Answers · Social · 10 questions
Updated 25 Oct 2026 · Reviewed by Mohammad Shakir, Co-founder & CEO
Public social content is the raw material for influencer vetting, share of voice and trend detection - as long as collection stays on the public layer and treats people's data with care. These answers cover what's usable, what's off-limits, and how teams actually work with it.
Publicly visible content and metrics: public profiles, posts, follower and engagement counts, comments on public posts, and hashtag or topic activity. Private accounts, private messages and personal data behind logins are out of scope.
That public layer is enough for most brand use: competitive content benchmarking, influencer evaluation, share of voice and trend detection.
By checking follower growth patterns, engagement rate against account size, comment quality, posting cadence and brand-safety of past content - across the influencer's public history, not the media kit.
Engagement bought or botted shows up in the data: growth spikes without engagement, comment pods, engagement rates far from category norms.
Collect competitors' public posts with their engagement metrics, classify content by format and theme, and compare performance per theme against your own - what formats work, what cadence, what gets shared.
The goal is not copying; it is knowing the baseline you are being compared against by the same audience.
Yes - comments on public posts in your category are unprompted consumer language: complaints, desires, comparisons and questions. Mined at scale and clustered by theme, they read like a continuous focus group.
Treat them in aggregate. The insight is in the patterns, not in profiling individual commenters.
By tracking topic, hashtag and sound velocity - what is accelerating, not what is big. Early acceleration on TikTok or X often precedes mainstream volume by weeks.
For product and content teams, the useful output is a ranked list of rising themes in their category, refreshed weekly or faster.
Public company pages carry followers, posts, engagement and hiring signals, which feed competitive and B2B market analysis. LinkedIn restricts automated access in its terms, and the legal history around scraping it is extensive.
Treat LinkedIn with particular care: public-only, aggregate use, and legal advice before building anything on it.
Trend detection needs daily or faster; share of voice and benchmarking work weekly; influencer vetting is point-in-time with a history lookback.
Freshness costs collection volume, so split your pipeline: fast lanes for trends and alerts, batch lanes for reporting.
Use official APIs where they cover your need - but platform APIs have narrowed over the years, with access, rate and pricing changes arriving abruptly. Many measurement use cases now exceed what the APIs offer.
The resilient pattern is the same as marketplaces: operate through official channels, measure the public layer through collected data, and keep legal review in the loop.
Social content is tied to people, so data protection laws - India's DPDP Act, GDPR in Europe and others - apply to personal data even when it is public. Aggregate analysis of public content is a different exercise from profiling individuals.
Design for aggregation, minimise personal data, and take legal advice for your jurisdictions. This is general information, not legal advice.
ScrapeGuys builds and operates production scraping APIs that return clean, structured JSON - and takes custom targets live in 48-72 hours.