Abstract
Recently, there are rapidly increasing stock-related comments sharing on Internet. However, the qualities of these comments are quite different. This paper presents an automatic approach to identify high quality stock comments by means of estimating the credibility of the comments from two aspects. Firstly, the credibility of information source is evaluated by estimating the historical credibility and industry-related credibility using a linear regression model. Secondly, the credibility of the comment information is estimated through calculating the uncertainty of comment content using an uncertainty glossary based matching method. The final stock comment credibility is obtained by incorporating the above two credibility measures. The experiments on real stock comment dataset show that the proposed approach identifies high quality stock comments and institutions/individuals effectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hovland, C.I., Weiss, W.: The influence of source credibility on communication effectiveness. Public Opinion Quarterly 15(4), 635–650 (1951)
Ratkiewicz, J., Conover, M., Meiss, M., et al.: Detecting and Tracking the spread of Astroturf Memes in Microblog streams. arXiv preprint arXiv:1011.3768 (2010)
Qazvinian, E., Rosengren, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of EMNLP, pp. 1589–1599 (2011)
Morris, M.R., Counts, S., Roseway, A., et al.: Tweeting is believing?: understanding microblog credibility perceptions. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 441–450 (2012)
Castillo, M.M., Poblete, B.:Information credibility on twitter. In: Proceedings of International Conference on World Wide Web, pp. 675–684(2011)
Agichtein, E., Castillo, C., Donato, D., et al.: Finding high-quality content in social media. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 183–194(2008)
Weng, J., Lim, E.P., Jiang, J., et al.: Twitter rank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
Pal, A.: Counts S.. Identifying topical authorities in microblogs. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining ACM, pp. 45–54(2011)
Bakshy, E., Hofman, J.M., Mason, W.A., et al.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65–74. ACM (2011)
Medlock, Briscoe, T.: Weakly supervised learning for hedge classification in scientific literature. In: Proceedings of ACL (2007)
SzarvasG.: Hedge classification in biomedical texts with a weakly supervised selection of keywords. In: Proceedings of ACL (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qiu, Q., Xu, R., Liu, B., Gui, L., Zhou, Y. (2014). Credibility Estimation of Stock Comments Based on Publisher and Information Uncertainty Evaluation. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-662-45652-1_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45651-4
Online ISBN: 978-3-662-45652-1
eBook Packages: Computer ScienceComputer Science (R0)