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Tim Ball, who delivered packages for Amazon until quitting this year, said he prayed the same prayer every time he walked up to a home that felt "sketchy" in rural Texas. He said he'd see confederate flags, or signs that warned of shooting trespassers. He said he felt most fearful when delivering packages at night, sometimes working hours after Amazon's competitors had ceased deliveries for the day. Lisa Saenz, owner of LMH Logistics, which delivers bulk items near Raleigh North Carolina, told CNN Business that her staff had suggested sending out its clearly branded Amazon trucks first each morning. Saenz's initial deliveries often happen before dawn.

Tim Ball, who delivered packages for Amazon until quitting this year, said he prayed the same prayer every time he walked up to a home that felt "sketchy" in rural Texas. He said he'd see confederate flags, or signs that warned of shooting trespassers. He said he felt most fearful when delivering packages at night, sometimes working hours after Amazon's competitors had ceased deliveries for the day. Lisa Saenz, owner of LMH Logistics, which delivers bulk items near Raleigh North Carolina, told CNN Business that her staff had suggested sending out its clearly branded Amazon trucks first each morning. Saenz's initial deliveries often happen before dawn.

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Tim Ball, who delivered packages for Amazon until quitting this year, said he prayed the same prayer every time he walked up to a home that felt "sketchy" in rural Texas. He said he'd see confederate flags, or signs that warned of shooting trespassers. He said he felt most fearful when delivering packages at night, sometimes working hours after Amazon's competitors had ceased deliveries for the day. Lisa Saenz, owner of LMH Logistics, which delivers bulk items near Raleigh North Carolina, told CNN Business that her staff had suggested sending out its clearly branded Amazon trucks first each morning. Saenz's initial deliveries often happen before dawn.

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be an issue to the list of our favourite "I mean you really do not feel like something on the show. The most common-off TV shows in the world were data,

be an issue to the list of our favourite "I mean you really do not feel like something on the show. The most common-off TV shows in the world were data,

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Fake review detection: We have used both supervised and unsupervised methods for the task. Three main types of features or signals are: Opinion Spam: Opinion spamming refers to "illegal" activities (e.g., writing fake reviews) that try to deliberately mislead readers or automated opinion mining and sentiment analysis systems by giving undeserving positive opinions to some target entities in order to promote the entities and/or by giving false negative opinions to some other entities in order to damage their reputation. Opinion spam comes in many forms, e.g., fake reviews (also called bogus reviews), fake comments, fake blogs, fake social network postings, deceptions, and deceptive messages. Manually spotting such postings is very hard, but there are several pages on the Web (see below) which tell people how to spot fake reviews and deceptive messages. To the best of our knowledge, our group was the first in academia to conduct the research to detect fake reviews and reviewers. Our first paper was published in 2007, and subsequent papers were published in 2008 and 2010. My textbook Web Data Mining also has a section in Chapter 11 discussing the issue (Springer, Second Edition, July 2011; First Edition, Dec, 2006). The objective of our current project is to detect fake reviews. We have not worked on detecting other forms of spam opinions.

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Fake review detection: We have used both supervised and unsupervised methods for the task. Three main types of features or signals are: Opinion Spam: Opinion spamming refers to "illegal" activities (e.g., writing fake reviews) that try to deliberately mislead readers or automated opinion mining and sentiment analysis systems by giving undeserving positive opinions to some target entities in order to promote the entities and/or by giving false negative opinions to some other entities in order to damage their reputation. Opinion spam comes in many forms, e.g., fake reviews (also called bogus reviews), fake comments, fake blogs, fake social network postings, deceptions, and deceptive messages. Manually spotting such postings is very hard, but there are several pages on the Web (see below) which tell people how to spot fake reviews and deceptive messages. To the best of our knowledge, our group was the first in academia to conduct the research to detect fake reviews and reviewers. Our first paper was published in 2007, and subsequent papers were published in 2008 and 2010. My textbook Web Data Mining also has a section in Chapter 11 discussing the issue (Springer, Second Edition, July 2011; First Edition, Dec, 2006). The objective of our current project is to detect fake reviews. We have not worked on detecting other forms of spam opinions.

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Welcome Bonus: This card does not offer a welcome bonus. For example, say you own the Discover it® Cash Back and Amazon is one of the bonus categories that quarter, earning you 5% cash back up to the quarterly maximum of $1,500 on spending when you activate. After you hit that $1,500 cap, any other spending at Amazon will only earn you 1% cash back. But, if you use a card like the Citi Double Cash which earns a net 2% cash back-1% when you make a purchase and another 1% when you pay the bill, you could beat what you'd earn if you just paid with the Discover it® Cash Back depending on how much you spend.

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