Quora Search Scraper — Fresh & Trending Question Monitor

Quora Trend Monitor — Fresh and Trending Questions

Quora Search Scraper — Fresh & Trending Question Monitor turns a keyword search or direct URL list into a filtered, sorted feed of recently-active Quora content, with every row carrying an absolute creation date, an age-in-days figure, and a freshness flag — plus optional delta tracking that flags which questions are new since your last scheduled run. It's built for trend researchers and content teams who need to know what's newly active on Quora right now, not just what matches a keyword, community managers running recurring monitoring on a topic, and market researchers who want AI-generated trend labels grouping fresh questions into themes at a glance. No Quora login is required. Every section below documents an input, an output field, or exactly how the freshness window and delta tracking work.

What is Quora Search Scraper — Fresh & Trending Question Monitor?

This Actor is a superset of a plain Quora search scraper: keyword search (via DuckDuckGo discovery) and direct-URL scraping both still work exactly as a base scraper would, with a real recency filter, topic filter, sort order, and cross-run new-question tracking layered on top.

Key capabilities, read from the source:

• Two-layer freshness filtering, not just a discovery-time filter. timeFilter is applied both at discovery (DuckDuckGo's own date-range parameter) and again as a genuine post-fetch check against each answer's real creationTime — so a question can't slip through just because DuckDuckGo's index metadata was stale.
• Question-level freshness inherited from its newest answer. When a question itself has no reliable timestamp, _apply_filters_and_delta() falls back to the age of its most recently-posted answer to decide whether the question counts as fresh — an undated question is marked is_fresh: null rather than guessed either way.
• Real topic filtering, not a keyword re-search. topicsFilter matches case-insensitively against each question's actual Quora topic tags (_topic_match()), keeping only questions genuinely tagged with your chosen topics — non-question content (profiles, spaces) passes through untouched since topic tagging doesn't apply to them.
• Persistent new-question tracking across scheduled runs. trackNewQuestions stores each seen question's URL and first-seen timestamp in a named key-value store; the very first run has nothing to diff against and is explicitly logged as a baseline-seeding run (is_new: null), with only subsequent runs producing a real is_new: true/false.
• Optional AI trend clustering, off by default. aiEnhancement groups the collected fresh questions into short trend_cluster labels via a chosen LLM provider — this only runs over questions already collected, so it never affects which content is scraped, only how it's labeled afterward.
• Global post-collection sorting. sortBy (recent, upvotes, or relevance) is applied once across the entire collected buffer at the end of the run, not per-search-batch, so the final feed is genuinely ordered across all your keywords and URLs combined.

What data can I extract with Quora Search Scraper — Fresh & Trending Question Monitor?

Every field below is read directly from _apply_filters_and_delta() and the row assembly in main() in src/main.py — dedicated dataset views (trending, overview, answers, questions, profiles, topics, spaces) group these by content type.

Freshness and trend fields (new in this variant)

Field	Example Value	Notes
created_at	2026-07-20T10:15:00Z	Absolute ISO timestamp — for a question, inherited from its newest answer
age_days	6	Days since created_at
is_fresh	true	Whether the row falls inside your timeFilter window; null when the row is undated and can't be proven stale
is_new	true	Whether this question wasn't in the prior run's seen-store; null on the first (baseline) run
first_seen_at	2026-07-14T09:00:00Z	When this question was first observed across scheduled runs
trend_cluster	"AI coding tools"	Short theme label; only populated when aiEnhancement is on

Base question, answer, profile, topic, and space fields

Field	Example Value	Content Type
title / answer_count / follow_count / topics	question fields	question
answer_text / author_name / author_credentials / upvotes / comments_count / shares_count / is_ai_answer	answer fields	answer
name / bio / credentials / follower_count / following_count / answer_count / total_views	profile fields	profile
name / description / follower_count / question_count	topic fields	topic
name / description / follower_count / post_count / contributor_count	space fields	space

Why not build this yourself?

Quora has no public API, and there's no documented endpoint for "recently active questions" at all — the only path to a real freshness signal is decoding each answer's actual creation timestamp from Quora's embedded GraphQL data and comparing it against a window yourself, since DuckDuckGo's own date filter (the discovery mechanism) is index-time, not content-time, and its finest bucket is a full day. Reliable cross-run "what's new" tracking additionally requires persisting state between runs and diffing against it correctly — including handling the first run's cold-start case honestly (returning everything as baseline rather than falsely flagging it all as "new"), which is easy to get subtly wrong.

