Review spam detection

By Nitin Jindal, Bing Liu. 2007

527 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems, Natural Language Processing

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Citations in the corpus

Universal Language Model Fine-tuning for Text Classification (2018, background): “Text classification is a category of Natural Language Processing (NLP) tasks with real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al.”
Fine-tuned Language Models for Text Classification (2018, background): “…is a category of Natural Language Processing (NLP) tasks with many important real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al., 2011), and commercial document classiﬁcation, such…”
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].”
Sentiment Analysis and Subjectivity (2010, background): “There are generally three types of spam reviews as defined by Jindal and Liu in [40, 41].”
Sentiment analysis: A review and comparative analysis of web services (2015, background): “, genre or authorship detection tries to determine the genre or the person who has written a text/opinion [22,78,56,79] or opinion spam detection tries to detect opinions or reviews which contain untrusted contents published to distort public opinion towards people, companies or products [34,65,60,9”
What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “Pseudo-fake reviews are either manually annotated [36, 61], or generated by Amazon Mechanical Turkers (AMT) [29, 46, 49].”
Promotional Reviews: An Empirical Investigation of Online Review Manipulation (2014, unknown): “In particular, we examine the di erence in review distributions across Expedia and TripAdvisor, sites with di erent 17The reason behind the later data collection e ort is that Orbitz had relatively few reviews in October of 2011: 1) there were 104 hotels that had reviews at TripAdvisor and Expedia…”
A comprehensive survey of anomaly detection techniques for high dimensional big data (2020, background): “Various other surveys can also be observed, such as those by Gama et al. [15], Gupta et al. [16], Heydari et al. [17], and Jindal and Liu [18], Pathasarathy [19], Phua et al. [20], Tamboli et al. [21], and Spirin et al. [22], which further highlight the problems either in anomaly detection or in…”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “Furthermore, researchers have extensively studied detection of opinion spam or fake reviews in online review systems using features based on review content [32, 48, 53] and a variety of metadata [10, 19, 20].”
Opinion Mining and Sentiment Analysis (2008, background): “Review- or opinion-spam detection — the identification of deliberately misleading reviews — is a line of work by Jindal and Liu [141, short version available as Jindal and Liu [140]] in the same vein.”
The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study (2016, background): “Indeed, a number of previous works have dealt with the detection of fake reviews (Jindal and Liu, 2007; Badaskar et al., 2008; Mackiewicz, 2008; Jindal et al., 2010; Ott et al., 2011; Fornaciari and Poesio, 2014).”
Detecting product review spammers using rating behaviors (2010, unknown): “A preliminary study was reported in [9].”
Text Level Graph Neural Network for Text Classification (2019, background): “Text classification is a fundamental problem of natural language processing (NLP), which has lots of applications like SPAM detection, news filtering, and so on (Jindal and Liu, 2007; Aggarwal and Zhai, 2012).”
Detection of Harassment on Web 2.0 (2009, methodology): “Jindal and Liu [9, 8, 10] used textual features and applied logistic regression to identify review spam.”
Adversarial Web Search (2011, background): “There are broadly two types of spam reviews [112].”