifications theory (UGT) and social capital theory, our study examined users of four social networking sites (SNSs) (Facebook, Twitter, Instagram, and Snapchat), and their influence on online bridging and bonding social capital. ...
Recommending branded products from social media
RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

E-commerce companies are increasingly encouraging their users to connect to social media venues such as Facebook and Pinterest. The main strategic goal of such social connections is to boost user interaction and adoption on social media. However only a ...
Why Do People Choose Different Social Media Platforms? Linking Use Motives With Social Media Affordances and Personalities

Drawing on uses and gratifications (U&G) theory, the current research investigates how social media users exploit different media affordances to satisfy their motives and how such motives are shaped by their personalities. A cross-sectional survey among ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

March 2013

470 pages

ISBN:9781450319652

DOI:10.1145/2449396

General Chair:
Jihie Kim
University of Southern California, USA
,
Program Chairs:
Jeffrey Nichols
IBM Research -- Almaden, USA
,
Pedro Szekely
University of Southern California, USA

Copyright © 2013 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 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: 19 March 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IUI '13

Sponsor:

IUI '13: 18th International Conference on Intelligent User Interfaces

March 19 - 22, 2013

California, Santa Monica, USA

Acceptance Rates

IUI '13 Paper Acceptance Rate 43 of 192 submissions, 22%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

38
Total Citations
View Citations
795
Total Downloads

Downloads (Last 12 months)65
Downloads (Last 6 weeks)1

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Azeez NFadhal E(2023)Classification of Virtual Harassment on Social Networks Using Ensemble Learning TechniquesApplied Sciences10.3390/app1307457013:7(4570)Online publication date: 4-Apr-2023
https://doi.org/10.3390/app13074570
Zhou MYang HMark GHu MMahumd JPal A(2021)Building Personalized Trust: Discovering What Makes One Trust and Act on Facebook PostsACM Transactions on Social Computing10.1145/34689774:3(1-28)Online publication date: 8-Oct-2021
https://dl.acm.org/doi/10.1145/3468977
Kamisaka DIshikawa Y(2021)Personality Prediction with Cross-Modality Feature ProjectionProceedings of the 2021 International Conference on Multimodal Interaction10.1145/3462244.3479948(758-762)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3462244.3479948
Das RKarmakar GKamruzzaman J(2021)How Much I Can Rely on You: Measuring Trustworthiness of a Twitter UserIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.292978218:2(949-966)Online publication date: 1-Mar-2021
https://doi.org/10.1109/TDSC.2019.2929782
Talpur BO’Sullivan D(2020)Multi-Class Imbalance in Text Classification: A Feature Engineering Approach to Detect Cyberbullying in TwitterInformatics10.3390/informatics70400527:4(52)Online publication date: 15-Nov-2020
https://doi.org/10.3390/informatics7040052
Ito MEnoki MOguchi M(2020)A study of spot recommendation personalization by considering personality traits2020 IEEE Global Humanitarian Technology Conference (GHTC)10.1109/GHTC46280.2020.9342626(1-6)Online publication date: 29-Oct-2020
https://doi.org/10.1109/GHTC46280.2020.9342626
TOMINAGA THIJIKATA Y(2019)Investigation and Analysis on the Relationship Between Received Feedback and Personality Changes of Twitter UsersTwitterユーザの受け取るフィードバックと人格特性の変化の関係に関する調査と分析Journal of Japan Society for Fuzzy Theory and Intelligent Informatics10.3156/jsoft.31.1_51631:1(516-525)Online publication date: 15-Feb-2019
https://doi.org/10.3156/jsoft.31.1_516
Zhou MFu WPan SBrdiczka OChau PCalvary G(2019)Getting virtually personalProceedings of the 24th International Conference on Intelligent User Interfaces10.1145/3301275.3308445(i-i)Online publication date: 17-Mar-2019
https://dl.acm.org/doi/10.1145/3301275.3308445
Zhou MMark GLi JYang H(2019)Trusting Virtual AgentsACM Transactions on Interactive Intelligent Systems10.1145/32320779:2-3(1-36)Online publication date: 18-Mar-2019
https://dl.acm.org/doi/10.1145/3232077
Li KLv GWang ZLiu QChen EQiao L(2019)Understanding the mechanism of social tie in the propagation process of social network with communication channelFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7453-x13:6(1296-1308)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1007/s11704-018-7453-x
Show More Cited By

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.

Media

Figures

Other

Tables

View Table of Contents