short-paper

Improved Stance Prediction in a User Similarity Feature Space

Authors:

Kareem Darwish,

Tahar ZanoudaAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 145 - 148

https://doi.org/10.1145/3110025.3110112

Published: 31 July 2017 Publication History

Abstract

Predicting the stance of social media users on a topic can be challenging, particularly for users who never express explicit stances. Earlier work has shown that using users' historical or non-relevant tweets can be used to predict stance. We build on prior work by making use of users' interaction elements, such as retweeted accounts and mentioned hashtags, to compute the similarities between users and to classify new users in a user similarity feature space. We show that this approach significantly improves stance prediction on two datasets that differ in terms of language, topic, and cultural background.

References

[1]

W. Magdy, K. Darwish, N. Abokhodair, A. Rahimi, and T. Baldwin, "#isisisnotislam or#deportallmuslims?: predicting unspoken views," in WebSci 2016, 2016, pp. 95--106.

Digital Library

[2]

M. Pennacchiotti and A.-M. Popescu, "Democrats, Republicans and Starbucks afficionados: user classification in Twitter," in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011, pp. 430--438.

Digital Library

[3]

F. M. F. Wong, C. W. Tan, S. Sen, and M. Chiang, "Quantifying political leaning from tweets and retweets." in ICWSM 2013, 2013, pp. 640--649.

[4]

D. DellaPosta, Y. Shi, and M. Macy, "Why do liberals drink lattes?" American Journal of Sociology, vol. 120, no. 5, pp. 1473--1511, 2015.

[5]

K. Garimella, "Quantifying and bursting the online filter bubble," in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 2017, pp. 837--837.

Digital Library

[6]

G. Wu and E. Y. Chang, "Adaptive feature-space conformal transformation for imbalanced-data learning," in ICML, 2003, pp. 816--823.

[7]

P. Barberá, "Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data," Political Analysis, vol. 23, no. 1, pp. 76--91, 2015.

[8]

J. Borge-Holthoefer, W. Magdy, K. Darwish, and I. Weber, "Content and network dynamics behind egyptian political polarization on twitter," in Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 2015, pp. 700--711.

Digital Library

[9]

I. Weber, V. R. K. Garimella, and A. Batayneh, "Secular vs. islamist polarization in egypt on twitter," in ASONAM 2013, 2013, pp. 290--297.

Digital Library

[10]

W. Magdy, K. Darwish, and I. Weber, "#failedrevolutions: Using twitter to study the antecedents of isis support," First Monday, vol. 21(2).

[11]

D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta, "Classifying latent user attributes in twitter," in Proceedings of the 2nd Int. Workshop on Search and Mining User-generated Contents, 2010, pp. 37--44.

Digital Library

[12]

I. Himelboim, S. McCreery, and M. Smith, "Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on twitter," Journal of Computer-Mediated Communication, vol. 18, no. 2, pp. 40--60, 2013.

Digital Library

[13]

M. Pennacchiotti and A.-M. Popescu, "A machine learning approach to twitter user classification." ICWSM, vol. 11, no. 1, pp. 281--288, 2011.

[14]

Y. Duan, F. Wei, M. Zhou, and H.-Y. Shum, "Graph-based collective classification for tweets," in CIKM 2012. ACM, 2012, pp. 2323--2326.

[15]

C. Tan, L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li, "User-level sentiment analysis incorporating social networks," in SIGKDD 2011, 2011, pp. 1397--1405.

[16]

S. M. Mohammad, S. Kiritchenko, P. Sobhani, X. Zhu, and C. Cherry, "Semeval-2016 task 6: Detecting stance in tweets," in SemEval 2016, vol. 16, 2016.

[17]

A. El-Kahky, K. Darwish, A. S. Aldein, M. A. El-Wahab, A. Hefny, and W. Ammar, "Improved transliteration mining using graph reinforcement," in EMNLP 2011, 2011, pp. 1384--1393.

[18]

T. Joachims, Learning to Classify Text Using Support Vector Machines -- Methods, Theory, and Algorithms. Kluwer/Springer, 2002.

[19]

A. Abdelali, N. Durrani, K. Darwish, and H. Mubarak, "Farasa: A fast and furious segmenter for arabic," in NAACL 2016, 2016.

