MADOC: Reddit / videos

A MADOC (Reddit, videos) dataset of 1,854,456 users and 8,653,524 reply edges from 36,775,460 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.

#Icwsm#Madoc#R videos#Reply network#Toxigen

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Analytics

The interactive network needs JavaScript.

· graph.json

The full graph has 1,854,456 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.

1.9M
Nodes
8.7M
Edges
2.52e-06
Density
8K
Components
-0.02782
Assortativity
0.0%
Reciprocity
940.2K
Bridges
Most central nodes
7ccc6faa-9eed-5297-ad15-ec0e65d246190.00658
c0af7001-4e98-5d29-bb2e-f25e3a0aea9e0.00616
b6eab8e8-8488-53ab-bd5d-c18d00bba9640.00564
6165889d-fb01-5ca1-806a-66726c84b00d0.00527
f9fe3f07-bee3-503b-8fc4-ce09549c151d0.0047
e0e0f01d-74b8-5446-ac3a-2afcf6c9a6670.00465
e8e5d15a-13a2-5cf4-9251-e4857f37fcfd0.00368
afdaa2ab-bf83-5a3e-9781-a1d3ba36cfef0.00367
39cb3a60-857b-5d29-acf0-cad713de08670.00351
0efe04ef-830a-505e-a64f-e21fefbb9fd90.00342
Content metrics

36,776,651 records scored · min–maxmean ± 0.675 σ (the interquartile range if the values are roughly normal)mean

Sentiment (VADER)μ 0.054 ± 0.472
min -1max 1
low 30%medium 37%high 33%
Sentiment (TextBlob)μ 0.067 ± 0.281
min -1max 1
low 23%medium 44%high 33%
Subjectivity (TextBlob)38.7% true
true 38.7%false 61.3%
Toxicity (ToxiGen)μ 0.15 ± 0.311
min 0.001max 0.993
low 33%medium 34%high 33%
6.5
Flesch–Kincaid grade
US school grade level
6.9
Automated readability index
Frequent terms
likepeopledonthinkvideotimeknowgoodguywayrightshitgoingactuallygotprettydidnthingwantnewfuckyearsmandoesnlotbetterdayfuckingsuresaidgreatpointneedbestusebadlovethingsprobablywatchyeahlookthoughtlifeisnyearlittlemakesworldfeel
Topic clusters

Topic 0

video
youtube
camera
watch
second
guy
like
great

Topic 1

pilot
plane
fly
flight
aircraft
land
landing
airport

Topic 2

catch
cricket
game
ball
play
baseball
sport
bat

Topic 3

beautiful
look
girl
pretty
good
hot
cute
nice

Topic 4

whale
orca
luna
animal
boat
human
killer
dolphin

Topic 5

wire
season
watch
episode
series
breaking
favorite
story

Topic 6

gun
shoot
cop
kill
glass
police
criminal
weapon

Topic 7

phil
scene
uncle
prince
fresh
avery
james
father

Topic 8

generation
people
book
game
knowledge
information
read
point

Topic 9

leno
funny
joke
laugh
louis
jay
hilarious
comedian

Topic 10

drug
lsd
trip
smoke
weed
mushroom
people
bad

Topic 11

link
time
bump
money
buck
pay
live
day
Show all 31 topics

Topic 12

new
year
happy
years
eve
dubai
fireworks
firework

Topic 13

car
drive
driver
road
accident
hit
truck
debris

Topic 14

card
deck
penn
trick
magician
magic
hand
force

Topic 15

downvote
comment
reddit
upvote
post
karma
subreddit
repost

Topic 16

wound
blood
gel
bleed
spray
heal
apply
bleeding

Topic 17

sample
daft
punk
song
music
sampling
album
original

Topic 18

guitar
mayer
guitarist
john
play
tune
player
good

Topic 19

wat
prefer
canada
que
whoop
para
parada
manuel

Topic 20

cunt
bad
sad
feel
suck
title
awful
shit

Topic 21

wave
frequency
object
turn
light
gravity
acoustic
pressure

Topic 22

understand
english
language
dialect
accent
south
word
german

Topic 23

song
music
singer
sing
national
theme
cover
sound

Topic 24

think
miss
point
source
mind
king
come
thing

Topic 25

thank
god
info
explanation
awesome
sir
captain
good

Topic 26

ableton
daw
program
music
software
midi
instrument
audio

Topic 27

dog
greyhound
howl
puppy
italian
pet
rabbit
cute

Topic 28

guy
superpimp
dick
dude
cleveland
man
like
look

Topic 29

fuck
feeny
fucking
fucker
annoying
fuckin
god
dude

Topic 30

sleep
foot
shirt
asleep
fall
pizza
eat
hat
Top hashtags
#t118
#130
#323224
#frequent20
#216
#34
#44
#explanation4
#53
#83
#uses3
#yolo3
#222
#62
#72
#85942
#a252
#aaaaahhhhh2
#criminal_investigations2
#es_sm2
Top emoji
😂3
🈲1
🍴1
😳1

Sampled: text metrics from 50K of 36.8M 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-videos
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
36,775,460
Nodes
1,854,456
Edges
8,653,524 (directed)
Nodes are
user
Edges are
reply
Attributes
nodes have no attributes; edges have no attributes
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, 6.4 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 / videos [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 / videos. 2025, doi.org/10.1609/icwsm.v19i1.35954.

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