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Context

I thought it would be a good case study to do anylytics on one of my whatsapp chating groups

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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In 2021, WhatsApp's user base in India amounts to approximately ****** million users. The number of WhatsApp users in India is projected to reach ****** million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users by WhatsApp and the action taken by it. The data compiled is based on the monthly transparency reports published by WhatsApp in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received include Account Support, Safety, Ban Appeal, Other Support and Product Support. The action taken includes the accounts actioned on the basis of grievances received.

Notes:

1. ‘Accounts Actioned’ denotes reports where WhatsApp took remedial action based on the report. Taking action denotes either banning an account or a previously banned account being restored as a result of the complaint.
2. ‘Other’ support pertains to requests which are not consistently classifiable
3. ‘N/A’ denotes grievance topics where it’s generally not applicable to action an account. In some rare cases, tickets may be later reassigned to another topic, and an action could arise from the re-assignment.

In 2021, WhatsApp's user base in Russia amounts to approximately ***** million users. The number of WhatsApp users in Russia is projected to reach ***** million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

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A Dataset of Fact-Checked Images Shared on WhatsApp during the Indian Elections

Description

Recently, messaging applications, such as WhatsApp, have been reportedly abused by misinformation campaigns, especially in Brazil and India. A notable form of abuse in WhatsApp relies on several manipulated images and memes containing all kinds of fake stories. In this work, we performed an extensive data collection from a large set of WhatsApp publicly accessible groups and fact-checking agency websites. This paper opens a novel dataset to the research community containing fact-checked fake images shared through WhatsApp for two distinct scenarios known for the spread of fake news on the platform: the 2018 Brazilian elections and the 2019 Indian elections.

Paper

Please go through the paper https://misinforeview.hks.harvard.edu/article/images-and-misinformation-in-political-groups-evidence-from-whatsapp-in-india/ In this paper you can see how the authors used Machine Learning to tackle to detect misinformation shared during the 2019 Indian elections over WhatsApp.

Motive

You may rember about the The Facebook–Cambridge Analytica data breach , how the public data has been used for manipulating the mindset of the voter. So, by sharing this type of fake images change the mindset of the voter and it may cause altering the result of elections. I want to see how kagglers use this dataset to tackle the most important problem of sharing misinformation over social media.

Upvote

Please upvote this dataset for better reach.

Citation

@dataset{julio_c_s_reis_2020_3779157,
 author    = {Julio C. S. Reis and
         Philipe Melo and
         Kiran Garimella and
         Jussara M. Almeida and
         Dean Eckles and
         Fabrício Benevenuto},
 title    = {{A Dataset of Fact-Checked Images Shared on 
          WhatsApp during the Brazilian and Indian Elections}},
 month    = apr,
 year     = 2020,
 publisher  = {Zenodo},
 doi     = {10.5281/zenodo.3779157},
 url     = {https://doi.org/10.5281/zenodo.3779157}
}

ngthquoczinh/bluesky-whatsapp-bot-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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Dataset Card for ivrit.ai Whatsapp Eval

Evaluation dataset of Hebrew Whatsapp voice-messages, expert-transcribed.

  Dataset Details





  Dataset Description

This dataset containeד Whatsapp hebrew voice recordings gathered around April 2025. The recordings are of volunteer native hebrew speakers using consumer devices in natural environments. Each recording is by a single speaker about a random topic of their choice spoken in a non-scripted natural manner. Each… See the full description on the dataset page: https://huggingface.co/datasets/ivrit-ai/eval-whatsapp.

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Dataset Card for Hmar Language Conversations

This dataset contains anonymized WhatsApp group chat data, primarily in Hmar, a language spoken by the Hmar community. The dataset has been cleaned to remove sensitive information and includes messages exchanged in various WhatsApp groups. The data is intended for research in Natural Language Processing (NLP) and machine learning tasks focused on Hmar language analysis.

  Dataset Details







  Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/hmar-lang/hmar-language-conversations.

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