Cited By

Aljawarneh SSwedat S(2024)Fake News Detection Using Enhanced BERTIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322378611:4(4843-4850)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2022.3223786
Charfi ABessghaier MAtalla AAkasheh RAl-Emadi SZaghouani W(2024)Stance detection in Arabic with a multi-dialectal cross-domain stance corpusSocial Network Analysis and Mining10.1007/s13278-024-01335-514:1Online publication date: 16-Aug-2024
https://doi.org/10.1007/s13278-024-01335-5
Gambini MSenette CFagni TTesconi M(2024)Evaluating large language models for user stance detection on X (Twitter)Machine Learning10.1007/s10994-024-06587-y113:10(7243-7266)Online publication date: 6-Sep-2024
https://doi.org/10.1007/s10994-024-06587-y
Show More Cited By

Improved Stance Prediction in a User Similarity Feature Space
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

Recommendations

Weakly-Guided User Stance Prediction via Joint Modeling of Content and Social Interaction
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Social media websites have become a popular outlet for online users to express their opinions on controversial issues, such as gun control and abortion. Understanding users' stances and their arguments is a critical task for policy-making process and ...
User Stance Prediction via Online Behavior Mining
WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Nowadays social media, such as Twitter and all kinds of online forums, becomes a platform where people can express their opinions implicitly or explicitly. For example, in Twitter, people follow people they trust, and retweet the tweets they agree. In ...
The disembodied predictor stance

Argue that predictors are embodied, and predictions affect their own realization.Demonstrate a technical framework explicitly acknowledging prediction embodiment.Present application domains that can benefit from an embodied prediction viewpoint.Argue ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 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: 31 July 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper
Research
Refereed limited

Conference

ASONAM '17

Sponsor:

SIGKDD

ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

Acceptance Rates

Overall Acceptance Rate 116 of 549 submissions, 21%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

24
Total Citations
View Citations
393
Total Downloads

Downloads (Last 12 months)28
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Aljawarneh SSwedat S(2024)Fake News Detection Using Enhanced BERTIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322378611:4(4843-4850)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2022.3223786
Charfi ABessghaier MAtalla AAkasheh RAl-Emadi SZaghouani W(2024)Stance detection in Arabic with a multi-dialectal cross-domain stance corpusSocial Network Analysis and Mining10.1007/s13278-024-01335-514:1Online publication date: 16-Aug-2024
https://doi.org/10.1007/s13278-024-01335-5
Gambini MSenette CFagni TTesconi M(2024)Evaluating large language models for user stance detection on X (Twitter)Machine Learning10.1007/s10994-024-06587-y113:10(7243-7266)Online publication date: 6-Sep-2024
https://doi.org/10.1007/s10994-024-06587-y
Poonam KRamakrishnudu T(2024)Dual Bi-LSTM-GRU based stance detection in tweets ordered classesNeural Computing and Applications10.1007/s00521-024-10549-9Online publication date: 6-Dec-2024
https://doi.org/10.1007/s00521-024-10549-9
Li HYang WWang WWang H(2024)Neural network approaches for rumor stance detection: Simulating complex rumor propagation systemsConcurrency and Computation: Practice and Experience10.1002/cpe.809336:16Online publication date: 15-May-2024
https://doi.org/10.1002/cpe.8093
Draws TNatesan Ramamurthy KBaldini IDhurandhar APadhi ITimmermans BTintarev N(2023)Explainable Cross-Topic Stance Detection for Search ResultsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578296(221-235)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578296
Nguyen VZhang XColarik A(2023)A Research Pathway for Stance Detection in Speech2023 Global Conference on Information Technologies and Communications (GCITC)10.1109/GCITC60406.2023.10659745(1-8)Online publication date: 1-Dec-2023
https://doi.org/10.1109/GCITC60406.2023.10659745
Rajendran UKhalifa ATrabelsi A(2023)Learning Representations through Contrastive Strategies for a more Robust Stance Detection2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302466(1-9)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302466
Alsaif HAldossari H(2023)Review of stance detection for rumor verification in social mediaEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105801119:COnline publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105801
Haouari FElsayed T(2023)Detecting Stance of Authorities Towards Rumors in Arabic Tweets: A Preliminary StudyAdvances in Information Retrieval10.1007/978-3-031-28238-6_33(430-438)Online publication date: 17-Mar-2023
https://doi.org/10.1007/978-3-031-28238-6_33
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