X Tweet Viral Score Analyzer with AI | $0.0003/Tweet

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Xquik is the world's fastest & cheapest X (Twitter) scraper service with the most complete X data. Xquik's X Tweet Viral Score Analyzer rates every tweet. It adds a Viral Score estimate & a verdict. Most other Apify Actors charge before filtering or deduplicating. Xquik charges only for delivered, unique, filter-matching results. AI costs are included in the per-tweet price. You need no AI account, tokens or key.

Learn why tweets spread or flop & keep the original tweet data. Xquik's X Tweet Viral Score Analyzer with AI collects matching tweets. The AI rates 8 traits of each post. The Actor turns those answers into a Viral Score estimate & a verdict. Every row keeps real likes, reposts, replies & quotes. Compare each estimate with what happened.

• Viral Score per post. Fixed, versioned rules compute each score from 0 to 100.
• 8 trait answers. They show why a post scored high or low.
• Hard stops. They cap posts that read as spam, ragebait or generic machine copy.
• Complete source records. Every row keeps every field the tweet exposes.

The Viral Score estimates how well the wording works. It does not predict likes or views. It does not reproduce how X ranks posts.

Xquik is an independent third-party service. Not affiliated with X Corp. "Twitter" and "X" are trademarks of X Corp.

How to check a tweet's viral score

New to Apify? Create a free account first. The free plan needs no credit card & gives $5 to spend each month. At $0.0003 per analyzed tweet, $5 pays for up to 16,000 analyzed tweets before platform usage. Then come back here & press Try for free.

1. Add search terms, profile handles, tweet URLs or tweet IDs.
2. Set maxItems & the extraction filters your task needs.
3. Describe your audience in analysis.context, or leave the default.
4. Start the run & open the Viral Score dataset view.

{
  "searchTerms": ["from:NASA -filter:replies -filter:retweets"],
  "maxItems": 150,
  "analysis": { "context": "Space fans & general readers." }
}

What the Actor answers

Question	Answer
Hook	0 no hook, 1 clear opening, 2 sharp opening
Clarity	0 confusing, 1 takes effort, 2 clear on first read
Informative	0 nothing new, 1 familiar point, 2 useful takeaway
Funny	0 not funny, 1 mildly amusing, 2 funny enough to share
Ragebait	Probability that the post mainly provokes outrage
AI written	Probability that the text reads like generic machine copy
Spam	Probability of spam, scam, giveaway or engagement farming
Reaction	Share, reply, like, argue or ignore

The AI-written answer judges style only. It does not establish who wrote the post.

How the viral score works

Hook, clarity, payoff & the expected reaction raise the score. Wording that reads like generic machine copy lowers it.

Hard stops cap the score of likely spam, ragebait & generic machine copy. The score is a whole number from 0 to 100.

Verdict	Score
send_it	70 to 100
edit_first	40 to 69
sleep_on_it	0 to 39

viral.weights names the version of these rules, such as viral_lite:1. It changes whenever the rules change. The score is null after a failed or skipped analysis. It is also null when a default trait answer is missing. Xquik's X Tweet Viral Score Analyzer never fills a missing score with a guess.

Algorithm score estimate

X published its ranking weights in the repository xai-org/x-algorithm, file home-mixer/params/param.rs. Xquik's X Tweet Viral Score Analyzer applies 4 of them to each post's public counts.

Count	Weight
Like	0.66
Reply	5
Repost	1
Quote	5

viral.algorithmWeightedSum is the sum of each count times its weight. viral.algorithmScore divides that sum by views & multiplies by 1,000. A post without a view count uses followers instead. viral.algorithmBasis names the divisor, views or followers. Compare scores with the same basis only. viral.weightsVersion names the weights, such as x_algorithm_params:2026-10-14.

The estimate has these limits.

• X multiplies each weight by a probability it predicts for one viewer. The Actor multiplies by observed counts. The result is an estimate, not the score X computes.
• X publishes no weight for bookmarks or views. The sum leaves both out.
• X uses more signals than these 4, such as dwell time & shares. Public data does not show them.
• The score is null when a post has no views & no follower count.
• The AI never sees these counts. It reads the text & context only.

Predicted against actual

Xquik's X Tweet Viral Score Analyzer compares each Viral Score with what happened. viral.actualEngagementRate is log10(1 + weighted sum per 1,000 followers). The log limits the effect of one very large post. The rate is null when the follower count is missing or 0.

The run summary's viral.calibration block reports these fields.

• comparedPosts counts posts with a Viral Score & an actual rate.
• rankCorrelation is a Spearman rank correlation from -1 to 1. It shows whether higher scores went with higher rates.
• calibrationScore is 100 times the correlation, floored at 0.
• overperformers & underperformers list up to 5 posts each. Each has its tweet ID, URL, Viral Score, actual rate & gap.

gap is the standardized actual rate minus the standardized Viral Score. A post enters a list when its gap reaches 1 standard deviation.

