Over 18 million reviews were created on Yelp 2014Footnote 4 and Trip Advisor currently has over 200 million reviewsFootnote 5. Online reviews are constantly being generated on various web sites across the Internet. Consequently, Big Data techniques are needed to address the problem of review spam. Big Data, while an overused buzzword with an elusive definition, is often quantified with the Four V'sFootnote 6: (1) Volume -- the sheer size and scale of the data, (2) Velocity -- the rate at which new data is created and consumed by processing engines, (3) Variety -- the different formats that data may be stored in, and (4) Veracity -- the quality level of the data. The Volume and Velocity of online reviews are noted by merely visiting e-commerce and customer rating sites, such as Yelp and Amazon. There is great Variety across the possible industry sectors for reviews (such as hotels, restaurants, e-commerce, home services, etc.), along with the multiplicity of languages that reviews are written in. Veracity is a problem with online reviews, since the vast majority of reviews are unlabeled, which means it is not easily known whether the review is fake or not. Additionally, standard machine learning algorithms tend to break down and become ineffective when dealing with data of this size, which poses a problem when trying to apply these algorithms for review spam detection [4]. Thus, review spam detection is a Big Data problem, as there are numerous challenges when analyzing and classifying varying reviews from disconnected sources. Review3 : The rooms service is bad These features deal with the underlying meaning or concepts of the words and are used by Raymond et al. [1] to create semantic language models for detecting untruthful reviews. The rationale is that changing a word like "love" to "like" in a review should not affect the similarity of the reviews since they have similar meanings. Feature engineering can have a significant impact on classifier performance. Different studies have used the same datasets, learners, and performance metrics but achieved different results due to different feature engineering methods; [3] and [25] or [23] and [11] are examples. Table 8 reports the performance for some of the studies discussed in this paper and what types of features were used to achieve that value. In studying the various sets of features used in the literature, one of the most notable conclusions is that performance increases through combining multiple types of features, and that using the most relevant and expressive features can make a predictive model more robust [25]. Jindal et al. [21] found that adding additional features (both review centric and reviewer centric) to text features improved performance. It can also be observed in Table 8 that augmenting bigrams with LIWC yields a small performance improvement [3]. Several experiments used the same datasets (built by Ott et al. [3] using AMT) and show that for this dataset, the highest performance is achieved using bigrams and LIWC [3, 11, 12]. As other studies are using unique datasets, or datasets that have been in some way altered, it is difficult to directly compare their results. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory (pp. 92–100). ACM, Madison, WI Competing interests Cite this article Big data
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