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Net sales in the October-December quarter, which includes the all-important holiday season, rose 9% to $137.4 billion. That narrowly missed the $137.6 billion average that analysts had expected, according to Yahoo. The performance was buoyed by sales in Amazon's ad services and cloud computing sector, which compensated for muted consumer retail spending.
One way Amazon will address the growing cost of labor and logistics is by raising Amazon Prime membership. Monthly memberships will jump to $15 from $13, while annual memberships will rise to $139 from $119. The changes, the first since 2018, will begin on Feb. 18 for new members and after March 25 for existing members.
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Our results, which highlight the importance of individual characteristics in understanding recently elevated misinformation campaigns, have similarities and differences with previous findings in the literature. Guess, Nagler, and Tucker13 and Grinberg et al.11 find-similar to our analysis-that it is primarily the more conservative individuals who share fake news. Both articles argue that older cohorts share much more fake news than the younger ones, whereas the opposite is true in our study and in Buchanan's study14. As in the case of Germany, Preston et al.10 identify higher abilities for fake news detection among those with better education. We find that those on the right on the political spectrum share fake news more often than those on the left. Similarly, Allcott and Gentzkow4 argued that, in the 2016 US election, fake news was widely shared in favor of Donald Trump. By contrast, Hopp, Ferrucci, and Vargo12 found partial support (on Facebook, but not on Twitter) to connect ideological extremity (left and right) to fake news sharing.
In Eq. (2), \(Y_{i}\) represents an indicator given a value of 1 if the respondent themselves stated they shared fake news (deliberately or accidentally), and 0 otherwise. The parameters of interest are \(\delta\) and \(\tau\) representing the change in log odds of stating to have shared fake news with a one unit increase in deliberate and accidental fake news sharing respectively. However, to ease interpretation, we provide the results in odds ratios rather than log odds. If the outcome is deliberate fake news sharing, we set the parameter \(\tau\) to zero, and if the outcome is accidental fake news sharing, we set the parameter \(\delta\) to zero. As for the OLS models, we perform all analyses by country and we use HC1 robust standard errors in each specification.
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Our results, which highlight the importance of individual characteristics in understanding recently elevated misinformation campaigns, have similarities and differences with previous findings in the literature. Guess, Nagler, and Tucker13 and Grinberg et al.11 find-similar to our analysis-that it is primarily the more conservative individuals who share fake news. Both articles argue that older cohorts share much more fake news than the younger ones, whereas the opposite is true in our study and in Buchanan's study14. As in the case of Germany, Preston et al.10 identify higher abilities for fake news detection among those with better education. We find that those on the right on the political spectrum share fake news more often than those on the left. Similarly, Allcott and Gentzkow4 argued that, in the 2016 US election, fake news was widely shared in favor of Donald Trump. By contrast, Hopp, Ferrucci, and Vargo12 found partial support (on Facebook, but not on Twitter) to connect ideological extremity (left and right) to fake news sharing.
In Eq. (2), \(Y_{i}\) represents an indicator given a value of 1 if the respondent themselves stated they shared fake news (deliberately or accidentally), and 0 otherwise. The parameters of interest are \(\delta\) and \(\tau\) representing the change in log odds of stating to have shared fake news with a one unit increase in deliberate and accidental fake news sharing respectively. However, to ease interpretation, we provide the results in odds ratios rather than log odds. If the outcome is deliberate fake news sharing, we set the parameter \(\tau\) to zero, and if the outcome is accidental fake news sharing, we set the parameter \(\delta\) to zero. As for the OLS models, we perform all analyses by country and we use HC1 robust standard errors in each specification.
promotion. Amazon said it would not be giving any details about how many people will
without Prime membership. Amazon has had to change its policies on free shipping to
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