Twitter Hashtag Post Scraper

Twitter Hashtag Post Scraper turns a small set of keywords or hashtags into structured public-post records that are ready to filter, compare, and analyze. Each record keeps the matched term, search-result category, post and author links, author name, publication time, post text, media links, advertising indicator, and reported engagement counts.

This dataset helps teams move from scattered social posts to a consistent evidence table. It preserves the text and the surrounding context needed to compare conversations, identify visible authors, inspect recurring themes, and understand which collected posts attracted more engagement at the time of collection.

Data notes

Records come from publicly visible Twitter search results and reflect what the collector could access when the run took place. A record represents one collected post associated with one submitted term and search-result category. The same post address is used to remove duplicates across retrieved result pages. Post timestamps retain the source's displayed UTC offset. Reply, retweet, like, and view counts are snapshots reported at collection time and can change afterward; they are preserved as reported text rather than recalculated values. Image links may point to an image or a video thumbnail, and a reported video link may be the same as the post link. Private, deleted, restricted, and sign-in-only content is not covered.

What the results look like

Each record is one collected public post:

The output also includes “Matched term,” “Result category,” “Search page URL,” “Author profile URL,” “Image URL,” “Video URL,” “Ad indicator,” “Reply count,” and “Retweet count.”

Use cases

• For hashtag conversation research, group records by “Matched term” and review “Post content” to identify recurring topics, claims, and phrases.
• For visible-author discovery, compare “Author name” and “Author profile URL,” then use “Post URL” to inspect the original public context.
• For engagement comparisons, evaluate “Reply count,” “Retweet count,” “Like count,” and “View count” together instead of treating one interaction measure as the whole response.
• For content-format analysis, compare “Image URL,” “Video URL,” and “Post content” to see which collected posts pair text with reported media.