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The predictive power of online chatter
Abstract
An increasing fraction of the global discourse is migrating online in the form of blogs, bulletin boards, web pages, wikis, editorials, and a dizzying array of new collaborative technologies. The migration has now proceeded to the point that topics reflecting certain individual products are sufficiently popular to allow targeted online tracking of the ebb and flow of chatter around these topics. Based on an analysis of around half a million sales rank values for 2,340 books over a period of four months, and correlating postings in blogs, media, and web pages, we are able to draw several interesting conclusions.First, carefully hand-crafted queries produce matching postings whose volume predicts sales ranks. Second, these queries can be automatically generated in many cases. And third, even though sales rank motion might be difficult to predict in general, algorithmic predictors can use online postings to successfully predict spikes in sales rank.
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Citations
Cited by
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Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
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BackgroundPredicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081
From Predictive to Prescriptive Analytics
Bertsimas, D., & Kallus, N. (2020) · Management Science · doi:10.1287/mnsc.2018.3253 · Cited by 784
Big data for development: a review of promises and challenges
Hilbert, M. (2016) · Development Policy Review · doi:10.1111/dpr.12142 · Cited by 679
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Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005962107 · Cited by 636
Metadata
- Authors
- Gruhl, D., Guha, R., Kumar, R., Novak, J., & Tomkins, A.
- Venue
- Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
- Publisher
- ACM
- Published
- 2005-08-21
- Pages
- 78-87
- OpenAlex
- W1982381099
- Cited by
- 407
- References
- 0
- FWCI
- 31.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The predictive power of online chatter. https://app.cochat.ai/papers/10.1145/1081870.1081883
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- Gruhl, D., Guha, R., Kumar, R., Novak, J., & Tomkins, A. (2005). The predictive power of online chatter. Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. https://doi.org/10.1145/1081870.1081883
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
