# Detecting Fake Reviews in Google Maps--A Case Study
### Existing Datasets  
One of the first review datasets was created by [PERSON] and [PERSON] [5]; they used web-scraping methods to obtain 5.8 million reviews from Amazon. Then, they analyzed the newly created dataset and labeled reviews genuine or fake by checking whether their content was repeated elsewhere. While this is a fast method of labeling a dataset of that size, it may produce a lot of false labels, i.e., genuine reviews which happened to repeat and were labeled as fake. Unfortunately, the authors did not publish their dataset. Another approach was taken by [PERSON] et al. [6], where the authors created a dataset of 400 real and 400 fake reviews. Real reviews were collected from TripAdvisor's 20 most popular hotels in American cities. Fake reviews were specially prepared for that study using crowd-sourcing. Crowd workers were tasked with writing either positive or negative reviews for hotels (the same as in authentic reviews) that they had never visited. This approach produced a dataset with no false labels, but the data were artificial and might not have been a good representation of how fake reviewers operate. The dataset was also relatively small (800 entries). Yet another dataset was created by [PERSON] and [PERSON] [7]. They focused on a particular Marriott hotel and collected 40 genuine reviews from TripAdvisor. To acquire fake reviews, they instructed tourism marketing students to write fake reviews aimed at promoting the hotel, and in this way, collected 42 deceptive reviews. Then, they looked for psycho-linguistic differences between the two types of reviews. Unfortunately, the size of their dataset is insufficient for training most ML algorithms. Despite this, this study gives an insight into how fake reviewers may think and how they may construct their reviews. Another noteworthy dataset was obtained by [PERSON] and [PERSON] [8]. Trustpilot, which is one of the largest platforms that allows customers to review a business from which they have purchased a product or service or where they contacted customer service, provided the dataset of 9000 reviews, labeled as fake or real. The dataset contained only reviews with four or five stars. Unfortunately, the dataset was not made public, so no other studies using this dataset were found. An Amazon review dataset was initially published in 2014 [9] and later updated by [PERSON] et al. [10] in 2018. This dataset includes reviews of products on Amazon and associated product metadata (descriptions, category information, price, brand, and image features). The original dataset from 2014 was filtered to exclude duplicated reviews (potentially fake reviews) and contains 83.68 M reviews. The updated version was not filtered and contains 233.1 M reviews. The original dataset contains reviews from May 1996 to July 2014; the updated version extends that to 2018. Another approach was proposed by [PERSON] et al. [11], where they used GPT-2 and ULMFiT models to generate samples of fake reviews. They created a dataset with 20,000 fake reviews generated with GPT-2 and 20,000 real reviews sampled from the Amazon review dataset. The Yelp database is a dataset owned and publicized by Yelp, Inc., which is an American company that gathers and publishes crowd-sourced reviews about businesses. It is a real dataset consisting of nearly 7M reviews and 150k businesses located in 11 metropolitan areas. The dataset is widely used among researchers; its key features are that it is a naturally collected dataset and its sheer size. The dataset is rarely used in its entirety; often, its subsets are used separately. One subset is the Chicago subset (YelpCHI). The Yelp filtering algorithm, which detects \"fake/suspicious\", was used to label all reviews in that set as either fake or genuine. The dataset includes 67,395 reviews of 201 restaurants and hotels written by 38,063 reviewers. The algorithm labeled 13.23% of reviews and 20.33% of reviewers as fake. Eventhough the algorithm was not perfect, its labels are considered \"near ground truth\".