DEV Community: Joe Vezzani

The July Break: 365 Days of Social Sentiment Before the 2026 Midterms

Joe Vezzani — Wed, 04 Nov 2026 20:14:08 +0000

Democrats won the House and the Senate on November 3. For most of the year, the social conversation looked different.

We pulled 365 days of sentiment on posts about each party with the LunarCrush CLI, lined it up against Kalshi, Polymarket and the polling averages, and asked a simple question: what did each signal show, and when?

The full study, with interactive charts and a free dataset, is at lunarcrush.com/research/midterms-2026-social-lookback. The code to rebuild every number is on GitHub.

Eight months one way, then near-even

Sentiment here is the engagement-weighted share of positive engagement on posts about a topic, 0 to 100, across X, Reddit, TikTok, YouTube, Instagram and 10K+ news sources. It measures the tone of the conversation about a party, whoever is posting. It is not a measure of support.

November to June: Republicans had the better tone in 33 of 34 weeks, by 6.6 points on average.
Week of July 6: both parties fell, and Republicans fell further.
July 6 to October 26: Republicans led 6 of 17 weeks. The average gap was +0.2.
October 30 to November 3: Democrats led every day to the vote.

Was July a measurement change?

That was the first thing our review panel asked. When two series drop 15 to 25 points in one week, the obvious suspect is the pipeline, not politics.

It wasn't platform-wide. Over the same two weeks, sentiment on the economy moved 3.9 points, Texas 2.0 and inflation 1.5, and Kalshi rose 3.2. The drop sat in party and election conversation: Republicans down 24.5, Democrats 16.2, "election" 15.7.

Volume is not tone

Democrats drew more than half of party engagement in 51 of 52 weeks, including the eight months they trailed on tone. If you only count mentions, you learn who is being argued about. The sign of the sentiment gap was the more useful read.

Social and the market

Kalshi's price for Democratic Senate control hit its summer low of 40% on July 2. The Republican sentiment edge ended the next week. Kalshi crossed 60% on September 19 and stayed there.

We tested whether social front-ran the market. It didn't. Week to week, the sign of the gap called the direction of the next week's Kalshi move 45% of the time, and the correlation was -0.13. Within the same week, the two moved together (correlation 0.34). Social moved with the market, not ahead of it. What it adds is the posts behind each move.

The September repricing

On the evening of September 9 ET, at the Republican midterm convention in Dallas, President Trump promised a $5,000 "dividend" to every adult citizen if Republicans held both chambers. The clip drew 16.7M engagements. On September 10, sentiment about Republicans hit 74 on 278M engagements, the party's strongest day since July.

By September 12 it was back to 51. The following week, Kalshi went from 49% (Sept 13) to 60% (Sept 19). The generic ballot barely moved, from D+7.3 to D+7.8.

Election night, by the hour

The parties were level through 7pm ET (66 vs 65). As results came in, the gap was 8 points at 9pm and 15 at 11pm, when engagement on the two parties peaked at 171M interactions in one hour.

The same search, with and without the data

We asked an AI assistant with live web search five questions about how the race looked online, and let it estimate. It answered none from a measured figure. Its estimate for the year, that Democrats held the better tone for most of it, is the opposite of the data: Republicans led 39 of 52 weeks.

That isn't a knock on the model. The public web has polls, prices and narratives. It doesn't have a daily, measured record of the conversation itself.

Rebuild it yourself

Everything on the page comes from public CLI calls:

curl -fsSL https://lunarcrush.ai/install.sh | bash
lunarcrush login

# a year of daily metrics for a topic
lunarcrush topic democrats time-series --interval 1y --json
lunarcrush topic republicans time-series --interval 1y --json

# hourly, last month
lunarcrush topic midterms time-series --bucket hour --interval 1m --json

# the posts behind a move
lunarcrush topic republicans posts --start 2026-09-10 --end 2026-09-11

The repo has the full pull script, the build, and an analysis.py that prints every headline number from the shipped data. The dataset (24 topics x 365 days, a weekly party panel, and the final week by the hour, in CSV and Parquet with an AI guide) is free on the study page.

If you'd rather ask in plain English, the same data is available to Claude, ChatGPT and other assistants through the LunarCrush MCP server.

Social data pulled 19:26 UTC, Nov 4, 2026. Markets: Kalshi and Polymarket public APIs. Polls: Silver Bulletin daily averages.

