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With the passage of Amendment 3, any efforts to expand gambling in Florida anywhere other than at tribal casinos must first be approved via statewide referendum and receive a 60% yes vote to take effect. Jai alai maintained a strong presence in Florida through the 1970s but entered a steady decline in the 1980s amid increasing competition from professional sports teams, the lottery, and South Florida casinos. Together, the three bills outlined how Florida sports betting would work, established a tax rate, and set licensing fees for operators. The proposal would place the Florida Lottery in charge of sports betting, including issuing licenses to online sports betting operators. Online Horse Racing Betting in Florida In November 2018, Florida voters approved Amendment 13 to end greyhound racing.

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Spam online reviews, which are similar to opinion spam in some certain situations, refer to violation activities, such as writing spam reviews, that try to confuse consumers with imitating real buyers. Furthermore, malicious merchants hire the real user to post spam reviews directly. Above all of these increased the difficulty of defining spam reviews. Jindal and Liu [4] make some contributions on spam review detection, and first generally proposed three kinds of definition of online reviews: In this paper, we integrate the untruthful opinions and the fraud one comes from the other two types of reviews, and uniformly called as "spam reviews". Spam audits are conflicting with the genuine assessment of items and attempt to deceive per users or intentionally overestimate or belittle one category things. The source of spam reviews might come from malicious merchants, individual spammers and fraud groups. Spam reviews take the form of various patterns designed by spammers [5, 6]. For occasion, the taking after has appeared two spam surveys was writing to Amazon review platform, which was identified with a model survey spam discovery framework [7]. After observing from "Review 1", off track betting online is troublesome for human per users to decide whether the audit is spam or generous. Fortunately, on the off chance that a per user finds these two reviews at the same time, he/she will be able to capture the fundamental spam include to classify these two as spam reviews, due to both of them have settled semantic design almost diverse items. Clearly, the manual approaches of identifying spam audits are not attainable for this event. The increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers' purchase choices in e-commerce, yet many online reviews are intentionally created to confuse or mislead potential consumers. Moreover, driven by product reputations and merchants' profits, more and more spam reviews were inserted into online platform. This kind of reviews can be positive, negative or neutral, but they had common features: misleading consumers or damaging reputations. In the past decade, many people conducted research on detecting spam reviews using statistical or deep learning method with various datasets. In view of that, this article first introduces the task of spam online reviews detection and makes a common definition of spam reviews. Then, we comprehensively conclude the existing method and available datasets. Third, we summarize the existing network-based approaches in dealing with this task and propose some direction for future research. 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