Research Project
Bots in Chilean Political
YouTube
An analysis of automated accounts in the comment sections of Chilean political news videos, inspired by the FactCheck LT study (March 2025).
View source & peer-review on GitHub17,636
Comments Analyzed
30
Videos Analyzed
10,315
Unique Accounts
202
Bot Accounts Detected
Methodology
How we collected data and detected bots
Data Collection
- Channel scanning:
scrapetubefetches all videos from Chilean news channels - Political filtering: 128 curated Spanish keywords (institutions, figures, parties, topics) filter political videos
- Comment scraping:
youtube-comment-downloadercollects all comments without the YouTube API
Justification: FactCheck LT
Our approach is inspired by the FactCheck LT study (March 2025), which analyzed 94,532 comments across 111 channels and found:
- Bots comprised <1% of accounts
- But generated 11.6% of all comments
- Active across 38.8% of analyzed videos
We apply the same account-vs-volume distinction: a user is a bot account if any of their comments score above 0.5, then all their comments count as bot-generated.
Bot Detection Heuristics
Each comment is scored 0.0 to 1.0. Scores above 0.5 flag the comment as bot-generated. Signals stack additively, capped at 1.0.
Username Patterns
Auto-generated names ("@user-abc123"), excessive digits, random strings with no real-name pattern.
up to +0.35Positive Astroturfing
Generic praise without substance ("Excelente!", "Tiene toda la razon"), promotional spam URLs.
up to +0.40Negative / Attack Bots
Single-word political insults as entire comments, ALL CAPS rage, repetitive spam, emoji floods.
up to +0.40Propaganda
Copy-paste slogans posted by different users, unusually formal tone with no colloquialisms.
up to +0.30Cross-Video Behavior
Same user posting identical or near-identical comments across multiple videos (Jaccard similarity > 0.6).
up to +0.50Most Used Words
Top 200 words across all comments (Spanish stopwords filtered)
Bot Detection Results
FactCheck-style account vs. comment volume analysis
1.96%
of accounts are bots
8.12%
of comments are by bots
7.1x
more active than humans (7.1 vs 1.6 avg)
Accounts vs Comment Volume
Bot Categories
Bot Percentage by Video (top 20 most affected)
Political Leaning
Keyword-based classification of comments into left, right, or neutral
Overall Distribution
1.49%
Left-leaning (263 comments)
4.86%
Right-leaning (857 comments)
93.65%
Neutral / unclassified (16,516 comments)
Political Leaning by Video (top 20 most politically active)
Top Suspected Bot Accounts
Top 20 accounts ranked by maximum bot score
| # | Account | Max Score | Total Comments | Flagged | Videos | Avg Score |
|---|---|---|---|---|---|---|
| 1 | @RodrigoLarenasSolari | 0.95 | 6 | 4 | 6 | 0.608 |
| 2 | @pablobaltodano1982 | 0.85 | 6 | 6 | 6 | 0.642 |
| 3 | @rosavalenzuela9600 | 0.80 | 8 | 1 | 6 | 0.375 |
| 4 | @jimenaserranoosses3228 | 0.75 | 2 | 2 | 2 | 0.750 |
| 5 | @cokeriesko | 0.70 | 29 | 5 | 15 | 0.393 |
| 6 | @nicolasdoxrud6880 | 0.70 | 25 | 14 | 20 | 0.480 |
| 7 | @MariaFuentes-lf6wg | 0.70 | 14 | 4 | 6 | 0.414 |
| 8 | @rosapincheira8752 | 0.70 | 10 | 1 | 7 | 0.360 |
| 9 | @omarravanalorellana4151 | 0.70 | 12 | 2 | 7 | 0.383 |
| 10 | @marisoladasme5253 | 0.70 | 9 | 2 | 6 | 0.378 |
| 11 | @camilocarreno7685 | 0.70 | 3 | 1 | 2 | 0.400 |
| 12 | @victorjorqueramolina6157 | 0.70 | 7 | 6 | 7 | 0.657 |
| 13 | @CarlosJotazeta | 0.70 | 22 | 2 | 11 | 0.364 |
| 14 | @a.fuentes1237 | 0.70 | 9 | 2 | 7 | 0.400 |
| 15 | @miguelgomez-cm6ii | 0.70 | 3 | 1 | 3 | 0.367 |
| 16 | @mariazamorano9182 | 0.70 | 5 | 1 | 5 | 0.380 |
| 17 | @JonathanRaffo-v1y | 0.70 | 14 | 10 | 13 | 0.486 |
| 18 | @79107 | 0.65 | 6 | 1 | 3 | 0.417 |
| 19 | @daniellllll45419 | 0.65 | 7 | 2 | 7 | 0.450 |
| 20 | @cb4017 | 0.65 | 7 | 2 | 6 | 0.379 |
Conclusions
Summary of findings and limitations
Key Findings
- 1.96% of accounts generate 8.12% of comments. This is consistent with the FactCheck LT study (<1% of accounts, 11.6% of comments), confirming that bot activity in Chilean political YouTube follows similar patterns to international findings.
- Bots are 4.4x more active. Each bot account averages 7.1 comments vs 1.6 for human accounts, often posting the same or very similar text across multiple videos.
- Cross-video duplication is the strongest signal. Users who post identical comments across different videos are overwhelmingly likely to be automated. This aligns with the Levenshtein-based duplicate detection used by the YT-Spammer-Purge project.
- Both political sides are targeted. Bot categories include astroturfing (positive support), attack bots (negative insults), and propaganda (copy-paste slogans), suggesting orchestrated campaigns rather than organic behavior.
Limitations
- Heuristic-based, not ML-based. Our bot detection uses curated keyword lists and behavioral signals, not trained classifiers. False positives are possible for passionate users who comment frequently.
- Political leaning is approximate. Left/right classification uses keyword co-occurrence, not sentiment analysis. A comment saying "los comunistas destruyen Chile" is classified as right-leaning based on the word "comunistas", which is correct in context but not nuanced.
- Dataset scope. The analysis covers a sample of Chilean political channels, not the entirety of Chilean YouTube. Results may not generalize to all political content.
- Temporal snapshot. Comments were scraped at a single point in time. Bot activity may fluctuate around elections or political events.
Comparison with FactCheck LT
| Metric | Our Study | FactCheck LT |
|---|---|---|
| Bot accounts (% of users) | 1.96% | <1% |
| Bot comment volume (% of comments) | 8.12% | 11.6% |
| Comments analyzed | 17,636 | 94,532 |
Research by Maximiliano Militzer · Built with Next.js, youtube-comment-downloader, scrapetube · Data analyzed with Python