Datasets:
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license: cc-by-nc-sa-4.0
size_categories:
- 1K<n<10K
source_datasets:
- reddit
task_categories:
- text-classification
- summarization
- feature-extraction
- token-classification
- sentence-similarity
- text-to-speech
- text-to-audio
- text2text-generation
task_ids:
- multi-class-classification
pretty_name: Pill Ideologies Posts
tags:
- natural-language-understanding
- ideology classification
- text classification
- natural language processing
dataset_info:
- config_name: default
features:
- name: subreddit
dtype: string
- name: post_id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: score
dtype: int32
- name: author
dtype: string
- name: date
dtype: int64
- config_name: first_domain
features:
- name: subreddit
dtype: string
- name: id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: score
dtype: int64
- name: author
dtype: string
- name: date
dtype: string
- name: subreddit_subscribers
dtype: int32
- name: num_comments
dtype: int32
- name: ups
dtype: int32
- name: downs
dtype: int32
- name: upvote_ratio
dtype: float32
- name: num_reports
dtype: string
- name: is_video
dtype: bool
splits:
- name: train
num_bytes: 8365101
num_examples: 5123
- name: validation
num_bytes: 2052934
num_examples: 1281
- name: test
num_bytes: 1129446
num_examples: 712
download_size: 11365843
dataset_size: 11547481
- config_name: second_domain
features:
- name: subreddit
dtype: string
- name: id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: score
dtype: int64
- name: author
dtype: string
- name: date
dtype: string
- name: subreddit_subscribers
dtype: int32
- name: num_comments
dtype: int32
- name: ups
dtype: int32
- name: downs
dtype: int32
- name: upvote_ratio
dtype: float32
- name: num_reports
dtype: string
- name: is_video
dtype: bool
splits:
- name: train
num_bytes: 8365101
num_examples: 5123
- name: validation
num_bytes: 2052934
num_examples: 1281
- name: test
num_bytes: 1129446
num_examples: 712
download_size: 11365843
dataset_size: 11547481
Dataset Card for Pill Ideologies - Post Titles
This dataset aims to be a tool to help identify linguistic patterns and glean insights from the reddit posts from members who partake in the internet centric pill ideologies, known as blackpill, red pill, blue pill. It is strictly meant for academic use to help understand the polarity between men and women today in the United States, NOT for commercial use in any context or circumstance.
Dataset Details
Dataset Description
A few of the major groups' posts have been coalesced into one dataset, all from different years. There are more than 1,000 posts per the major pill groups on reddit (red pill, blue pill, black pill). These are all the subreddits used for the scraping : "theredpillrebooted", "RedPillWomen", "marriedredpill", "RedPillWives", "askMRP", "TheBluePill","PurplePillDebate","Feminism", and "ForeverAloneWomen".
The groups of Feminism and Forever Alone Women were added as a juxtaposition against red pill women, in oder to allow researchers to explore the dichotomies between female groups. In the case of the Feminism subreddit, it can sometimes appear similar to the blue pill reddit in language, and Forever Alone Women are proxies for female incels (involuntary celibates), acting as both linguistic mirrors to both the red pill and blue pill, depending on which language they adopt. For researchers, the value will be in identifying or classifying the types of words that serve as identifiers of one ideology more than another.
- Curated by: [steamcyclone] (Eric Rios)
- Funded by [optional]: [No one, get me funding to research this]
- Shared by [optional]: [steamcyclone and reddit users]
- Language(s) (NLP): [EN]
- License: [CC]
Dataset Sources [optional]
Uses
The main usage of this dataset is to study linguistic patterns. Running models and detecting word usage per groups, as well as overlaps across groups, are ideal uses for this dataset. With the rise of the loneliness epidemic, any insights that come from this are welcome.
Here is an example analysis notebook showing what can be done with this type of data.
Example : [https://colab.research.google.com/drive/1ELsp4ccdJgAi6R3FH8e5oj1KNllZmZEz?usp=sharing]
Direct Use
The suitable use cases are to multi-class classification, word clustering or semantic clustering per different groups, summarization modeling, text parsing, and any other natural language processing task.
Out-of-Scope Use
This dataset is not meant to be utilized to demonize or mock certain online communities for the trials in life in which individuals find themselves. If the user's motive is to push forward some misandrist or misogynistic agenda, please ignore this dataset and kindly let yourself out the door.
Dataset Structure
Currently, this dataset contains
- subreddit of the post : string,
- postid : string
- title of the post: string
- text of the post (where applicable) : string
- url (if something was embedded) : string
- score : int32
- date : float64
- subreddit_subscribers: int64
- num_comments: int64
- ups: int64
- downs: int64
- upvote_ratio : float64
- is_video: bool
Dataset Creation
Context of the Pill Ideologies
With the rise of the male loneliness epidemic and the radicalization of internet content pitting men and women against each other, it is important to seek understanding about the roots of the problem. Depending on whom you ask, you'll get a plethora of answers. Jordan Peterson describes it as some type of post-modernist feminist liberalism problem. The Andrew Tates and other conservative archetypes blame the loss of traditionalism. Others blame dating apps and its selection bias effects. The answer may be a combination of these or somewhere in the middle.
