research-article

A Multistage Credibility Analysis Model for Microblogs

Authors:

Majed AlRubaian,

Muhammad Al-Qurishi,

Mabrook Al-Rakhami,

Sk Md Mizanur Rahman,

Atif AlamriAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1434 - 1440

https://doi.org/10.1145/2808797.2810065

Published: 25 August 2015 Publication History

Abstract

Currently, microblogs such as the well-known social network Twitter are one of the most important sources of information in an era of information overload, restiveness and uncertainty. Consequently, developing models to verify information from Twitter has become both a challenging and necessary task. In this paper, we propose a novel multi-stage credibility analysis framework to identify implausible content in Twitter in order to prevent the proliferation of fake or malicious information. We used Naïve Bayes classifier and it is enhanced by considering the relative importance of the used features to improve the classification accuracy. We examine the classifier with 1000 unique tweets along with 700 account. The result quite motivating with accuracy 90.3%, 86.24% Precision and 98.8% recall.

References

[1]

A. J. Flanagin and M. J. Metzger, "Digital media and youth: Unparalleled opportunity and unprecedented responsibility," Digital media, youth, and credibility, pp. 5--27, 2008.

[2]

M. Indrawan-Santiago, H. Han, H. Nakawatase, and K. Oyama, "Evaluating credibility of interest reflection on Twitter," International Journal of Web Information Systems, vol. 10, pp. 343--362, 2014.

[3]

C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter," presented at the Proceedings of the 20th international conference on World wide web, Hyderabad, India, 2011.

Digital Library

[4]

K. R. Canini, B. Suh, and P. L. Pirolli, "Finding credible information sources in social networks based on content and social structure," in Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, 2011, pp. 1--8.

[5]

S. K. Sikdar, B. Kang, J. O'Donovan, T. Hollerer, and S. Adal, "Cutting through the noise: Defining ground truth in information credibility on twitter," HUMAN, vol. 2, pp. pp. 151--167, 2013.

[6]

S. Sikdar, S. Adali, M. Amin, T. Abdelzaher, K. Chan, J. H. Cho, B. Kang, and J. O'Donovan, "Finding true and credible information on Twitter," in Information Fusion (FUSION), 2014 17th International Conference on, 2014, pp. 1--8.

[7]

D. Saez-Trumper, "Fake Tweet Buster: A Webtool to Identify Users Promoting Fake News on Twitter," 2014.

[8]

A. A. AlMansour, L. Brankovic, and C. S. Iliopoulos, "A Model for Recalibrating Credibility in Different Contexts and Languages-A Twitter Case Study," International Journal of Digital Information and Wireless Communications (IJDIWC), vol. 4, pp. 53--62, 2014.

[9]

A. Gupta and P. Kumaraguru, "Credibility ranking of tweets during high impact events," in Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, 2012, p. 2.

Digital Library

[10]

A. Gupta and P. Kumaraguru, "@ Twitter credibility ranking of tweets on events# breakingnews," 2012.

[11]

S. Y. Rieh, M. R. Morris, M. J. Metzger, H. Francke, and G. Y. Jeon, "Credibility Perceptions of Content Contributors and Consumers in Social Media," 2014.

[12]

S. M. Shariff, X. Zhang, and M. Sanderson, "User Perception of Information Credibility of News on Twitter," in Advances in Information Retrieval, ed: Springer, 2014, pp. 513--518.

[13]

J. Yang, S. Counts, M. R. Morris, and A. Hoff, "Microblog credibility perceptions: Comparing the usa and china," in Proceedings of the 2013 conference on Computer supported cooperative work, 2013, pp. 575--586.

Digital Library

[14]

M. Schmierbach and A. Oeldorf-Hirsch, "A little bird told me, so I didn't believe it: Twitter, credibility, and issue perceptions," Communication Quarterly, vol. 60, pp. 317--337, 2012.

[15]

M. R. Morris, S. Counts, A. Roseway, A. Hoff, and J. Schwarz, "Tweeting is believing?: understanding microblog credibility perceptions," in Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, 2012, pp. 441--450.

Digital Library

[16]

A. Pal and S. Counts, "What's in a@ name? How Name Value Biases Judgment of Microblog Authors," in ICWSM, 2011.

[17]

D. Westerman, P. R. Spence, and B. Van Der Heide, "A social network as information: The effect of system generated reports of connectedness on credibility on Twitter," Computers in Human Behavior, vol. 28, pp. 199--206, 2012.

Digital Library

[18]

K. A. Johnson, "The effect of Twitter posts on students' perceptions of instructor credibility," Learning, Media and Technology, vol. 36, pp. 21--38, 2011.

[19]

C. L. Armstrong and M. J. McAdams, "Blogs of information: How gender cues and individual motivations influence perceptions of credibility," Journal of Computer-Mediated Communication, vol. 14, pp. 435--456, 2009.

[20]

A. A. AlMansour, L. Brankovic, and C. S. Iliopoulos, "Evaluation of credibility assessment for microblogging: models and future directions," in Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, 2014, p. 32.

Digital Library

[21]

A. Gupta, H. Lamba, P. Kumaraguru, and A. Joshi, "Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy," presented at the Proceedings of the 22nd international conference on World Wide Web companion, Rio de Janeiro, Brazil, 2013.

Digital Library

[22]

A. Pal and S. Counts, "Identifying topical authorities in microblogs," in Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 45--54.