How to use data extracted from Quora Search Scraper — Fresh & Trending Question Monitor?

Trend and content research

Search a broad topic keyword with timeFilter: "past_week" and sortBy: "recent" to see what's genuinely newly active on Quora in that space right now, rather than a keyword match regardless of age.

Recurring community monitoring

Schedule this Actor with trackNewQuestions on against your niche's core topics — after the first baseline run, every subsequent run flags exactly which questions are new since the last check, ideal for a monitoring dashboard or alert feed.

Market research with theme grouping

Turn on aiEnhancement to get fresh questions automatically grouped into trend_cluster labels — useful for spotting emerging sub-themes within a broader topic without manually reading every question title.

AI agents and monitoring pipelines

Because is_fresh/is_new/age_days are all computed server-side from real timestamps, an agent can trust these fields directly for alerting logic without re-deriving freshness itself.

🔼 Input sample

Parameter	Required	Type	Description	Example Value
searchQueries	No	array	Keywords to search on Quora via DuckDuckGo discovery.	["python programming"]
directUrls	No	array	Direct Quora URLs — questions, profiles, topics, or spaces.	[]
maxResults	No	integer (1–50000)	Total rows to collect after filtering, across all keywords/URLs combined. Default 10.	100
timeFilter	No	string enum	all_time, past_day, past_week, past_month, past_year. Default "all_time".	"past_week"
topicsFilter	No	array	Only keep questions tagged with these Quora topics (case-insensitive).	["Programming Languages"]
sortBy	No	string enum	recent, upvotes, or relevance. Default "recent".	"recent"
trackNewQuestions	No	boolean	Flag questions new since the last scheduled run. Default false.	true
aiEnhancement	No	boolean	Group fresh questions into trend_cluster labels. Default false.	false
aiModel	No	string enum	Model/provider for clustering (Claude, GPT, Gemini, Grok, DeepSeek, Sonar, Mistral variants). Default "claude-haiku-4-5".	"claude-haiku-4-5"
aiApiKey	No	string (secret)	Provider API key; falls back to the matching environment variable.	"sk-..."
proxyConfiguration	No	object	Starts direct, escalates to datacenter then residential automatically on a block.	{"useApifyProxy": true}

{
  "searchQueries": ["python programming"],
  "timeFilter": "past_week",
  "sortBy": "recent",
  "trackNewQuestions": true,
  "maxResults": 100
}

Common pitfall: the schema notes that "past_hour" isn't a real option — DuckDuckGo's finest date bucket is one day, so a past_hour value is silently normalized to past_day internally. Don't expect hour-level freshness precision from this Actor's discovery layer.

🔽 Output sample

Output is one JSON row per question, answer, profile, topic, or space, pushed to the run's default dataset and charged as one row_result event per row.

{
  "content_type": "question",
  "title": "What are the best new Python libraries for data science in 2026?",
  "url": "https://www.quora.com/What-are-the-best-new-Python-libraries-for-data-science-in-2026",
  "answer_count": 12,
  "follow_count": 340,
  "topics": ["Python (programming language)", "Data Science"],
  "created_at": "2026-07-20T10:15:00Z",
  "age_days": 6,
  "is_fresh": true,
  "is_new": true,
  "first_seen_at": "2026-07-20T10:15:00Z",
  "trend_cluster": null,
  "source_query": "python programming",
  "scrape_timestamp": "2026-07-26T09:00:00Z"
}

How do you filter and target fresh and trending content?

Freshness window drives both cost and relevance. A narrow timeFilter (past_day/past_week) both speeds up the run (fewer stale results to filter after fetch) and focuses the output on genuinely current activity — start narrow and widen only if a niche topic doesn't have enough recent volume.

Combine topic and freshness filters for a focused trend feed. topicsFilter plus timeFilter together is the core "trend monitor" pattern — scoped to your niche's real Quora topic tags, restricted to a recency window, sorted by recent.

Delta tracking needs a consistent schedule. trackNewQuestions only produces meaningful is_new values on the second and later runs against the same query/URL set — running with different searchQueries each time won't build a useful history, since the seen-store is keyed by question URL, not by search term.