The Binance Hack, Reconstructed

Joe Vezzani — Fri, 30 Oct 2026 18:09:29 +0000

The Binance hack was rehearsed. Twice, on mainnet, in public, hours before it happened.

At 13:51 UTC on October 29, a wallet funded only through Tornado Cash delegated two fresh wallets of its own to an unverified sweeper contract, seeded them with a few dollars of stablecoins and emptied them. At 16:20 it ran the full sequence with its production contract: delegate, sweep, leave 1 ETH, withdraw. Its first sweeper had been on-chain since 02:28. Six hours and 29 minutes after the first rehearsal, it ran the same play on Binance 14, one of the exchange's main Ethereum hot wallets.

We rebuilt the whole attack from the Ethereum record, live balance reads on six chains, and 53,181 posts across X, TikTok, YouTube, Instagram, Reddit and news. Here is what happened, how fast the public pieced it together, and where the money sits now.

The attack in six acts

I. The setup. About 1.49 ETH of Tornado Cash withdrawals, starting at 07:39 on October 28, funded the operator. That was the operation's entire budget. It paid for two sweeper contracts and two rehearsals, the second a full dress run of what came that night.

II. The drain. At 20:20:59, one transaction delivered 50 EIP-7702 authorizations to Binance 14. All 50 were signed by Binance 14's own key. Exactly one, matching the wallet's live nonce, applied, and the wallet started running the attacker's code. At 20:24:23 a single sweep moved 167,464 ETH and 63 tokens to the attacker's receiver for $0.93 in gas. The attacker then used that code to call Hot Wallet 34, a second Binance wallet that accepted Binance 14 as its owner, and pulled 823M USDC through it in two calls at 20:41 and 20:59.

III. The open door. The attacker's code stayed on Binance 14 for 3 hours 49 minutes, until the wallet signed its own revocation at 00:10:35. Each sweep left exactly 1 ETH behind, so the wallet kept paying for its own transactions and kept looking normal. It took in 533 incoming transfers in that window, and the re-sweep at 21:44 alone took 200.4M USDT.

IV. The response. The issuers moved first. Tether's $971M freeze was on the feed at 21:05. Binance's first word came at 21:15, from CZ and the company in the same minute. Circle froze 823M USDC, Tether raised its freeze to 1.567B USDT at 21:25, Aave froze its USDT market at 21:52, and World Liberty froze $27M of USD1. Binance put its verified frozen total at $2.54B at 23:22. At 00:13 it said it would cover all losses and paused all deposits and withdrawals. At 00:52 it said about $3B was frozen.

V. The money. Where every traced dollar sits, read live the next morning. More below.

VI. The public record. How the crowd rebuilt each step, and how fast.

Social rebuilt every step

The first public post calling it a hack landed 8 minutes after the sweep: @spreekaway at 20:32 UTC. The 780M USDC pull through Hot Wallet 34 was described in 7 minutes by @0xfoobar. The delegation was public in 33 minutes via @odysseas_eth, and the fifty authorizations in an hour via @0xQuit. By 21:25, posts had the mechanism, the contract, the hardcoded operator and the fifty signatures, and every one of those claims checks out against the chain. Tether's freeze reached the feed ten minutes before Binance said anything.

The setup was a different story. The first sweeper sat on-chain for 19 hours before anyone wrote about it. Both rehearsals were found at 21:31 by @MetaFinancialAI, after the money had moved. The Tornado Cash funding waited 37 hours for its first mention.

What varied across posts was never the sequence. It was the total. Public outflow estimates ran from $2.53B at 20:39 to $5.57B by 21:12 and $5.7B by 21:58, depending on which chains and which freezes each poster counted.

That is the part of an incident social data captures best. Every step in our timeline is matched to the first public post that described it, and in a live event that record moves faster than any official statement.

Where the money is

LunarCrush re-read every traced balance live between 03:20 and 03:27 UTC on October 30, on Ethereum, Bitcoin, Tron, XRP Ledger, Solana and Arbitrum, priced at CoinGecko spot.

Frozen or recovered, about $2.66B by our read. That is $2.63B blacklisted at the token contracts (1.73B USDT across four addresses on two chains, 823M USDC, 27.8M USD1 and $50M of XAUt) plus $30.2M moved out on Arbitrum, which @zachxbt reported as the Arbitrum Security Council's recovery.
Unfrozen in traced addresses, about $2.83B by our read. BTC $1.41B, ETH $950M, XRP $287M, SOL $93M, wBETH $73M, and $16.3M of Aave collateral net of debt.
Gross traced, about $5.49B.