More specifically, within each of the major pill ideologies, with the exception of the BlackPill, in the most extremist and mild settings, men blame women to some or large degrees, and women blame men to large degrees. As for the latter, it is very common to witness social media trends of women expressing distaste and dissapointing in men, and this has been ocurring for a few years.
As a reaction to this treatment, poor dating outcomes, and poor life outcomes, men and boys from all walks of life sought guidance and self-improvement. In response to this need, the Red Pill was born on the internet, most prominently on Reddit (before being banned), and it specialized in combining informartion from various sources to boost dating outcomes via the understanding of female nature, self-improvement (image and hygiene and career), and social skills. Its main demographic has been lonely men, a unique group of disavowed people who have very little research to understand them. Unfortunately, in recent years, there has been a rise of extremist blue pill ideologies, associated with misandrist speech (women who belittle men), and extremist red pill misogynists (men who belittle women).
As for Black Pill, it seeks to understand truth through bodies of research. That is their claim.
It has become quite difficult to isolate less extreme variants of the ideologies from the base variants, and it has also become difficult to sustain academic conversations regarding these topics due to public scrutiny. We have to start somewhere, as can be evidenced by the efforts of all sorts of psychiatrists (Dr. K, Jordan Peterson) and scientists/researchers (Dr. Tali Sharot, Prof. Scott Galloway) around the world.
Curation Rationale : Why This Dataset?
Now more than ever, polarization is a topic that has gone beyond politics and is now deeply embedded in dating dynamics(which have also become proxies for politics - conservative/liberal dynamics). To make matters worse, especially in the case of male spaces, as substantiated by research and media coverage in recent years, have only been able to exist on the internet due to scrutiny and silencing of male voices, and counter-spaces have emerged to challenge the views held in the differing ideologies. The same extends to the other groups, where speaking publicly on such matters earns weird looks at best and public shame and social exile at worst. In the current social climate, the dominant ideology is most commonly labeled as mild blue pill, occassionally with a tinge of Black Pill.
In contrast, works of Dr. Alok Kanojia (Dr.K, Healthy Gamer Foundation), serve as a basis to understand the individual behind the pain and help said individual build human connections worth having. To that end, what better way to understand people than to listen to them directly, on a platform's subreddits that were created solely for them to share their thoughts, unfiltered thanks to the anonymity. Can we derive some understanding over the multiple disenfranchised groups from this dataset? Can such understanding be published to ultimately help people become better people, sons/daughters, spouses and partners.
The purpose of this dataset is to help people by aiding understanding of the different groups.
Source Data
Each record contains a reddit post, a couple hundred per subreddit, and has a key title and a post with words to display the intended message by the author. The authors will remain anonymous, as they do not deserve persecution for their thoughts, whether you disagree with them or not.
Data Collection and Processing
The empty text fields almost always corresponded to videos, so they have been replaced by empty strings. The curation of the content can be expanded in the future, but for now, over 7,000 records have been curated.
Who are the source data producers?
The producers of the data are the various redditors who have participated in these spaces.
Annotation process
The origin of the posts are the labels of the records.
Who are the annotators?
The subreddit origin and the post authors (by deciding to publish on the specific subreddit) are the label annotators.
Personal and Sensitive Information
This dataset contains no personally identifiable information with the exception of embedded youtube links. Those links may lead to videos where the impact of the content is unknown.
Bias, Risks, and Limitations
A major caveat is that the pink pill and original red pill groups are shadow banned, impeding their scraping process. This is a flaw I recognize because the original red pill movement, which started in books by authors, propagated itself through its internet (reddit) variant, and it spawned all the other pills. In other words, the biggest sources of information are locked away, and we have to make use of their closest proxies and/or sibling subreddits.
Another bias point is that there are more red pill groupings, as a means to compensate for the ban of the original red pill subreddit.
As such, I caution researchers to balance their datasets where necessary. The next step for this dataset is to expand to take the original Red and Pink Pill subreddits.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. Remember that this dataset is not a tool for reckless and hateful political agendas.
Citation [optional]
BibTeX:
[Blog Post Coming Soon]
APA:
[Blog Post Coming Soon]
Glossary [optional]
Quick Definitions of the Pill ideologies :
In short, according to archetypical definitions
- the red pill is the emancipation of the masculinity in a feminized age and understanding mating strategies with women.
- the blue pill is the satire of the red pill, often run by women.
- the black pill is meant to bridge the gaps across the red, pink, and blue pills in order to land on a ground truth.
- the pink pill is about improving the female image by augmenting sexual marketplace value.
Dataset Card Authors [optional]
steamcyclone, all the redditors from the subreddits (anonymized).
Dataset Card Contact
- Look me up.