WeRateDogs Data Analysis and Impact on Animal Rescue

WeRateDogs is a social media account that rates dogs with a humorous twist. The account's ratings are based on a 10-point scale, with the goal of highlighting the unique personality of each dog.

The data behind WeRateDogs is massive, with over 50,000 tweets analyzed in the dataset. This analysis was used to identify trends and patterns in the ratings.

The ratings were found to be highly correlated with the dog's breed, with certain breeds consistently receiving higher ratings. For example, Golden Retrievers were found to be one of the highest rated breeds.

WeRateDogs' data has also been used to predict the likelihood of a dog being adopted from a shelter. By analyzing the ratings and characteristics of dogs in shelters, the data suggested that dogs with higher ratings were more likely to be adopted.

See what others are reading: Dogs at Polling Stations

The WeRateDogs Twitter archive contains over 5000 tweets with basic data, but it's missing some important information. One column it does have is the text of each tweet, which can be used to extract additional data.

The archive contains 5000+ tweets, but only 2356 of them have ratings. This is because the archive was filtered for tweets with ratings only.

You can use the Dogtionary to learn about the various stages of dog, including doggo, pupper, puppo, and floof(er). This is helpful because the archive contains these stages, but they may not be accurate.

The Twitter API can be used to gather additional data, such as retweet count and favorite count, but it's limited to the 3000 most recent tweets.

Here are the columns that are available in the WeRateDogs Twitter archive:

Note that the ratings, dog names, and dog stages may not be accurate, so they will need to be assessed and cleaned before using them for analysis or visualization.

We're going to dive into the world of WeRateDogs, a Twitter account that's gained a massive following by rating dog pictures. The Twitter archive is available, but it only contains basic tweet information, which isn't enough for interesting and trustworthy analyses and visualizations.

To get more out of this data, we need to gather additional information, assess it, and clean it up. This process is crucial for creating "Wow!"-worthy analyses and visualizations.

WeRateDogs has made a significant impact on the Twitter community. As of December 2020, the account had nearly 9 million followers.

Their posts can go viral easily, with 30,000 likes being a common occurrence. Nelson's most popular post, of a dog marching in the 2017 Women's March, was retweeted over 50,000 times and favorited 134,000 times.

The account's language has helped popularize the "DoggoLingo" Internet language, where dogs are referred to as doggos and puppers. This unique way of speaking has become a staple of the Weird Twitter community.

A notable interaction on the account in 2016 sparked the catchphrase "They're good dogs, Brent", which became one of the biggest memes of 2016.

Take a look at this: Cultural Depictions of Dogs

WeRateDogs, a popular Twitter account, faced copyright issues when it was closed twice due to spurious Digital Millennium Copyright Act complaints.

The complaints were brought by competitors and unknown accounts, raising serious questions about the ease of abusing the copyright claim process.

This ordeal highlights the vulnerability of online content to removal by pranksters exploiting the system.

The Washington Post noted that this incident has raised concerns about the integrity of the internet, where content can be easily removed by those who abuse the copyright claim process.

WeRateDogs' owner Nelson and his fans were left wondering if it's too easy for pranksters to successfully remove content from the internet.

The Dogs Data Analysis Project is a fun and challenging task that requires you to dive into the world of data analysis using Python and various libraries. To complete this project, you'll need to download a few files, which we'll cover in a moment.

You'll start by downloading the WeRateDogs Twitter archive, a file that contains all the data you'll need for this project. You can find this file by clicking on a link provided in the project instructions.

The project requires you to gather data from three sources: the WeRateDogs Twitter archive, a file called image_predictions.tsv, and the Twitter API. The image_predictions.tsv file contains tweet image predictions, which are predictions of what breed of dog (or other object, animal, etc.) is present in each tweet according to a neural network.

To gather data from the Twitter API, you'll need to use Python's Tweepy library and query the API for each tweet's JSON data using the tweet IDs in the WeRateDogs Twitter archive. You'll store each tweet's JSON data in a file called tweet_json.txt.

Here's a step-by-step guide to gathering the necessary data:

If you're working in the Project Workspace, you can upload files to the Jupyter Notebook Workspace by clicking the "Upload" button in the top right-hand corner of the dashboard.