Bitcoin is the largest unfrozen position: 18,270.2 BTC across 19 addresses, none of which has spent an output. About 430,000 ETH sits downstream of the sweep, much of it in addresses that have never sent a transaction. The Bitcoin, Tron, XRP and Solana legs carry Whale Alert's Binance label; Binance has confirmed none of them and has not confirmed a loss total. Its own figure is about $3B frozen, as of 00:52.

The pattern is simple. Freezes worked where the asset had an issuer, and nowhere else.

Five signals that could have flagged it

1. The rehearsal, 13:51. A mixer-funded fresh address, a type-4 delegation to unverified code with a sweep selector, dust wallets seeded and swept. A rule for that pattern fires at 13:51:35. It would not have named the target, but it would have put the operator on a watchlist that hit again at 20:20:59, six and a half hours later.

2. The cross-chain start, 20:05 to 20:11. Whale Alert posted 185,017,485 XRP ($236M) and 965,455 SOL ($95M) leaving Binance-labeled wallets for brand-new addresses at 20:06 and 20:11. A rule for "hot wallet to never-seen address, above $50M, on two chains, within ten minutes" fires ten minutes before the Ethereum delegation. Binance has not said whether those transfers were its own.

3. The delegation, 20:20:59. Seventeen blocks, 3 minutes 24 seconds, separated delegation from sweep. A monitor that decodes type-4 transactions in new blocks and matches authorization signers against an exchange's own wallets fires at inclusion. The first public mention came 30 minutes after the sweep.

4. The open delegation, 20:24 to 00:10. Etherscan showed a "Delegate to" label on Binance 14 from 20:21. Clearing it before 20:41 blocks about $1.03B of the later outflows on explorer-visible lines; clearing it by 20:59 still blocks about $255M.

5. The mood of the day, 17:47 to 18:56. In the two hours before the delegation, X was full of signing-attack news: warnings of a malicious script on Polymarket's front end, CZ on a Ledger supply-chain compromise, and at 18:56 CZ describing "an injection attack on the front end" to 640K engagements. A different class of attack, on the same day, about the same thing: what gets signed.

Read the full report

The full reconstruction runs 77 pages, with every transaction, every live read and every first post laid out step by step. The data and code are open, and every on-chain read can be replayed.

The Crowd Says War. The Curve Says the Fed.

Joe Vezzani — Mon, 28 Sep 2026 19:48:49 +0000

The 10-year Treasury closed at 5.11% on September 23, its high for the year. The 30-year hit 5.40%, and the average 30-year mortgage crossed 7%.

Social media noticed. Posts about Treasury yields are running at roughly four times their first-half pace. So we asked a simple question: is the story the crowd is telling the same one the bond market is pricing?

Mostly, no.

Explore the full interactive study in The Crowd Says War. The Curve Says the Fed.

The data and a validation script are on GitHub: JoeVezzani/rates-crowd-vs-curve.

A real-rate shock, not an inflation scare

From the February 27 low, the 2-year rose 147bp, the 10-year 114bp and the 30-year 76bp. The front end led and the curve flattened.

Split the 10-year's move into its parts and the answer is lopsided. The real yield (TIPS) rose 104bp. Ten-year inflation expectations rose just 8bp, and five-year expectations actually fell 7bp. Real yield is 91% of the move.

An inflation scare lifts breakevens. A deficit scare steepens the curve. This did neither. Breakevens are flat and the curve is flatter, which is what a tighter expected Fed path looks like. The Fed cut three times in late 2025, then hiked 25bp at its September meeting.

Four times louder, and it follows the tape

Posts on the "treasury yields" topic averaged about 3,300 a month in the first half of 2026. September is running at about 14,200. The surge arrived in August and September as the 10-year pushed toward 5%.

Day to day, rate chatter tends to follow yield moves rather than lead them. On rates, the crowd explains the move more than it predicts it.

War and oil lead the story. The Fed trails it.

We tagged 16,899 rate-specific posts from January 1 to September 23 (1.7 billion interactions across X, Reddit, TikTok, YouTube, Instagram and 10,000+ news sources) by the driver they cite.

