Opinion spam and analysis
Abstract
Evaluative texts on the Web have become a valuable source of opinions on products, services, events, individuals, etc. Recently, many researchers have studied such opinion sources as product reviews, forum posts, and blogs. However, existing research has been focused on classification and summarization of opinions using natural language processing and data mining techniques. An important issue that has been neglected so far is opinion spam or trustworthiness of online opinions. In this paper, we study this issue in the context of product reviews, which are opinion rich and are widely used by consumers and product manufacturers. In the past two years, several startup companies also appeared which aggregate opinions from product reviews. It is thus high time to study spam in reviews. To the best of our knowledge, there is still no published study on this topic, although Web spam and email spam have been investigated extensively. We will see that opinion spam is quite different from Web spam and email spam, and thus requires different detection techniques. Based on the analysis of 5.8 million reviews and 2.14 million reviewers from amazon.com, we show that opinion spam in reviews is widespread. This paper analyzes such spam activities and presents some novel techniques to detect them
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Disinformation can take forms beyond hoaxes or "fake news", including manipulation of social systems such as Google bombs or review spam.
“Disinformation can take forms beyond the production of hoaxes or “fake news” (Lazer et al., 2018); and include other types of manipulation of social systems, such as Google bombs to associate a keyword to a URL by repeatedly searching for the keyword and clicking on the URL, or review spam (Jindal and Liu, 2008) to manipulate the reputation of a product/service.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Jindal and Liu, 2008
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Recommender systems — beyond matrix completion
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A Novel Transit Rider Satisfaction Metric: Rider Sentiments Measured from Online Social Media Data
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Detecting Review Manipulation on Online Platforms with Hierarchical Supervised Learning
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hPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews
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- Authors
- Jindal, N., & Liu, B.
- Affiliations
- University of Illinois Chicago (US)
- ORCID
- Liu, Bing
- Venue
- Proceedings of the international conference on Web search and web data mining - WSDM '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 219
- OpenAlex
- W2047756776
- Cited by
- 1,542
- References
- 29
- Citations by year
- 2026: 36 · 2025: 64 · 2024: 71 · 2023: 78 · 2022: 91 · 2021: 112 · 2020: 124 · 2019: 118 · 2018: 128 · 2017: 137 · 2016: 99 · 2015: 137 · 2014: 103 · 2013: 77 · 2012: 77
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- opinion spam, spam detection, product reviews, online reviews, review aggregation, Amazon reviews
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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