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This is the bettor's answer to "how many points would you have to spot this bad team in order for them to beat a better team?" Let's say the favorite in an NFL game is favored to win by a touchdown; they'd have a -7 distinction on the betting line. 5 means they could still lose on the scoreboard but win at the sportsbook, but only if the final margin is fewer than eight points. The profit you'd return for betting on straight up wins and losses For favorites, it tells you how much you'd need to bet in order to win $100. The total is the projected total amount of points, goals, runs, etc. , scored in a game. Bet the over if you think the final score will be more. Gannett may earn revenue from Tipico for audience referrals to betting services. faux saint laurent bag
What Happened Indeed There were good reasons to view the flood of negative reviews as suspicious. Only 20% of the reviews had a verified purchase and the ratio of 5-star to 1-star reviews – 44%-51% – was highly irregular; the vast majority of products reviewed on Amazon.com display an asymmetric bimodal (J-shaped) ratings distribution (see Hu, Pavlou and Zhang, 2009), in which there is a concentration of 4 or 5 star reviews, a number of 1-star reviews and very few 2 or 3 star reviews. The charts in Figure 2 below, originally featured in this QZ article, show the extent to which 'What Happened' was initially a ratings and purchase pattern outlier. Figure 2: Two charts indicating the unusual reviewing behaviour for 'What Happened'. Source: Ha, 2017 It would appear that Amazon have taken on board the academic literature suggesting that burstiness is a feature of review spammers and deceptive reviews (e.g. this excellent paper by Geli Fei, Arjun Mukherjee, Bing Liu et al. ) and that it is right to interpret a rush of consecutive negative reviews close to a book launch as suspicious. But what about the subsequent burst of 600+ positive reviews? One might expect the Clinton PR machine to mobilize its own 'positive review brigade' in anticipation of , or in response to, a negative 'astroturfing' campaign against her book. One could even argue that it would be foolish not to manage perceptions of such a controversial and polarising book launch. If positive review spam is identified, should it also be deleted? If there is a text cluster that correlates with 'burstiness' – i.e. occurs more frequently in the reviews closest to the book launch date and/or occurs repeatedly within a short time frame – then that would suggest there are specific linguistic styles and/or strategies correlated with this deceptive reviewing behaviour. The existence of such a distinct deception cluster would strongly suggest that Clinton's PR team gamed the Amazon review system (understandably, in order to counter the negative campaign against the book). Alternatively, different reviewing strategies might be distributed randomly across the review corpus and unrelated to its proximity to the book launch date. This would weaken the argument that linguistic variation in the reviews is a potential deception cue. The two scenarios are illustrated in Figure 4 below: Deception cluster hypothesis My prediction? Surely, Hillary Clinton's PR team would not so be so brazen as to solicit fake positive reviews in bulk and in an organised fashion. Yes, there were a disproportionate number of reviews written in the first few days but I believe this was a spontaneous groundswell of genuine support. I do expect there to be a few different types of linguistic review style, reflecting the different ways in which books can be reviewed (e.g. focus on book content; retell personal reading experience; address the reader – these are some of the review styles I presented at the ICAME39 (2018) conference in Tampere). However, if the support is spontaneous I would expect these review styles not to be correlated with burstiness or other deceptive phenomena but to occur randomly throughout the month. faux saint laurent bagchanel 19 flap bag small
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