recommendation
Popularity Analysis
Stable
62
out of 100
Good Performance
Key Insights
- 1Cross-platform usage skews toward “utility discovery” audiences: on X, discovery posts are often amplified by retweets/quote-tweets from interest clusters (tech, finance, media); on Bluesky and Mastodon, engagement concentrates in tighter communities where follows and replies matter more than raw hashtag volume. Practical takeaway: write for shareability on X (one-line hook + link), and for conversation on Bluesky/Mastodon (prompt + context).
- 2Content that performs best with #recommendation is highly specific and scannable: (a) 1–3 item lists (e.g., “3 newsletters for …”), (b) personal proof (“I used this for 30 days…”), and (c) niche qualifiers (“for beginners,” “under 10 minutes,” “free/OSS”). These formats consistently earn higher saves/bookmarks and replies than generic “any recs?” posts, which tend to get many low-quality responses and less downstream sharing.
- 3Platform nuances: X favors velocity—early engagement in the first 15–30 minutes strongly affects distribution, so pairing #recommendation with a clear category keyword in the first 80 characters (e.g., “app/book/tool”) improves CTR and repost rate. Bluesky boosts posts that spark threads; questions outperform links unless you add a short rationale + invite counter-recs. Mastodon users often follow hashtag timelines directly; using 1–2 precise tags (not 6–10) plus alt-text for media improves inclusion in local and hashtag feeds and can lift engagement, especially in community-run instances.
Pro Tip: Make it “category + constraint + proof” in the first lineAI
Use #recommendation alongside a tight category keyword and a constraint (time, price, level), then add one sentence of proof. Example: “#recommendation: Free note-taking app for students—switched from X last month and my study time dropped ~20% (template link).” This structure boosts both click-through and replies on X, while still inviting discussion on Bluesky/Mastodon.