social-analytics

Social media competitor analysis is performed by scraping competitor profiles with beautifulsoup4 to identify follower counts, post frequency, and top-performing content, then calculating their engagement rates to benchmark against your own profile.

Top-performing content is identified by scraping the Instagram profile to gather post data, then using pandas to sort and rank posts by engagement metrics such as applause, amplification, and conversation rates to discover what resonates most with the audience.

Social media engagement rates are calculated by measuring key metrics like amplification, conversation, and applause using Python libraries such as pandas. This requires gathering post-level interaction data including likes, comments, and shares.

Python and beautifulsoup4 work for scraping social media profiles by fetching profile data with requests and parsing it to extract follower counts and post frequency for engagement analysis using pandas.

The best way to benchmark social media performance is to audit competitor presence by scraping their profiles and calculating their engagement rates, then comparing these metrics against your own profile data to identify gaps and top-performing content strategies.

Yes, you can audit competitor presence on Twitter and LinkedIn using Python by leveraging requests and beautifulsoup4 to extract profile data, then using pandas to analyze follower counts, post frequency, and engagement rates.