{ "info": { "author": "Alexander L. Hayes (@batflyer)", "author_email": "alexander@batflyer.net", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Other Audience", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3" ], "description": "# Collaborative Filtering in FanFiction Networks\n\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ffscraper.svg?style=flat-square) ![PyPI](https://img.shields.io/pypi/v/ffscraper.svg?style=flat-square) ![license](https://img.shields.io/pypi/l/ffscraper.svg?style=flat-square)\n\n\"ffscraper\" Yet another set of scraping tools for FanFiction.Net\n\n**Alexander L. Hayes** ([@batflyer](https://github.com/batflyer))\n\n### Installation\n\n`pip install ffscraper`\n\nRequires: `bs4`, `requests`\n\n### Background\n\nFanFiction.Net was established in 1998 and is among the world's largest collection of user-submitted fanfiction (works of fiction authored by fans of existing stories, such as movies, books, or TV shows). The large amount of easily-available user content has drawn interest from those interested in analyzing the content and creative differences between original works and their fanfiction derivatives [1]. More recently, [2] created an anonymized dataset of the metadata from fanfiction sources.\n\nThis repository's purpose is twofold: creating robust open-source tools for scraping content, and using that content to build open-source systems which can be used by the FanFiction.Net community.\n\n### References\n\n* [1] Milli, Smitha and David Bamman, \"Beyond Canonical Texts: A Computational Analysis of Fanfiction.\" Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.\n* [2] Yin, K., Aragon, C., Evans, S. and Katie Davis. \"Where No One Has Gone Before: A Meta-Dataset of the World's Largest Fanfiction Repository.\" Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2017.\n\n### Attribution\n\n* This was originally part of a final project for Professor Vibhav Gogate's Spring 2018 [Advanced Machine Learning](http://www.hlt.utdallas.edu/~vgogate/ml/2018s/index.html) class at the University of Texas at Dallas. This version of the code, TeX, and .pdf are tagged as v0.1.0.\n* [monochrome](https://github.com/dyutibarma/monochrome) is a Jekyll theme by [@dyutibarma](https://github.com/dyutibarma/). 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