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Articles related to "comment"


Censoring toxic comments using fastai v2 with a multi-label text classifier

  • The data we’ll use for demonstrating the process of multi-label text classification is obtained from Toxic Comment Classification Challenge on Kaggle.
  • There’s a construct for Embedding in PyTorch which helps lookup the vector representation for a word given it’s numerical token and that’s followed by other RNN layers and fully connected layers to build an architecture which can take sequence as an input and return a bunch of probabilities as the output.
  • These vector embeddings could be randomly initialized or borrowed from commonly available GLoVE or Word2Vec embeddings which have been trained on a large corpus of text so that they have a good semantic word understanding about context in that particular language in a generic sense.
  • Next, we have to create a dataloader to tokenize these texts, do all the numericalization, padding and preprocessing before feeding it to the language model.

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Michael Caputo fits very well in the Trump administration

  • In a statement to CNN, Caputo said that he made the comments in the video while dealing with pressures stemming from he and his family being harassed recently.
  • As CNN has reported, a whistleblower alleges that acting Department of Homeland Security chief Chad Wolf urged officials to tailor intelligence to fit the President's political agenda by downplaying the threat of Russian interference and instead focus more on gathering information on China and Iran.
  • For example, shortly after Caputo was appointed as the new HHS spokesman in April, CNN reported that he had deleted inflammatory posts from his Twitter account, including one on March 8 that suggested the pandemic was a hoax.
  • Caputo's coronavirus comment seems especially ill conceived and would, with another president, have made him ineligible for a post at HHS, but it marks him as a person perfectly suited for the Trump administration.

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OneFuzz – A self-hosted Fuzzing-As-A-Service platform [from Microsoft]

  • Project OneFuzz enables continuous developer-driven fuzzing to proactively harden software prior to release.
  • With a single command, which can be baked into CICD, developers can launch fuzz jobs from a few virtual machines to thousands of cores.
  • Our source code will drop in sync with our public presentation at CppCon 2020 on September 18th, 2020.
  • For details, visit https://cla.opensource.microsoft.com.
  • When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment).
  • Simply follow the instructions provided by the bot.
  • You will only need to do this once across all repos using our CLA.
  • This project has adopted the Microsoft Open Source Code of Conduct.
  • For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

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Analyze YouTube Discourse and Find Troll Bots with Python

  • With the goal of investigating political discourse on YouTube, I will explain how to use the free YouTube Data API to gather YouTube comments into an interesting dataset.
  • The YouTube Data API allows up to 10,000 requests per day for free to retrieve information on YouTube videos, playlists, comments, and channels.
  • To do this, we will have to go back to the YouTube Data API to retrieve full data on each unique video in the Comments database (this is because the API response for a CommentsThread request provides the videoID for each comment’s video, but not additional information such as the number of likes and dislikes).
  • I wanted to see if there is a way to automatically detect likely bot activity purely based on our comments database.
  • Another red flag is an account posting the same exact comment on multiple different videos.

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Analyze YouTube Discourse and Find Troll Bots with Python

  • With the goal of investigating political discourse on YouTube, I will explain how to use the free YouTube Data API to gather YouTube comments into an interesting dataset.
  • The YouTube Data API allows up to 10,000 requests per day for free to retrieve information on YouTube videos, playlists, comments, and channels.
  • To do this, we will have to go back to the YouTube Data API to retrieve full data on each unique video in the Comments database (this is because the API response for a CommentsThread request provides the videoID for each comment’s video, but not additional information such as the number of likes and dislikes).
  • I wanted to see if there is a way to automatically detect likely bot activity purely based on our comments database.
  • Another red flag is an account posting the same exact comment on multiple different videos.

save | comments | report | share on


Analyze YouTube Discourse and Find Troll Bots with Python

  • With the goal of investigating political discourse on YouTube, I will explain how to use the free YouTube Data API to gather YouTube comments into an interesting dataset.
  • The YouTube Data API allows up to 10,000 requests per day for free to retrieve information on YouTube videos, playlists, comments, and channels.
  • To do this, we will have to go back to the YouTube Data API to retrieve full data on each unique video in the Comments database (this is because the API response for a CommentsThread request provides the videoID for each comment’s video, but not additional information such as the number of likes and dislikes).
  • I wanted to see if there is a way to automatically detect likely bot activity purely based on our comments database.
  • Another red flag is an account posting the same exact comment on multiple different videos.

save | comments | report | share on