The calibration has these limits.

• Fewer than 10 compared posts give a null calibration with the reason too_few_posts. Identical scores or rates give no_variation.
• The correlation is approximate.
• The calibration describes one run. A low score can mean the posts differ in timing, topic or audience. It does not prove the wording estimate failed.
• Young posts have not finished collecting engagement. Compare posts of similar age.

Account report

The run summary's viral.accounts block reports each author handle.

• Post count, average Viral Score & average actual engagement rate.
• The best & worst post by Viral Score, with tweet ID & URL.
• Average Viral Score per bucket. Buckets are posting hour in UTC, text length band, has media, has link & self-thread.

Text length bands are short, medium, long & extended. short ends at 80 characters, medium at 200 & long at 280. extended covers longer text. A self-thread post replies to its own author.

The report has these limits.

• The report lists the 50 handles with the most scored posts.
• The report tracks the first 1,000 handles of a run. untrackedPosts counts scored posts from later handles & posts without a handle.
• A bucket with few posts says little. Check posts before you compare averages.
• Buckets show what went together in this run. They do not show cause.

Leaderboard

The run summary's viral.leaderboard block ranks the handles of the account report. byViralScore ranks by average Viral Score. byActualEngagementRate ranks by average actual rate. Each list holds up to 20 handles with rank, posts & average.

The leaderboard has these limits.

• A handle needs at least 3 scored posts to rank.
• The rate list skips handles without a follower count.
• More posts, then the handle name, break ties.
• The leaderboard covers the posts of one run, not an account's whole history.

Score a draft before you post

Paste your own text in texts. Xquik's X Tweet Viral Score Analyzer scores it & fetches nothing from X.

{
  "texts": [
    "We shipped dark mode today. Try it and tell us what breaks.",
    "5 things we learned from 1,000 support tickets."
  ],
  "analysis": { "context": "Developers who use our app." }
}

• Each text becomes 1 row with viralScore, viralVerdict & viral.stops.
• tweet.id is text:1, text:2 & so on, & tweet.type is text.
• A draft has no likes or views yet, so viral.algorithmScore stays null.
• Each analyzed text costs the same $0.0003 as an analyzed tweet.
• With texts set, the run analyzes only those texts. Run X targets separately.

How much does it cost to check viral scores?

Xquik's X Tweet Viral Score Analyzer costs from $0.0003 per analyzed tweet. It charges no start fee. The price includes collection, AI costs & the Viral Score. You need no AI account, tokens or key. The price covers up to 8 questions & 64,000 bytes of context per tweet. Each question definition may use up to 8,000 bytes.

For more volume, pick an Apify plan with more prepaid usage.

Extraction filters & deduplication run before analysis. You never pay for filtered-out or duplicate rows. Failed analyses, skipped analyses & diagnostic rows have no result charge. Apify bills platform usage for compute, storage & transfer separately at your plan's rates. The Pricing tab shows it.

Input & output examples

The input above is copy-ready. An abbreviated output row looks like this.

{
  "tweet": { "id": "2100493544842494265", "text": "...", "likeCount": 12 },
  "viral": {
    "score": 74,
    "verdict": "send_it",
    "weights": "viral_lite:1",
    "stops": [],
    "algorithmScore": 9.46,
    "algorithmBasis": "views",
    "algorithmWeightedSum": 18.92,
    "actualEngagementRate": 0.7582,
    "weightsVersion": "x_algorithm_params:2026-10-14"
  },
  "viralScore": 74,
  "viralVerdict": "send_it",
  "viralAlgorithmScore": 9.46,
  "viralActualEngagementRate": 0.7582,
  "analysis": {
    "status": "succeeded",
    "answers": [
      { "questionId": "hook", "type": "score", "value": 2, "confidence": 0.84 },
      { "questionId": "spam", "type": "probability", "probability": 0.03 },
      {
        "questionId": "reaction",
        "type": "choice",
        "value": "share",
        "confidence": 0.7
      }
    ]
  }
}

Each result contains tweet, analysis & viral. Answers include types, question versions & available probabilities. viral.stops lists the hard stops that capped the score. A row with a failed or skipped analysis keeps the collected tweet & a reason. Its answer list is empty & its score is null.

Free diagnostics in the key-value store explain invalid inputs, missing results & interrupted collection. The run report separates collected rows, charged analyses & pending charges.

Run summary & flat answers

A run writes an analysis-summary record to its key-value store in 3 cases.

• It hits a problem or is large.
• Its comparison finds a changed, new or not comparable tweet.
• It has alwaysSaveRunRecords on.

Other runs skip the record, including the first run of a series. Their status names the top answer, like Average Viral Score is 64. A comparison without a change states No change since the earlier run. A run that hits a problem or is large also writes run-report. So does a run with alwaysSaveRunRecords on. run-report repeats the summary under results.analysisSummary.

