5 datasets found
  1. U.S. consumers confident in having seen fake reviews on Amazon 2024

    • statista.com
    Updated Jun 24, 2025
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    Statista (2025). U.S. consumers confident in having seen fake reviews on Amazon 2024 [Dataset]. https://www.statista.com/statistics/997026/amazon-shopping-categories-largest-share-fake-product-reviews/
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    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2024, ** percent of U.S. consumers answering a survey were confident in having seen fake product reviews on Amazon. Although the number might seem very high, the figure has decreased compared to 2023, when ** percent of respondents stated the same.

  2. Leading health disinformation websites worldwide 2019-2020

    • statista.com
    Updated Apr 28, 2022
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    Statista (2022). Leading health disinformation websites worldwide 2019-2020 [Dataset]. https://www.statista.com/statistics/266916/top-health-disinformation-websites-visits/
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    Dataset updated
    Apr 28, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 28, 2019 - May 27, 2020
    Area covered
    Worldwide
    Description

    Medical misinformation relies on an ecosystem of actors as the global spread of health misinformation is encouraged by social media algorithms. According to industry estimates, top-ranked health misinformation spreader Realfarmacy.com accumulated an approximate 253.6 million views between May 2019 and May 2020. The top medical misinformation pages were identified by analyzing website credibility reviews based on credibility and transparency criteria.

  3. P

    IMDb Movie Reviews Dataset

    • paperswithcode.com
    Updated Dec 20, 2013
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    Andrew L. Maas; Raymond E. Daly; Peter T. Pham; Dan Huang; Andrew Y. Ng; Christopher Potts (2013). IMDb Movie Reviews Dataset [Dataset]. https://paperswithcode.com/dataset/imdb-movie-reviews
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    Dataset updated
    Dec 20, 2013
    Authors
    Andrew L. Maas; Raymond E. Daly; Peter T. Pham; Dan Huang; Andrew Y. Ng; Christopher Potts
    Description

    The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly polarizing reviews are considered. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. No more than 30 reviews are included per movie. The dataset contains additional unlabeled data.

  4. Most common counterfeits on cross-border e-commerce platforms in China 2019,...

    • statista.com
    Updated Dec 20, 2024
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    Statista (2024). Most common counterfeits on cross-border e-commerce platforms in China 2019, by type [Dataset]. https://www.statista.com/statistics/1120896/china-most-common-counterfeits-on-cross-border-ecommerce-platforms-by-category/
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    Dataset updated
    Dec 20, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2019
    Area covered
    China
    Description

    According to a survey about cross-border e-commerce platforms and overseas shopping users in China in 2019, 45.5 percent of respondents stated that they had purchased fake toiletries, which was the most common counterfeit product category encountered by Chinese overseas shopping users on cross-border e-commerce platforms. Over 39 percent of respondents stated that they had bought fake beauty and makeup products.

  5. SNL share and ratings in the U.S. 2018

    • statista.com
    Updated Jan 27, 2021
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    Statista (2021). SNL share and ratings in the U.S. 2018 [Dataset]. https://www.statista.com/statistics/785262/snl-ratings-network-share/
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    Dataset updated
    Jan 27, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 30, 2017 - May 19, 2018
    Area covered
    United States
    Description

    Saturday Night Live’s 850th episode, starring Tina Fey, aired on May 19, 2018 and received a 4.5 household rating along with an 11 percent household share. Some of the comedy sketch show’s most popular segments include comedic representations of popular politicians, such as Alec Baldwin’s impression of Donald Trump - a sketch which was seen by over 60 percent of adults in the United States.

    Saturday Night Live’s lasting popularity

    The show, currently in its 44th season, remains a fixture of American television. Even after over 850 episodes, SNL remains one of the most in-demand television shows in the United States. Some of America’s successful comedians have made a name for themselves as SNL cast members, and weekly guest appearances from celebrities and musicians provide viewers with a unique glimpse of modern pop culture.

    SNL in politics

    Produced as a variety comedy series, SNL has found itself increasingly present in political discussions since the election of Donald Trump. The president’s comments regarding the show, its ratings, and accusations of collusion have made opinions regarding the show increasing politically divisive. Republicans view the show far less than democrats, and brand the show as one of the most likely sources of fake news in the U.S.

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Statista (2025). U.S. consumers confident in having seen fake reviews on Amazon 2024 [Dataset]. https://www.statista.com/statistics/997026/amazon-shopping-categories-largest-share-fake-product-reviews/
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U.S. consumers confident in having seen fake reviews on Amazon 2024

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2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 24, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
United States
Description

In 2024, ** percent of U.S. consumers answering a survey were confident in having seen fake product reviews on Amazon. Although the number might seem very high, the figure has decreased compared to 2023, when ** percent of respondents stated the same.

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