Data Science Applications

The dataset's structure and content make it ideal for a variety of data science applications, including:

• Content Analysis: Explore the themes and subjects in the descriptions and titles of the most popular pins.
• Popularity Metrics: Utilize repin counts to measure content virality and audience interest.
• Trend Identification: Identify trending topics and styles among top Pinterest influencers.
• NLP (Natural Language Processing): Analyze textual data for sentiment analysis, keyword extraction, and trend prediction. -Image Analysis : Analyse Images distance, through multiple metrics, Vision Algorithm
• graph Analysis : For clustering and features extraction

Column Descriptors The dataset is concise yet informative, comprising the following columns:

-ID: A unique identifier for each pin, facilitating easy reference and analysis. -Description: The textual description provided for each pin, offering insights into the content and its appeal. -Title: The title of the pin, which may contain key information or keywords relevant to the content. -Repin Count: A quantitative measure of the pin's popularity, indicating how often it has been repinned by users.

Graph description : The nxGraph is composed of following edges metrics :

• Description similarities : Compute with gensim : word2vec-google-news-300 model.
• Title similarities : Compute with gensim : word2vec-google-news-300 model.

For the Nodes metrics :

• Metrics from datasets
• Images Metrics (from sklearn.stats.describe) for each image

Images the images correspond to pin id of the dataset. They are resized to 64x64 pixels.

Acknowledgements

We are thankful to the vibrant community of Pinterest and its top contributors whose creativity and engagement have made this dataset possible. Their dedication to sharing and curating content has offered us a window into the dynamics of social media engagement and content popularity.

Ethically Mined Data This dataset upholds the highest standards of ethical data collection. It has been compiled with respect for user privacy and in alignment with Pinterest's data usage policies. By focusing on publicly available data such as pin descriptions and repin counts, the dataset ensures respect for individual privacy while providing valuable insights for analysis.

Thanks to Oneli WICKRAMASINGHE for releasing this dataset