War and oil dominate at 18.8% of engagement, spiking in March, May and September. The Fed (8.6%) and inflation (8.3%) each get less than half as much. Deficits get 4.8%.

The curve points to a different chain: war and oil feed the inflation fear, the Fed responds, and real yields do the work. The crowd has the trigger. It is missing the transmission.

The midterms: two loud conversations that barely touch

Posts about the midterms have grown roughly 6x since January, to a pace near 300,000 a month. Yet only 0.5% of bond-market engagement mentions them.

Where the two conversations do meet, the posts are about affordability, gas prices and the national debt, not the yield curve. Before November 3 the bond market is trading the Fed. After it, the channel that matters is the fiscal path: who controls Congress, and how much Treasury has to borrow.

Who feels it: housing first, energy with the move

For 923 of the 1,000 largest US stocks, we measured the average daily move for each +10bp in the 10-year, from March 1 to September 22.

Energy is the only sector that rises with yields, consistent with oil sitting underneath both. Among large caps, homebuilders and the mortgage lender Rocket track the 10-year most tightly:

Stock	Correlation with 10Y	Move per +10bp day	Since Feb 27
RKT	-0.70	-5.8%	-32%
DHI	-0.63	-3.1%	-10%
LEN	-0.63	-3.3%	-32%
CCL	-0.62	-4.3%	-29%
OXY	+0.55	+2.8%	+8%
XOM	+0.52	+2.0%	+4%

Airlines and cruise lines show up because the same oil move hits them, not only rates.

What to watch into November

The 5-year breakeven. If the war-and-oil story is right, it rises. So far it is lower than in February.
The 2s30s curve. Flattening says the Fed. A steepener led by the long end would say fiscal worry.
House-control odds. The midterm signal for rates is who controls Congress, not the affordability debate.
The early-November refunding and the late-October FOMC. Watch whether social attention shifts from war to deficits.

Read this before quoting it

Social shares measure attention, not cause. Driver tags are keyword-based and overlapping, and most posts cite no driver. Stock sensitivities are correlations over one regime. Yields are from FRED as of September 23-24, 2026. This is research on public data, not investment advice or a rate forecast.

Get the data

Every number in this post is in the free dataset: daily yields with social volume, rate sensitivity for 923 stocks, sector medians and an AI guide you can drop into Claude or ChatGPT. Download it from the study page.

The same measurements run hourly, point-in-time, for any ticker, topic or watchlist through the LunarCrush API and the MCP server.

The Crowd Shows Up a Week Before Earnings. It Still Does Not Know the Trade.

Joe Vezzani — Thu, 13 Aug 2026 20:01:01 +0000

Social media begins moving before earnings. In aggregate, it still does not hand you the trade.

That is the useful answer.

We aligned social attention around 2,898 US earnings prints from January through June 2026. The crowd begins leaning in roughly seven days before the report. Attention accelerates into the event, peaks on print day, then fades over the following week.

But loud does not mean bullish, bearish, or even important. Some of the loudest conversations barely moved the stock. Some of the largest moves happened in near silence.

Reproduce the public dataset anchors

Download the CSV from LunarCrush Research and run:

import pandas as pd

df = pd.read_csv("lc_earnings_social_events_2026H1.csv")

assert len(df) == 2_898
assert df.shape[1] == 12
assert df["sector"].nunique() == 13

liquid = df[df["baseline_daily_interactions"] >= 500]
share_preprint_hot = (liquid["pre_print_social_z"] > 1).mean()

print(len(liquid))                 # 2,193
print(round(share_preprint_hot, 3))  # 0.284

The 1,665-event curve used in the interactive Atlas applies an internal dollar-volume liquidity cut that is not included in the public file. The public-file anchors above are designed to be independently reproducible.

The crowd leans in a week early

Across 1,665 liquid-name prints, average social attention reached +1.07σ one day before the print and peaked at +1.79σ on print day.

The rise does not prove that the crowd knows the result. It proves that the information environment around a company begins changing before the event.

Loud is not the same as right

The loudest print in the study was Mondelez at +7.7σ. The stock moved only +1.2%. Palo Alto Networks reached +5.4σ and fell 8.7%. UnitedHealth reached +5.2σ and fell 16.4%. Hewlett Packard Enterprise reached +4.2σ and rose 30.4%.

Social reaction measures the size of the conversation, not the direction or magnitude of the stock move.

