Avalanche Safety on Social Media: Evidence from Reddit and YouTube

Repository / notebook
tercasaskova311/mountain_safety — CSS_assigment-1.ipynb

Which avalanche-safety topics are most visible on Reddit and YouTube, and what is the prevailing sentiment?

• Data. 155 items from r/AvalancheAwareness (posts and comments) via Communalytic; 100 YouTube videos with engagement metrics and sampled comments via the YouTube Data API v3.

• Methods. Sentiment with platform tools (plus Detoxify for toxicity screening); topic exploration with Latent Dirichlet Allocation (LDA) on YouTube comments.

• Findings. YouTube discussions are highly reactive (~81% positive, ~7% negative; rest neutral), dominated by exclamations and spectacle; Reddit is more neutral/mixed (~12% negative, ~72% neutral, ~16% positive). LDA topics on YouTube cluster around reactions; explicit procedural safety terms are comparatively sparse.
• Implication. Avalanche content on large platforms appears to amplify emotion more than procedure. Safety communication that foregrounds concise checklists and decision workflows may help rebalance attention.

1) Background and framing

Avalanche incidents are low-base-rate, high-consequence events. Social media shapes risk perception both directly (viewers’ takeaways) and indirectly (norms about gear and behavior). Conceptually, treat each comment as a small “vector” of attention: if vectors point toward awe and shock, the field encourages spectating; if they point toward forecast checks and terrain choices, the field encourages practice. This study measures where that attention tends to flow.

2) Data

Reddit (r/AvalancheAwareness). 155 posts/comments, with metadata (score, upvote ratio, flairs).
YouTube. 100 videos retrieved by “avalanche safety”; metadata and engagement (views, likes, comments), plus a sample of comments for text analysis.

Note. This is a scoped, transparent sample intended for method demonstration rather than population estimates.

3) Methods (succinct)

• Sentiment. Platform sentiment via Communalytic; complementary toxicity screening via Detoxify to flag incivility.
• Topics. LDA on tokenized YouTube comments to reveal recurring word bundles (K chosen pragmatically; stopwords tuned iteratively).
• Comparisons. Descriptive contrasts across platforms; no causal claims.

Intuition: sentiment ≈ temperature; topics ≈ contours. Together they sketch where conversation heat and mass concentrate.

4) Results

4.1 Sentiment

• YouTube comments: ~80.95% positive, ~7.14% negative (remainder neutral). Language is arousal-heavy (“amazing”, “wow”, “wtf”, “rip”).
• Reddit (r/AvalancheAwareness): ~16.16% positive, ~12.12% negative, ~71.72% neutral.

Interpretation. YouTube’s engagement incentives correlate with high-arousal reactions; Reddit displays a steadier tone but still not dominated by procedural discourse.

4.2 Topics (YouTube comments, LDA)

• T1–T3: Reaction clusters — frequent tokens like wtf, bro, lol, snow, avalanche.
• T4: Light gear/gratitude — tokens such as helmet, good, thank, would, great.
• T5: Hype/amazement — amazing, wow, never, clean, thanks.

Notably absent or rare in dominant topics: multi-step procedural terms (e.g., beacon checks, terrain traps, slope angle, compression tests).

5) Discussion

Why the tilt toward reaction?

• Platform incentives. Algorithms reward watch time and engagement; dramatic clips outperform tutorials.
• Affordances. Short comment boxes favor exclamations; instruction is longer and effortful.
• Selection. Viral avalanche content is often spectacle, not pedagogy.

Practical implications.

• Pair dramatic content with actionable micro-checklists (forecast → terrain → roles → beacon check → travel protocol).
• Pin and timestamp procedural summaries in descriptions and top comments.
• Encourage creators and SAR organizations to standardize brief, high-signal safety callouts.

6) Validity, ethics, and limitations

• Scope. One subreddit and 100 videos ≠ the ecosystem.
• Text limits. Sarcasm and context gaps can mislead polarity; LDA is bag-of-words and ignores phrase structure.
• Engagement bias. High-arousal content likely overrepresented.
• Ethics. Respect platform TOS; avoid doxxing; treat tragedy content with care.