---
license: apache-2.0
task_categories:
- text-generation
- fill-mask
tags:
- not-for-all-audiences
pretty_name: Incel Dataset 🎭
size_categories:
- 100K<n<1M
language:
- en
---
# Dataset Card for IncelSet

### Dataset Summary

This dataset is based off the incels.is forum and is ⚠️HIGHLY OFFENSIVE⚠️
A compilation of almost 3 years worth of posts, highlighting topics such as (self-described) celibatism, self-views, life-improvement (attempts or advice), suicide, perceived failure, views on women, views on society, views on politcs - from the members' perspective.

Co-Authored by inmate & curly for Universiteit van Amsterdam 
[Politics, Psychology, Law and Economics (PPLE)](https://pple.uva.nl)

### Languages

English with a lot of racial slurs, misoginy, mentions of sexual assault and general hatred - do not view or use if easily offended.

## Dataset Structure

The dataset consists of 2 colums, "title" - representing the thread title & "text" - representing the user replies (posts) under the thread title

### Source Data

Incels.is Forum.

#### Initial Data Collection and Normalization

1. We first built a script in GoLang that scrapes all the content of the incel.is Forum.
We downloaded roughly 150.000 threads - containing almost 2.1 Million posts - in approximately 9 hours from start to finish - using a dedicated server with 72 cores.
2. We then took the scraped data and started processing it, firstly building a script in Python that processed the data & formatted it into the JSON data format according to (RFC 8259) standards. 
3. We then started the removal process of PII (Personal Identifiable Information) - thus anonymizing user posts in the dataset. This wasn't hard to do as users already set up monikers for themselves & never gave out personal information such as full names, addresses or social security numbers, nevertheless we still validated the removal of such data.
4. We then proceeded to remove leftover non-human readable text such as HTML tags or base64 encodings, along URLs users may have posted in their discussions.
5. We now begin the dataset formatting process of compiling all 143.501 files left (threads) & ~2.1M posts in Parquet.
6. Final results yield approx 1bil characters on ~144k rows. 

#### Who are the source language producers?

Self-described incels / members of the incels.is website (not to be taken in the mot-a-mot sense of the word)

### Personal and Sensitive Information

  Includes details of the users' (tragic & tragically self-perceived) lifes. No personal information contained in itself but touches on many sensitive subjects.

## Considerations for Using the Data

Go wild with it. Keep in mind that we are not trying to expose, radicalize or even remotely harm this community.
We have compiled almost 3 years worth of posts on this forum so we could better study this phenomena for a University project.
We will be taking into consideration the actual publishing of the model trained on this data, but we do not see a potential scientific gain that would convince us to do so.

### Social Impact of Dataset

Public Awareness and Education:

Pro: Publishing a dataset might bring greater public awareness to the issue and could be used for educational purposes, enlightening people about the intricacies of this community. Greater understanding might foster empathy and encourage supportive interventions.
Con: It might also inadvertently glamorize or sensationalize the community, leading to an increased interest in and potential growth of such ideologies.
Source: Marwick, A., & Caplan, R. (2018). Drinking male tears: Language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543-559.

Potential Stigmatization and Alienation:

Pro: Identifying problematic behaviors and attitudes can help professionals develop targeted interventions.
Con: Generalizing or pathologizing the behaviors of this community might further stigmatize and alienate its members. Labeling can reinforce undesirable behavior if individuals internalize these negative identities.
Source: Dovidio, J. F., Major, B., & Crocker, J. (2000). Stigma: Introduction and overview. In T. F. Heatherton, R. E. Kleck, M. R. Hebl, & J. G. Hull (Eds.), The social psychology of stigma (p. 1–28).
Misuse of Data:

Pro: When used responsibly, such a dataset can be a treasure trove for academic research.
Con: However, there's always a risk of data being misused, misinterpreted, or cherry-picked to support harmful narratives or agendas.
Source: boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.

Ethical Concerns:

Pro: Revealing problematic beliefs might serve a greater good.
Con: There are ethical concerns, especially if data was collected without consent. Respect for individuals' autonomy and privacy is paramount in research ethics. (Data is collected under anonymity from a free-to-view, no-signup required, non-scrape blocking Forum - as per their ToS)
Source: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research.

Psychological Impact on Incels:

Pro: Confronting one's views might lead to self-reflection and change.
Con: Conversely, it might entrench their beliefs further if they feel attacked or misunderstood, a phenomenon supported by the backfire effect.
Source: Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303-330.

### Discussion of Biases

The authors compiled only the first 150.000 of the 270.000 threads in the "Inceldom discussion" part of the forum. As a consequence, older posts have been left out and the dataset may not thoroughly represent the full extent of incel discourse. The authors declare no further biases or conflicts of interest - the data was scraped and processed as it appears on the forum.