Good research starts with a clear question. If you are preparing a neighborhood coffee shop launch, for example, you might want to understand what local customers ask about independent cafés, which public posts describe unmet needs, and which creators are already talking about the area. The xCinder Data Scraper helps turn a focused X search into a reviewable list you can work with after the search.
This guide walks through a practical workflow: choose a narrow question, collect a small batch of public results, review what is relevant, and export the rows for analysis. The scraper is a research aid, not a substitute for judgment or permission to contact people.
What the Data Scraper collects
The Data Scraper collects public X data through a connected account. You can search public posts, collect a person’s public posts, or collect the followers or following of your own connected account. Results appear in the live view as they arrive, and you can export available rows as CSV or JSON.
Each job can collect up to 1,000 rows, subject to your plan’s remaining monthly row allowance. A smaller, well-scoped collection is often easier to inspect and more useful than a large, unfocused export.
A real-life example: planning a neighborhood café launch
Imagine you are helping a small café prepare to open in Zamalek. The owner wants to understand what nearby coffee customers discuss publicly before deciding which menu details and opening information to highlight. The goal is to learn about recurring themes—not to build a list for unsolicited messages.
1. Write down the research question
Start with something you can answer: “What do people publicly mention when they recommend or look for independent cafés in Zamalek?” This keeps the search grounded in a decision, such as whether to emphasize quiet work space, specialty coffee, or weekend hours.
2. Create a focused search job
Open the Data Scraper column, choose Search, and enter a few relevant phrases such as “Zamalek coffee shop,” “café in Zamalek,” or “specialty coffee Cairo.” Search terms should reflect how people actually describe the topic. Start with 100 or 200 rows so you can assess relevance before using more of your monthly allowance.
3. Watch the results and check their fit
As rows arrive, inspect the author, post text, date, and engagement counts. Skip posts that are unrelated, duplicated, or outside the local context. If the results mix several neighborhoods or broad coffee news, stop and narrow the search before starting another job.
4. Export and summarize themes
Download the CSV and open it in a spreadsheet. Add your own review column for a simple label such as “work-friendly,” “price,” “coffee quality,” or “hours.” Summarize recurring themes in aggregate, with links back to the public posts for context. A small reviewed sample can help shape a menu description or opening-hours announcement without making claims about every customer.
Other useful workflows
- A communications team can collect public search results around a product name to review questions and correct misunderstandings.
- A creator can collect their own public posts and compare which topics attracted more replies or reposts.
- A community manager can collect the followers or following of their own connected account for an authorized account review.
Make the collection useful and responsible
- Collect only public data that is relevant to a defined work purpose.
- Keep the row limit modest, and use the monthly allowance shown in your plan intentionally.
- Treat engagement counts as context, not proof that a post is accurate or representative.
- Avoid sensitive profiling, unsolicited bulk outreach, or sharing exported personal data beyond the people who need it.
- Follow X’s rules, applicable privacy requirements, and your organization’s data-handling policies.
The free plan includes 500 rows a month to try the Data Scraper, and paid plans include more. Plan row allowances and features can change; check the current plan details before starting a job.