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One of the reasons I'm quick to recommend the Kids Edition versions of Amazon's Kindle, Fire Tablet, and Echo Dot is because they're bundled with a year of access to Amazon FreeTime. FreeTime 2 Want help creating your content business? Subscribe to The Tilt, a free twice-a-week newsletter helping content entrepreneurs start and grow successful businesses. And join us at the conference for serious content creators, Creator Economy Expo, in May. As part of the program, Wattpad is awarding up to $25K stipends for 500 eligible creators in 2022. But it's also inviting 2K more creators to the program. All receive mentorship, marketing support, writing resources, and other opportunities. A minimum number of "engaged readers" is required to participate. That threshold number varies based on the genre. First, meta data of the behavior of the reviewer can be used to check the fake probability of reviews [2, 6, 8, 9]. For instance, if the reviewer writes a lot of hotel reviews in a short time period, this might help to identify fake reviews. Besides meta data, a fake detection can be carried out for example by investigating the writing style, grammar, spelling etc. [10]. Special data sources for training of fake classification models are provided by Yelp.de and through research using crowdsourcing approaches to write fake reviews [4, 10]. Furthermore, research focuses on the comparison of different textual styles between fake and non-fake classified texts [2, 4, 11]. The specific use of some phrases or special spelling issues can lead to the detection of fake reviews, too. Further, there are some online platforms (e.g., reviewmeta.com) providing assistance to identify fake reviews from Amazon. However, flexible hotel review-specific approaches applying different approaches with a scientifical base are still missing. Thus, the research wants to address this gap by providing a flexible fake detection system based on different adjusted approaches. A flexible hotel fake detection system - called HOTFRED - was implemented within a first prototype according to general recommendations coming from previous research [13]. The prototype focuses on the main components to collect data on two major analytical components (1) text mining-based classification and (2) spell checker as well as the scoring system to provide the user (here: Tourist) an aggregated, comprehensive information. A web crawler tool [21] was developed in Python to collect the review data from tripadvisor.com. The web crawler has to collect different data of the hotel (e.g., name, URL, class) and the review (e.g., date, review text, points) to run a proper fake review detection and related analysis. After data receiving via HTTPS, it is stored for further analysis within a MySQL database. As a first analytical component (1) a text mining-based fake detection approach was implemented according to the general text pre-processing recommendations [14]. Following, classified fake review data from Yelp was used as a data source for training the classification model [2]. This data set consists of pre-labeled examples regarding the filtered fake characters of hotel reviews written in English. Approximately, 14% of the data can be seen as filtered fake reviews. Existing research already used and validated this data source for e.g. validations [2]. After the evaluation of different classification algorithms (e.g., Support Vector Machines, Naïve Bayes Classifier, KNN), the Support Vector Machine has been chosen as a good fake review classifier based on the accuracy of the classification (e.g., combined metrics like precision, recall, F-score, etc.). For the second analytical component, (2) a spelling checker software tool was developed. This detection component of the system recognizes spelling mistakes based on the ideas of the Levenshtein Distance [15]. The software was programmed in Python. Therefore, the Python library pyspellchecker was used. The scoring system component can use the individual results of the finished analytical components to show a summarized view about the fake probabilities of the reviews for the given hotel. amazon review program
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