The blind spots are the better research queue

Quiet shocks are large earnings moves that social attention barely registered. Attention traps are huge conversation spikes attached to small price moves.

Both are useful because they move the question beyond "what is trending?" The better question is "where does attention disagree with the event?"

Read this before quoting the result

At daily granularity, top-decile names moved roughly 1.2 to 1.4 times more than bottom-decile names, depending on the liquidity and season cut. That is a real relationship, but it is not a standalone trading strategy.

Daily bars hide sequencing. They cannot tell you who moved the conversation first, what they said, or whether attention led price inside the day. Treat the published result as a floor. Hourly, point-in-time data is required to test lead-lag structure without look-ahead bias.

Take the data

The complete 2,898-event CSV, Parquet file, and AI companion guide are available from The Earnings Social Atlas, with no registration required.

The GitHub companion includes the data, validation script, and all article graphics: JoeVezzani/earnings-social-atlas.

Institutional teams can request the same study cut to a custom universe with hourly resolution, more than five years of history, and point-in-time membership at joe@lunarcrush.com.

This analysis is for research purposes and does not constitute investment advice.

The 50 X Creators Driving the Iran War Conversation (607.9M Engagements) -- With Code

Joe Vezzani — Tue, 14 Apr 2026 02:05:02 +0000

The Iran-Israel conflict generated 607.9 million engagements on X in Q1 2026 from just the top 50 creators. We tracked 22 war-related topics to find who actually drives the conversation and how to pull this data yourself.

GitHub repo: JoeVezzani/iran-war-creators

The Scale of the Conversation

Across 22 topics -- from "Iran" and "Israel" to "Strait of Hormuz" and "Airstrikes" -- the top 50 creators alone generated 607.9M engagements. Engagement peaked on February 28, coinciding with Strait of Hormuz escalations and nuclear deal negotiations.

The Top 10 Creators

Rank	Creator	Engagements	Topics Covered
1	@MarioNawfal	161.4M	19 of 22
2	@netanyahu	28.5M	2
3	@RyanRozbiani	22.3M	10
4	@A_K_Mandhan	20.5M	7
5	@BRICSinfo	20.3M	11
6	@jacksonhinklle	20.3M	15
7	@rebelliousdogra	19.8M	7
8	@KobeissiLetter	18.8M	7
9	@MOSSADil	18.7M	16
10	@Osint613	14.4M	17

@MarioNawfal's dominance is remarkable -- 161.4M engagements across 19 of 22 topics. More than the next five creators combined. His breadth of coverage is what separates him.

@Osint613 covers 17 topics with the broadest intelligence coverage. @MOSSADil covers 16. Citizen journalists and independent accounts consistently outperform legacy media on engagement.

Pull This Data Yourself

Track any geopolitical topic in real time with the LunarCrush API:

const API_KEY = process.env.LUNARCRUSH_API_KEY;

async function getTopicData(keyword) {
  const res = await fetch(
    `https://lunarcrush.com/api4/public/topic/${encodeURIComponent(keyword)}/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function getTopicPosts(keyword) {
  const res = await fetch(
    `https://lunarcrush.com/api4/public/topic/${encodeURIComponent(keyword)}/posts/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

// Track multiple war-related topics
async function scanConflict() {
  const topics = [
    "iran", "israel", "strait of hormuz", "airstrikes",
    "ceasefire", "nuclear deal", "oil prices", "sanctions"
  ];

  for (const topic of topics) {
    const data = await getTopicData(topic);
    console.log(
      `${topic.padEnd(20)} | ` +
      `${(data.data?.num_posts || 0).toLocaleString().padStart(8)} mentions | ` +
      `${(data.data?.interactions || 0).toLocaleString().padStart(12)} engagements | ` +
      `sentiment: ${data.data?.sentiment || 'N/A'}%`
    );
  }
}

scanConflict();

Track Top Creators on Any Topic

async function getTopCreators(topic) {
  const res = await fetch(
    `https://lunarcrush.com/api4/public/topic/${encodeURIComponent(topic)}/creators/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function main() {
  const creators = await getTopCreators("iran");

  console.log(`
Top Creators for "Iran":
`);
  for (const c of (creators.data || []).slice(0, 10)) {
    console.log(
      `  @${(c.screen_name || '').padEnd(20)} | ` +
      `${(c.interactions || 0).toLocaleString().padStart(12)} eng | ` +
      `${(c.followers_count || 0).toLocaleString().padStart(10)} followers`
    );
  }
}

main();

