News credibility scroing: suggestion of research methodology to determine the reliability of news distributed in SNS
Pages 737 - 740
Abstract
We provide a more optimized model for calculating credibility score of information in SNS. We premeditated two heuristics which using characteristics of the credibility score for each document: (1) Expertise and (2) unbiasedness. Also, we divide the users in SNS into three types: (1) Creator (2) Distributor, and (3) Follower. Our model is designed to calculate Expertise and Un-biasedness for three types of SNS users (Creator, Distributor, and Follower) by using logistic regression model. Our model not only reveals whether the information is 'accurate and unbiased', but also investigates the 'source, distribution channel, and audience' of the information. We expect our credibility scoring will give answers to the 'qualitative problem' our online world is currently facing.
References
[1]
Kiousis, Spiro. "Public trust or mistrust? Perceptions of media credibility in the information age." Mass Communication & Society 4.4, 2001, pp.381-403.
[2]
Kolari, Pranam, Tim Finin, and Anupam Joshi. "SVMs for the blogosphere: Blog identification and splog detection." AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs. Vol. 4. 2006.
[3]
Lin, Yu-Ru, et al. "Detecting splogs via temporal dynamics using self-similarity analysis." ACM Transactions on the Web (TWEB) 2.1, 2008, pp.4.
[4]
Macdonald, Craig, Iadh Ounis, and Ian Soboroff. "Is spam an issue for opinionated blog post search?." Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, 2009.
[5]
Jindal, Nitin, and Bing Liu. "Opinion spam and analysis." Proceedings of the international conference on Web search and web data mining. ACM, 2008.
[6]
Hu, Meiqun, et al. "Measuring article quality in wikipedia: models and evaluation." Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. ACM, 2007.
[7]
Agichtein, Eugene, et al. "Finding high-quality content in social media." Proceedings of the international conference on Web search and web data mining. ACM, 2008.
[8]
Zhou, Yun, and W. Bruce Croft. "Document quality models for web ad hoc retrieval." Proceedings of the 14th ACM international conference on Information and knowledge management. ACM, 2005.
[9]
Jeon, Jiwoon, et al. "A framework to predict the quality of answers with non-textual features." Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2006.
[10]
Balog, Krisztian, Leif Azzopardi, and Maarten De Rijke. "Formal models for expert finding in enterprise corpora." Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2006.
[11]
Zhang, Jing, Jie Tang, and Juanzi Li. "Expert finding in a social network." Advances in Databases: Concepts, Systems and Applications. Springer Berlin Heidelberg, 2007. pp.1066-1069.
[12]
Petkova, Desislava, and W. Bruce Croft. "Hierarchical language models for expert finding in enterprise corpora." International Journal on Artificial Intelligence Tools 17.01, 2008, pp.5-18.
[13]
Yin, Xiaoxin, and Wenzhao Tan. "Semi-supervised truth discovery." Proceedings of the 20th international conference on World wide web. ACM, 2011.
[14]
Yin, Xiaoxin, Jiawei Han, and Philip S. Yu. "Truth discovery with multiple conflicting information providers on the web." Knowledge and Data Engineering, IEEE Transactions on 20.6, 2008, pp.796-808.
[15]
Dong, Xin Luna, Laure Berti-Equille, and Divesh Srivastava. "Truth discovery and copying detection in a dynamic world." Proceedings of the VLDB Endowment 2.1, 2009, pp.562-573.
[16]
Morris, Meredith Ringel, et al. "Tweeting is believing?: understanding microblog credibility perceptions." Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. ACM, 2012.
[17]
Flanagin, Andrew J., and Miriam J. Metzger. "Internet use in the contemporary media environment." Human communication research 27.1 (2001): 153--181.
[18]
Flanagin, Andrew J., and Miriam J. Metzger. "Perceptions of Internet information credibility." Journalism & Mass Communication Quarterly 77.3, 2000, pp.515-540.
[19]
Chi, Michelene TH, Paul J. Feltovich, and Robert Glaser. "Categorization and representation of physics problems by experts and novices." Cognitive science 5.2, 1981, pp.121-152.
[20]
Wiener, Joshua L., and John C. Mowen. "Source credibility: on the independent effects of trust and expertise." Advances in consumer research 13.1, 1986, pp.306-310.
[21]
Gaziano, Cecilie, and Kristin McGrath. "Measuring the concept of credibility." Journalism Quarterly 63.3, 1986, pp.451-462.
[22]
Lehmann, Janette, et al. "Finding news curators in twitter." WWW/SNOW workshop. 2013.
[23]
Castillo, Carlos, Marcelo Mendoza, and Barbara Poblete. "Information credibility on twitter." Proceedings of the 20th international conference on World wide web. ACM, 2011.
Recommendations
Effects of Credibility Indicators on Social Media News Sharing Intent
CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing SystemsIn recent years, social media services have been leveraged to spread fake news stories. Helping people spot fake stories by marking them with credibility indicators could dissuade them from sharing such stories, thus reducing their amplification. We ...
NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges
CSCW2Struggling to curb misinformation, social media platforms are experimenting with design interventions to enhance consumption of credible news on their platforms. Some of these interventions, such as the use of warning messages, are examples of nudges---a ...
Factors and effects of information credibility
ICEC '07: Proceedings of the ninth international conference on Electronic commerceWebsite success hinges on how credible the consumers consider the information on the website. Unless consumers believe the website's information is credible, they are not likely to be willing to act on the advice and will not develop loyalty to the ...
Comments
Information & Contributors
Information
Published In
August 2019
1228 pages
Copyright © 2019 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
In-Cooperation
- IEEE CS
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 January 2020
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
ASONAM '19
Sponsor:
ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining
August 27 - 30, 2019
British Columbia, Vancouver, Canada
Acceptance Rates
ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;
Overall Acceptance Rate 116 of 549 submissions, 21%
Upcoming Conference
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 106Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)1
Reflects downloads up to 07 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in