Three real examples:

{ "searchQueries": ["startup funding"], "timeFilter": "past_week", "sortBy": "recent", "trackNewQuestions": true }

Recurring weekly trend monitor with new-question flagging.

{ "searchQueries": ["ai tools"], "topicsFilter": ["Artificial Intelligence"], "timeFilter": "past_month", "aiEnhancement": true }

Topic-scoped trend feed with AI clustering into themes.

{ "directUrls": ["https://www.quora.com/some-question"], "timeFilter": "all_time" }

Single-question deep scrape with no freshness restriction.

▶️ Want to try other scrapers?

Scraper	What it extracts
Quora Scraper with Human Answer Quality Score	Quality-ranked, AI-filtered Q&A data
Google News Scraper	Headlines, publisher domain and links
X/Twitter Trends Scraper by City	Trending topics across cities
Reddit Trends Scraper with Author Contact Leads	Trending Reddit posts with contact leads

How to extract Quora trend data programmatically

This Actor runs as a standard Apify Actor call — one API call in, structured JSON dataset out, using your Apify API token.

Python example

from apify_client import ApifyClient

client = ApifyClient("<YOUR_API_TOKEN>")
run = client.actor("<YOUR_USERNAME>/quora-search-scraper-fresh-trending-question-monitor").call(run_input={
    "searchQueries": ["python programming"],
    "timeFilter": "past_week",
    "trackNewQuestions": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("content_type") == "question":
        print(item["title"], item["age_days"], item.get("is_new"))

Export to spreadsheets or CRM

Every dataset supports one-click CSV/Excel export from the Apify Console, plus dedicated dataset views (trending, questions, answers, profiles, topics, spaces) for exporting one content type at a time.

Is it legal to scrape Quora?

Scraping publicly visible Quora questions, answers, and profiles is generally lawful, since this data is published for anyone to view without logging in — the underlying legal question was tested directly in hiQ Labs v. LinkedIn (9th Cir.), which held that scraping public, non-password-protected data does not violate the U.S. Computer Fraud and Abuse Act. Author names and profile bios can constitute personal data under GDPR/CCPA when tied to an identifiable individual, so treat that subset accordingly if you store or reuse it at scale, and consult legal counsel for commercial applications.

❓ FAQ

How precise is the freshness filter?

DuckDuckGo's discovery-time date filter buckets by day at its finest, but the post-fetch filter checks each answer's real creationTime down to the timestamp, so age_days and is_fresh are accurate even though discovery itself can't target anything finer than a day.

What happens on the very first scheduled run with trackNewQuestions on?

It's treated explicitly as a baseline-seeding run — every question gets is_new: null (not true), since there's no prior snapshot to compare against. Only the second and later runs against the same seen-store produce real true/false values.

Does topicsFilter re-search Quora for those topics?

No — it filters the already-discovered questions by matching their real Quora topic tags against your filter terms, case-insensitively and substring-either-way. It doesn't issue additional search queries.

Can a question be marked fresh even with no timestamp of its own?

A question with no reliable timestamp inherits freshness from its newest answer's age; if there's no dated answer either, is_fresh is null rather than guessed, since the Actor can't prove the question is stale without any real date to check.

Do I need an AI API key for the trend monitoring features?

No — freshness filtering, topic filtering, sorting, and new-question tracking are all core features requiring no AI or API key. aiEnhancement (trend-cluster labels) is the only feature that needs a provider API key, and it's off by default.

How does this compare to other Quora monitoring scrapers?

As observed on the Apify Store on 2026-07-26, crawlerbros/quora-search-scraper covers keyword-based Quora search but doesn't document a real freshness window, topic filtering, or cross-run new-question tracking — those trend-monitoring features are specific to this Actor.

Does this work with AI agent frameworks?

Yes — call it as a standard HTTP endpoint via the Apify API from any agent framework capable of making an API call; there's no MCP-specific integration for this Actor.

Conclusion

Quora Search Scraper — Fresh & Trending Question Monitor turns a keyword search into a genuine trend feed — real freshness filtering backed by actual timestamps, topic-scoped results, and cross-run new-question tracking for recurring monitoring. It fits trend research, community monitoring, and any workflow that needs to know what's newly active, not just what matches. Start a run from the Apify Console or the Apify API with your target keywords to get your first trend-scoped export.