2 datasets found
  1. Twitter Multilabel Classification Dataset

    • kaggle.com
    zip
    Updated Sep 8, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Proksh (2023). Twitter Multilabel Classification Dataset [Dataset]. https://www.kaggle.com/datasets/prox37/twitter-multilabel-classification-dataset/discussion
    Explore at:
    zip(1121625 bytes)Available download formats
    Dataset updated
    Sep 8, 2023
    Authors
    Proksh
    Description

    The file contains 9,921 tweets labelled with the concerns towards vaccines. There are 3 columns in the file: - ID of the tweet in a string format, appended with a "t" (to make it easier to work with on spreadsheet softwares). - The tweet text - The different labels (vaccine concerns) expressed in the tweet, seperated by spaces.

    List of the 12 different vaccine concerns in the dataset: - [unnecessary]: The tweet indicates vaccines are unnecessary, or that alternate cures are better. - [mandatory]: Against mandatory vaccination — The tweet suggests that vaccines should not be made mandatory. - [pharma]: Against Big Pharma — The tweet indicates that the Big Pharmaceutical companies are just trying to earn money, or the tweet is against such companies in general because of their history. - [conspiracy]: Deeper Conspiracy — The tweet suggests some deeper conspiracy, and not just that the Big Pharma want to make money (e.g., vaccines are being used to track people, COVID is a hoax) - [political]: Political side of vaccines — The tweet expresses concerns that the governments / politicians are pushing their own agenda though the vaccines. - [country]: Country of origin — The tweet is against some vaccine because of the country where it was developed / manufactured - [rushed]: Untested / Rushed Process — The tweet expresses concerns that the vaccines have not been tested properly or that the published data is not accurate. - [ingredients]: Vaccine Ingredients / technology — The tweet expresses concerns about the ingredients present in the vaccines (eg. fetal cells, chemicals) or the technology used (e.g., mRNA vaccines can change your DNA) - [side-effect]: Side Effects / Deaths — The tweet expresses concerns about the side effects of the vaccines, including deaths caused. - [ineffective]: Vaccine is ineffective — The tweet expresses concerns that the vaccines are not effective enough and are useless. - [religious]: Religious Reasons — The tweet is against vaccines because of religious reasons - [none]: No specific reason stated in the tweet, or some reason other than the given ones.

  2. World Drug Report 2021 (UNODC)

    • kaggle.com
    zip
    Updated Aug 27, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Craig Chilvers (2022). World Drug Report 2021 (UNODC) [Dataset]. https://www.kaggle.com/datasets/craigchilvers/world-drug-report-2021-unodc
    Explore at:
    zip(320662 bytes)Available download formats
    Dataset updated
    Aug 27, 2022
    Authors
    Craig Chilvers
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    The following dataset is the World Drug Report 2021 produced by the United Nations Office on Drugs and Crime. https://www.unodc.org/unodc/en/data-and-analysis/wdr2021_annex.html

    The Executive Summary: https://www.unodc.org/res/wdr2021/field/WDR21_Booklet_1.pdf

    Special points of interest from the report: - Cannabis has come to be seen as less risky by adolescents from 1995 to 2019, but the herb potency has increased 4x in that time period. - Web-based sales have increased dramatically. - Number of drug users in Africa is projected to rise by 40 per cent by 2030, based on expected population growth in the 15-64 demographic. - Drug markets quickly recovered after the onset of the pandemic, but some trafficking dynamics have been accelerated during Covid-19 - Non-medical use of cannabis and sedatives has increased globally during the pandemic

    On Opioids specifically: - The two pharmaceutical opioids most commonly used to treat people with opioid use disorders, methadone and buprenorphine, have become increasingly accessible over the past two decades. The amount available for medical use has increased sixfold since 1999, from 557 million daily doses in that year to 3,317 million by 2019. - The amounts of fentanyl and its analogues seized globally have risen rapidly in recent years, and by more than 60 per cent in 2019 compared with a year earlier. Overall, these amounts have risen more than twenty-fold since 2015. The largest quantities were seized in North America. - Elsewhere in the world, other pharmaceutical opioids (codeine and tramadol) predominate. Over the period 2015–2019, the largest quantities of tramadol seized were reported in West and Central Africa; they accounted for 86 per cent of the global total. Codeine was intercepted in large quantities in Asia, often in the form of diverted cough syrups. - Almost 50,000 people died from overdose deaths attributed to opioids in the United States in 2019, more than double the 2010 figure. By comparison, in the European Union, the figure for all drug-related overdoses (mostly relating to opioid use) stood at 8,300 in 2018, despite the larger population. - However, the opioid crisis in North America is evolving. The number of deaths attributed to heroin and the non-medical use of pharmaceutical opioids such as oxycodone or hydrocodone has been declining over the past five years. - The crisis is now driven mainly by overdose deaths attributed to synthetic opioids such as fentanyl and its analogues. Among the reasons for the large number of overdose deaths attributed to fentanyls is that the lethal doses of them are often small when compared with other opioids. Fentanyl is up to 100 times more potent than morphine. - The impact of fentanyl is illustrated even further by the fact that more than half of the deaths attributed to heroin also involve fentanyls. Synthetic opioids also contribute significantly to the increased number of overdose deaths attributed to cocaine and other psychostimulants, such as methamphetamine.

  3. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Proksh (2023). Twitter Multilabel Classification Dataset [Dataset]. https://www.kaggle.com/datasets/prox37/twitter-multilabel-classification-dataset/discussion
Organization logo

Twitter Multilabel Classification Dataset

A Comprehensive Multilabel Twitter Classification Dataset

Explore at:
zip(1121625 bytes)Available download formats
Dataset updated
Sep 8, 2023
Authors
Proksh
Description

The file contains 9,921 tweets labelled with the concerns towards vaccines. There are 3 columns in the file: - ID of the tweet in a string format, appended with a "t" (to make it easier to work with on spreadsheet softwares). - The tweet text - The different labels (vaccine concerns) expressed in the tweet, seperated by spaces.

List of the 12 different vaccine concerns in the dataset: - [unnecessary]: The tweet indicates vaccines are unnecessary, or that alternate cures are better. - [mandatory]: Against mandatory vaccination — The tweet suggests that vaccines should not be made mandatory. - [pharma]: Against Big Pharma — The tweet indicates that the Big Pharmaceutical companies are just trying to earn money, or the tweet is against such companies in general because of their history. - [conspiracy]: Deeper Conspiracy — The tweet suggests some deeper conspiracy, and not just that the Big Pharma want to make money (e.g., vaccines are being used to track people, COVID is a hoax) - [political]: Political side of vaccines — The tweet expresses concerns that the governments / politicians are pushing their own agenda though the vaccines. - [country]: Country of origin — The tweet is against some vaccine because of the country where it was developed / manufactured - [rushed]: Untested / Rushed Process — The tweet expresses concerns that the vaccines have not been tested properly or that the published data is not accurate. - [ingredients]: Vaccine Ingredients / technology — The tweet expresses concerns about the ingredients present in the vaccines (eg. fetal cells, chemicals) or the technology used (e.g., mRNA vaccines can change your DNA) - [side-effect]: Side Effects / Deaths — The tweet expresses concerns about the side effects of the vaccines, including deaths caused. - [ineffective]: Vaccine is ineffective — The tweet expresses concerns that the vaccines are not effective enough and are useless. - [religious]: Religious Reasons — The tweet is against vaccines because of religious reasons - [none]: No specific reason stated in the tweet, or some reason other than the given ones.

Search
Clear search
Close search
Google apps
Main menu