Digital Library

[23]

B. Kang, J. O'Donovan, and T. Höllerer, "Modeling topic specific credibility on twitter," in Proceedings of the 2012 ACM international conference on Intelligent User Interfaces, 2012, pp. 179--188.

Digital Library

[24]

Y. Ikegami, K. Kawai, Y. Namihira, and S. Tsuruta, "Topic and Opinion Classification Based Information Credibility Analysis on Twitter," in Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, 2013, pp. 4676--4681.

Digital Library

[25]

E. J. Briscoe, D. S. Appling, and H. Hayes, "Social Network Derived Credibility," in Recommendation and Search in Social Networks, ed: Springer, 2015, pp. 59--75.

[26]

S. Ravikumar, R. Balakrishnan, and S. Kambhampati, "Ranking tweets considering trust and relevance," in Proceedings of the Ninth International Workshop on Information Integration on the Web, 2012, p. 4.

Digital Library

[27]

H. S. Al-Khalifa and R. M. Al-Eidan, "An experimental system for measuring the credibility of news content in Twitter," International Journal of Web Information Systems, vol. 7, pp. 130--151, 2011.

[28]

M. Abdul-Mageed, S. Kübler, and M. Diab, "Samar: A system for subjectivity and sentiment analysis of arabic social media," in Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, 2012, pp. 19--28

Digital Library

Cited By

Qureshi KMalick R(2025)Tweet Credibility Ranker: A Credibility Features’ Fusion ModelCognitive Computation10.1007/s12559-025-10413-517:1Online publication date: 29-Jan-2025
https://doi.org/10.1007/s12559-025-10413-5
Luo NXie DMo YLi FTeng CJi D(2024)Joint rumour and stance identification based on semantic and structural information in social networksApplied Intelligence10.1007/s10489-023-05170-754:1(264-282)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05170-7
Surya Bharadwaj GManikanta NBishi MKrishna Teja PRama Koteswara Rao GSrinivas P(2023)Uncovering the Truth: Exploring Traditional Deep Learning Techniques for Fabricated News Detection2023 2nd International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA58104.2023.10212337(714-723)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECAA58104.2023.10212337
Show More Cited By

Index Terms

A Multistage Credibility Analysis Model for Microblogs
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

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 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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Publication History

Published: 25 August 2015

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Funding Sources

Deanship of Scientific Research, King Saud University

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ASONAM '15

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SIGKDD

ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Citations

Cited By

Qureshi KMalick R(2025)Tweet Credibility Ranker: A Credibility Features’ Fusion ModelCognitive Computation10.1007/s12559-025-10413-517:1Online publication date: 29-Jan-2025
https://doi.org/10.1007/s12559-025-10413-5
Luo NXie DMo YLi FTeng CJi D(2024)Joint rumour and stance identification based on semantic and structural information in social networksApplied Intelligence10.1007/s10489-023-05170-754:1(264-282)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05170-7
Surya Bharadwaj GManikanta NBishi MKrishna Teja PRama Koteswara Rao GSrinivas P(2023)Uncovering the Truth: Exploring Traditional Deep Learning Techniques for Fabricated News Detection2023 2nd International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA58104.2023.10212337(714-723)Online publication date: 19-Jul-2023
https://doi.org/10.1109/ICECAA58104.2023.10212337
Narang PSingh AMonga H(2023)Opinion-Based Machine Learning Approach for Fake News ClassificationProceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022)10.1007/978-3-031-31164-2_4(33-42)Online publication date: 1-May-2023
https://doi.org/10.1007/978-3-031-31164-2_4
Kularbphettong KVichivanives RKanjanaprakarn PBussaban KChutrtong JRuksuntorn N(2022)Machine Learning Algorithms for Natural Language Processing Tasks: A Case of COVID-19 Twitter data (Thailand)International Journal on Applied Physics and Engineering10.37394/232030.2022.1.51(31-32)Online publication date: 31-Dec-2022
https://doi.org/10.37394/232030.2022.1.5
Kavitha MSrinivasan RBhuvanya R(2022)Fake News Detection Using Machine Learning AlgorithmsMachine Learning Paradigm for Internet of Things Applications10.1002/9781119763499.ch10(181-207)Online publication date: 4-Apr-2022
https://doi.org/10.1002/9781119763499.ch10
Alonso MVilares DGómez-Rodríguez CVilares J(2021)Sentiment Analysis for Fake News DetectionElectronics10.3390/electronics1011134810:11(1348)Online publication date: 5-Jun-2021
https://doi.org/10.3390/electronics10111348
Qureshi KMalick RSabih M(2021)Social Media and Microblogs Credibility: Identification, Theory Driven Framework, and RecommendationIEEE Access10.1109/ACCESS.2021.31144179(137744-137781)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3114417
Abu-Salih BWongthongtham PZhu DChan KRudra AAbu-Salih BWongthongtham PZhu DChan KRudra A(2021)Credibility Analysis in Social Big DataSocial Big Data Analytics10.1007/978-981-33-6652-7_3(61-88)Online publication date: 11-Mar-2021
https://doi.org/10.1007/978-981-33-6652-7_3
Kaliyar RMohnot ARaghhul RPrathyushaa VGoswami ASingh NDash P(2021)MultiDeepFake: Improving Fake News Detection with a Deep Convolutional Neural Network Using a Multimodal DatasetAdvanced Computing10.1007/978-981-16-0401-0_20(267-279)Online publication date: 11-Feb-2021
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