| --- |
| tags: |
| - news |
| - article |
| - links |
| pretty_name: 1m huffpost news article links |
| size_categories: |
| - 1M<n<10M |
| --- |
| # huffpost news article dataset |
| The dataset consists of >1m links to huffpost news articles. They have been crawled for an educational approach to learn how content could be gathered through reading the websites sitemap(s). |
|
|
| The code can be found here [Link](https://github.com/manzke/huffpost-crawler/) |
|
|
| ## huffpost-crawler |
| educational - how to use sitemap.xml for crawling a website (huffpost.com - could be any) |
|
|
| Every public website has (or should have) a sitemap.xml. The sitemap.xml allows robots like Google to find links, which should be crawled. |
|
|
| In the most cases the sitemaps can be found with just adding a /sitemap.xml to the domain or in bigger sites, check the robots.txt (ex. https://www.huffpost.com/robots.txt) |
|
|
| The robots.txt tells you where to find the sitemap.xml and if a robot is allowed to follow the links. A site can also manage to not get indexed, when it has the metatags for nofollow, noindex. |
|
|
| Your crawler should respect them to not read old content, privacy reasons, etc. |
|
|
| What does our crawler do? |
| - it loads the robots.txt |
| - detects the sitemaps |
| - loads every sitemap |
| - gets the urls from each sitemap |
| - which can be either another sitemap or a url |
| - we are loading each sitemap |
| - storing every url which is an entry as well as the skipped ones |
|
|
| This led to 1,121,569 unique links for new entries. |
|
|
| ### robots.txt |
|
|
| A lot of LLM models have been trained on public data. To forbit it, pages has added |
|
|
| User-agent: GPTBot |
| Disallow: / |
|
|
| This tells the GPTBot to not crawl the site. Let's hope it complies with it. |
|
|
| Also take the Crawl-delay into account, which tells you how much time should be between every request to not get banned. |
|
|
| Example robots.txt from huffpost.com |
|
|
| ``` |
| # Cambria robots |
| |
| User-agent: grapeshot |
| Disallow: /member |
| Disallow: /*?*err_code=404 |
| Disallow: /search |
| Disallow: /search/?* |
| |
| User-agent: * |
| Crawl-delay: 4 |
| Disallow: /*?*page= |
| Disallow: /member |
| Disallow: /*?*err_code=404 |
| Disallow: /search |
| Disallow: /search/?* |
| Disallow: /mapi/v4/*/user/* |
| Disallow: /embed |
| |
| User-agent: Googlebot |
| Allow: / |
| Disallow: /*?*err_code=404 |
| Disallow: /search |
| Disallow: /search/?* |
| |
| User-agent: google-extended |
| Disallow: / |
| |
| User-agent: GPTBot |
| Disallow: / |
| |
| # archives |
| Sitemap: https://www.huffpost.com/sitemaps/sitemap-v1.xml |
| Sitemap: https://www.huffpost.com/sitemaps/sitemap-google-news.xml |
| Sitemap: https://www.huffpost.com/sitemaps/sitemap-google-video.xml |
| Sitemap: https://www.huffpost.com/sitemaps/sections.xml |
| |
| # huffingtonpost.com archive sitemaps |
| Sitemap: https://www.huffpost.com/sitemaps-huffingtonpost/sitemap.xml |
| Sitemap: https://www.huffpost.com/sitemaps-huffingtonpost/sections.xml |
| ``` |
|
|
| ### Inspired by |
| ``` |
| @article{misra2019sarcasm, |
| title={Sarcasm Detection using Hybrid Neural Network}, |
| author={Misra, Rishabh and Arora, Prahal}, |
| journal={arXiv preprint arXiv:1908.07414}, |
| year={2019} |
| } |
| |
| @book{misra2021sculpting, |
| author = {Misra, Rishabh and Grover, Jigyasa}, |
| year = {2021}, |
| month = {01}, |
| pages = {}, |
| title = {Sculpting Data for ML: The first act of Machine Learning}, |
| isbn = {9798585463570} |
| } |
| ``` |