The summary counts analyzed, failed & skipped rows. It sums engagement & summarizes every question.

• The viral block reports averageScore & the count of each verdict. It also counts scored & unscored rows.
• The same block holds calibration, accounts & leaderboard, described above.
• Score questions report a mean & an engagement-weighted mean.
• The reaction split shows how many tweets fall into each reaction.
• top lists the 3 most engaged tweets per reaction.
• Every row lists sourceDomains, the hostnames it links to.
• Every row lists cashtags found in its text, such as $NVDA.
• With monitor.baselineDatasetId set, the summary's monitor block counts comparison statuses. It lists up to 50 changed rows.

An empty run reports counts of 0 & a null average.

Every result row also carries viralScore, viralVerdict, viralAlgorithmScore & viralActualEngagementRate. It also carries answers, a flat map keyed by question ID. Each value is the chosen category, score or probability. The Viral Score dataset view & CSV or Excel exports show these columns. They sit beside the tweet, so spreadsheets need no JSON parsing. Failed & skipped rows carry an empty map.

Compare with an earlier run

Pass monitor.baselineDatasetId, the dataset ID of a completed earlier run with the same analysis settings. The comparison reads that run's rows. It works even when that run skipped its summary. Every row then gains a monitor object. Its status takes these values.

• first_run without a baseline.
• new_to_baseline for tweets the earlier run did not have.
• unchanged or changed for tweets it had.

changes lists each trait decision that moved from previous to current. Decisions compare by category, rounded score level or yes/no at 0.5. A decision counts as changed only when it moves clearly. Near ties between runs stay unchanged.

A baseline above maxBaselineRows, or from different settings, stops the run before collection. The run then writes a diagnostic row. maxBaselineRows defaults to 100,000.

Task examples

Choose from 50 public tasks. Each starts from a real English search & a bounded maxItems. It uses the Viral Score dataset view. Some add audience context. Edit the search or context before running.

• Viral score of AI startup launch tweets
• Viral score of SaaS founder build in public posts
• Viral score of Product Hunt launch posts
• Viral score of Developer tool announcements
• Viral score of Open source release posts
• Viral score of Crypto project announcements
• Viral score of Parenting humor posts
• Viral score of Office humor posts
• Viral score of Pet photo captions
• Viral score audit of NASA posts
• Viral score audit of Duolingo posts
• Viral score audit of Wendy's posts

The remaining tasks cover more topics & brand accounts on the Actor page.

FAQ & support

Answers to common questions, then where to get help.

Do I need an AI account, X API key or login?

No. Xquik's X Tweet Viral Score Analyzer includes AI costs in its price. You need no AI account, tokens or key. You also need no X API key, login or credentials.

Does a high score mean a tweet will go viral?

No. The score estimates how well the wording works for a general reader. Timing, audience size, media & luck also decide reach. Compare scores with the real engagement counts on each row before you rely on them.

Can I use my own questions?

Yes. Custom analysis.questions replace the defaults. Send 1 to 8 choice, score or probability questions. Choice questions accept 2 to 255 categories. Score questions need at least 2 ordered levels. The Viral Score needs all 8 default questions, so custom questions leave it null.

Why did a row come back with a failed or skipped analysis?

analysis.status is failed or skipped. The Actor collected & delivered the tweet, but the AI analysis did not complete. analysis.reason names the cause. context_limit means your context & targets leave no room for the tweet. service_unavailable means the analysis service was briefly unavailable. These rows carry no result charge & no score. Shorten analysis.context or rerun the affected IDs.

The Actor still analyzes a tweet longer than maxContextBytes. It cuts quoted & replied-to posts first, then the tweet. analysis.contextAvailability.postText is then truncated. Raise maxContextBytes up to 64,000 to keep more text.

Does the analysis verify facts?

No. Answers describe what the post expresses & how the post frames it. Probabilities express AI confidence, not truth. Review important classifications against the original tweet, which every row keeps.

Which languages work?

Extraction supports every language X serves. We validate analysis on English customer scenarios first. Other supported languages return answers with the same structure.

How do I limit cost?

Filters, deduplication & maxItems run before analysis. You pay only for unique, filter-matching tweets. Use precise search operators, date bounds & engagement floors. Start with a small maxItems to check answer quality before a large run.

Is it legal to analyze X data?

The Actor requests public X fields. Results can contain personal data. Confirm a lawful purpose & follow applicable privacy rules. Ask qualified counsel when uncertain.

Can I use the API, schedules & integrations?

Yes. See the API tab for Python, JavaScript & cURL examples. Use Apify schedules for recurring runs. Pass the previous dataset ID as monitor.baselineDatasetId to see what changed. Apify integrations also connect runs to webhooks, Make, Zapier, n8n & Google Sheets.

Where do I get help?

Open an issue in the Issues tab with the run ID. Free diagnostics in the key-value store explain runs that need attention. A run that gets no row back says why in its status.

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