MADOC: Reddit / KotakuInAction

A MADOC (Reddit, KotakuInAction) dataset of 83,581 users and 1,758,653 reply edges from 6,793,795 interactions, collected by Dankulov et al. via platform APIs, published at ICWSM 2025. Each record contains user-user reply edges, post and comment text, per-record sentiment scores (VADER and TextBlob), subjectivity scores, and toxicity scores (ToxiGen). The dataset is notable for its FAIR-compliant dataset with pre-computed NLP quality labels (sentiment, subjectivity, toxicity) per record, enabling network analysis and content quality research without additional annotation. Intended for research into social network analysis, toxicity detection, cross-platform community dynamics, misinformation research, and hate speech propagation studies.

#Gamergate#Kotakuinaction#Madoc#Reply network#Toxigen#Vader

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Analytics

The interactive network needs JavaScript.

· graph.json

The full graph has 83,581 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.

83.6K
Nodes
1.8M
Edges
2.52e-04
Density
7
Components
-0.01159
Assortativity
3.1%
Reciprocity
37.2K
Bridges
Most central nodes
add580ff-0fe0-5f19-8f0d-2dc38f6ef4871.753
c92918b8-7876-505f-b4c4-9a78776198d90.6201
ff8eec3c-72ca-5c0d-9e62-694cfa152f8f0.3713
3a839b81-11a6-5155-a713-bc54e60e618a0.268
5a92fa2c-288e-5d97-8624-e37865b0a6380.2637
bccb91a0-8462-58e5-a4c2-4468cdd378c40.2518
bc1bd9d4-2808-5249-90dd-65cf1fe649c50.2094
da4d7a44-e96f-5416-9107-6d95196024dd0.2086
67c8dde7-d8bb-5138-a4e0-97362a50433c0.2081
af224991-1d4b-5307-b358-22a2c60c27c40.1787
Content metrics

6,793,795 records scored · min–maxmean ± 0.675 σ (the interquartile range if the values are roughly normal)mean

Sentiment (VADER)μ -0.029 ± 0.532
min -1max 1
low 33%medium 34%high 33%
Sentiment (TextBlob)μ 0.043 ± 0.25
min -1max 1
low 29%medium 38%high 33%
Subjectivity (TextBlob)41.5% true
true 41.5%false 58.5%
Toxicity (ToxiGen)μ 0.213 ± 0.338
min 0.001max 0.993
low 33%medium 34%high 33%
7.7
Flesch–Kincaid grade
US school grade level
8.5
Automated readability index
Frequent terms
peoplelikedongamesthinkgameknowwantgamergategaminggoodwomengoingthingwaytimerightshitgamerssjwactuallyneedpointvideodoesnthingslotsaidisnarticlepostdidngottryingsurefuckingmediaprettybadlooksayingzoetwitterindustrypersonjournalistsjournalismletusemean
Topic clusters

Topic 0

game
gamergate
kotaku
gamer
gaming
play
like
people

Topic 1

good
think
fuck
time
look
hope
idea
wow

Topic 2

zoe
quinn
anita
sarkeesian
people
like
game
video

Topic 3

feminist
feminism
woman
man
patriarchy
gender
like
people

Topic 4

game
play
woman
character
female
gamer
like
want

Topic 5

ban
reddit
admin
mod
subreddit
post
sub
vote

Topic 6

sjw
justice
social
people
sjws
think
warrior
like

Topic 7

journalist
article
breitbart
journalism
write
corruption
like
fact

Topic 8

link
page
site
archive
donotlink
post
use
google

Topic 9

video
podcast
money
pay
watch
youtube
stream
know

Topic 10

twitter
tweet
account
hashtag
delete
link
post
retweet
Top hashtags
#gamergate1.4K
#009336
#notyourshield265
#or111
#sfw111
#gg105
#136
#t33
#gameethics31
#226
#317
#killallmen14
#selection14
#writeakotakuarticle14
#gamersgate12
#510
#99
#burgersandfries9
#gameovergate9
#papersplease9
Top emoji
🎂1
📅1

Sampled: text metrics from 50K of 6.8M records; topic model from 20K. Degree assortativity: exact — Pearson correlation of total degree over every edge (DuckDB) Bridge bottleneck count: exact — biconnected components of the undirected graph (NetworKit) Connected components: exact — weakly connected components (NetworKit) Density: exact Reciprocity rate: exact (DuckDB) Top 10 centrality hubs: exact — full degree count (DuckDB)

Record

Catalog key
madoc-reddit-KotakuInAction
Authors
Dankulov, Marija Mitrović; Tomašević, Aleksandar; Maletić, Slobodan; Anđelković, Miroslav; Vranić, Ana; Cvetković, Darja; Stupovski, Boris; Vudragović, Dušan; Major, Sara; Bogojević, Aleksandar
Year
2025
Source paper
MADOC: Multi-Platform Aggregated Dataset of Online Communities
DOI / arXiv
10.1609/icwsm.v19i1.35954
License
Creative Commons Attribution 4.0 International (CC BY 4.0) (CC-BY-4.0)
Records
6,793,452
Nodes
83,581
Edges
1,758,653 (directed)
Nodes are
user
Edges are
reply
Attributes
nodes have no attributes; edges have no attributes
Text corpus
post text
Languages
en
Collected
2012 to 2024 (2010s, 2020s)
Tasks
From the graph: link prediction, community detection
Region
Global (inferred: the platform is used worldwide)

Provenance

Originally published at
https://zenodo.org/records/15690964 link checked
Hosted copy
Mirrored on echo.directory on 2026-10-02, 1.5 GB across 1 files. Checksums in MANIFEST.json.

How to cite

BibTeX

@misc{echo2026,
  title  = {ECHO: Exploratory Compendium of Humanity Online},
  author = {Switzer, Braden and Zafarani, Reza},
  year   = {2026},
  url    = {https://echo.directory},
  note   = {Syracuse University}
}

@article{dankulov2025madoc,
  title   = {{MADOC: Multi-Platform Aggregated Dataset of Online Communities}},
  author  = {Dankulov, Marija Mitrović and Tomašević, Aleksandar and Maletić, Slobodan and Anđelković, Miroslav and Vranić, Ana and Cvetković, Darja and Stupovski, Boris and Vudragović, Dušan and Major, Sara and Bogojević, Aleksandar},
  journal = {Proceedings of the International AAAI Conference on Web and Social Media (ICWSM)},
  volume  = {19},
  year    = {2025},
  doi     = {10.1609/icwsm.v19i1.35954}
}

APA

Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory

Dankulov, M. M., Tomašević, A., Maletić, S., Anđelković, M., Vranić, A., Cvetković, D., Stupovski, B., Vudragović, D., Major, S., & Bogojević, A. (2025). MADOC: Reddit / KotakuInAction [Data set]. https://doi.org/10.1609/icwsm.v19i1.35954

MLA

Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.

Dankulov, Marija Mitrović, et al. MADOC: Reddit / KotakuInAction. 2025, doi.org/10.1609/icwsm.v19i1.35954.

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