Opinion spam and analysis

By Nitin Jindal, B. Liu. 2008

1,668 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “Recommendation on (different) Amazon data has been discussed in [15] and [25].”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…“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.”
DynaSent: A Dynamic Benchmark for Sentiment Analysis (2020, background): “Many sentiment datasets are derived from customer reviews of products and services (Pang and Lee, 2004, 2005; Socher et al., 2013; Maas et al., 2011; Jindal and Liu, 2008; Ni et al., 2019; McAuley et al., 2012; Zhang et al., 2015).”
Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics (2014, positive): “studies use machine learning methods to identify fake reviews (Jindal and Liu 2008; Li, Huang, Yang and Zhu 2011; Ott, Choi, Cardie and Hancock 2011).”
Graph based anomaly detection and description: a survey (2014, methodology): “More specifically, with respect to (i) and (ii), [Jindal and Liu, 2008, Jindal et al., 2010] extract behavioral features such as review length, posting times, time order of reviews (whether first posted review or not), etc. in addition to rule-based mining to spot suspicious reviewers.”
A Survey of Fake News (2020, background): “Fake news, unlike information such as fake reviews [Jindal and Liu 2008], can “attract” both malicious and normal users, where each group is attracted for different reasons (i.e., different intentions).”
Social Media Text Classification under Negative Covariate Shift (2015, methodology): “We obtained the Amazon review database from the authors of (Jindal and Liu 2008), and constructed a dataset with reviews of 50 types of products, which we also call 50 topics.”
Aspect-based sentiment analysis of movie reviews on discussion boards (2010, background): “For instance, Jindal and Liu [49] studied opinion spam in product reviews on Amazon.com, and built a logistic regression model for opinion spam detection.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “Related studies can be seen in, e.g., [Jindal and Liu 2008; Li et al. 2014, 2017b; Mukherjee et al. 2013b; Ott et al. 2011; Popoola 2018; Ren and Ji 2017; Shojaee et al. 2013; Zhang et al. 2016].”
In Defense of Minhash over Simhash (2014, background): “…has been widely adopted in the search industry, with numerous applications, for example, large-sale machine learning systems [23, 21], Web spam [29, 18], content matching for online advertising [25], compressing social networks [9], advertising diversiﬁcation [13], graph sampling [24], Web graph…”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, methodology): “, [13, 19, 28, 30, 32, 42, 49]), which were extracted from review text.”
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection (2018, background): “Since people are increasingly relying on online reviews to make purchase decisions [11, 72, 78, 81], machine learning methods have been used to detect deception in online reviews [22, 24, 32, 60, 77, 79].”
Estimating the prevalence of deception in online review communities (2012, methodology): “Related work [8, 11] has hypothesized that the relative impact of spam reviews”
A Survey of Opinion Mining and Sentiment Analysis (2012, background): “The problem of detecting fake or spam opinions was introduced by Jindal and Liu in [38, 39].”
Exploiting Burstiness in Reviews for Review Spammer Detection (2021, methodology): “com as our experiment data, which were crawled by the authors of (Jindal and Liu 2008).”
Survey on mining subjective data on the web (2011, background): “At the same time, we observe that the phenomenon of opinion spam (or fake reviews) is also growing (Jindal and Liu 2008; Chen et al. 2009; Lim et al. 2010).”
Adapting Social Spam Infrastructure for Political Censorship (2012, background): “Despite the complexity of the attack, we show how Twitter’s relevance-based search helped mitigate the attack’s impact on users searching for information regarding the Russian election.”