Build a Sentiment Alert for Geopolitical Events

const fs = require("fs");
const STATE_FILE = "./conflict_state.json";

function loadState() {
  try { return JSON.parse(fs.readFileSync(STATE_FILE)); }
  catch { return {}; }
}

function saveState(state) {
  fs.writeFileSync(STATE_FILE, JSON.stringify(state, null, 2));
}

async function checkConflictShifts() {
  const state = loadState();
  const topics = ["iran", "israel", "oil", "ceasefire", "strait of hormuz"];

  for (const topic of topics) {
    const data = await getTopicData(topic);
    const current = data.data?.sentiment || 0;
    const previous = state[topic]?.sentiment;

    if (previous && Math.abs(current - previous) > 10) {
      const dir = current > previous ? "UP" : "DOWN";
      console.log(
        `ALERT: "${topic}" sentiment shifted ${dir} ` +
        `(${previous}% -> ${current}%)`
      );
    }

    state[topic] = {
      sentiment: current,
      mentions: data.data?.num_posts || 0,
      timestamp: new Date().toISOString()
    };
  }

  saveState(state);
}

checkConflictShifts();

Run on a cron during active conflict periods. When sentiment shifts 10+ points, you know something just happened.

Why Social Data Matters for Geopolitics

Oil hit $114/barrel during the Strait of Hormuz situation. X produced 92,066 mentions of oil in 24 hours -- more than Reddit (22K), YouTube (46K), TikTok (44K), and all news sources (1.3K) combined.

For traders, researchers, and prediction market participants, social data is the fastest signal for geopolitical events. The conversation happens on X before it hits Bloomberg.

Try It

API key: lunarcrush.com/developers
API docs: lunarcrush.com/developers/api
MCP server (use with Claude): lunarcrush.com/products/lunarcrush-mcpserver
Full analysis with heatmaps: lunarcrush.com/blog/iran-war-creators-2026

I'm building a bunch of small projects like this. Follow along if you're into this kind of thing.

The Real X Creator Rankings for 2026 (With Code to Pull Them Yourself)

Joe Vezzani — Fri, 10 Apr 2026 16:58:36 +0000

Most rankings of X accounts use follower count. That tells you almost nothing about who actually drives conversation on the platform.

LunarCrush processes 50M+ social posts per hour across X, Reddit, TikTok, YouTube, Instagram, and 10K+ news sources. We ranked every X account by actual engagements (likes, reposts, replies, quotes, impressions) for 2026. The results are surprising.

GitHub repo: JoeVezzani/x-creator-rankings

X post: @joevezz

The Top 25 by 2026 Engagements

Rank	Creator	Category	Followers	Engagements
1	@elonmusk	Tech	237.8M	54.4B
2	@PopBase	Entertainment	2.9M	7.7B
3	@MarioNawfal	News	3.3M	7.3B
4	@narendramodi	Politics	106.6M	7.2B
5	@FabrizioRomano	Sports	27.2M	5.9B
6	@centralreality	Geopolitics	1.3M	5.7B
7	@DiscussingFilm	Entertainment	2.8M	5.3B
8	@TheCinesthetic	Entertainment	1.6M	5.1B
9	@Rainmaker1973	Science	4.3M	3.8B
10	@PopCrave	Entertainment	3.0M	3.5B
11	@EricLDaugh	Politics	1.0M	3.5B
12	@Variety	Entertainment	3.2M	3.4B
13	@sciencegirl	Science	5.8M	3.3B
14	@nypost	News	3.7M	3.2B
15	@Polymarket	Brand	1.4M	3.2B
16	@TouchlineX	Sports	1.6M	3.0B
17	@starlink	Tech	1.6M	3.0B
18	@WhiteHouse	Politics	4.4M	2.7B
19	@tmz	Entertainment	8.6M	2.7B
20	@bosunatiklama	Entertainment	1.3M	2.6B
21	@Dexerto	Entertainment	1.3M	2.5B
22	@BRICSinfo	Geopolitics	2.0M	2.4B
23	@FilmUpdates	Entertainment	1.4M	2.4B
24	@cristiano	Sports	106.6M	2.4B
25	@foxnews	News	28.7M	2.3B

You have to get to #11 before you find a partisan political commentator. The top 10 is entertainment, news, sports, science, and tech.

@cristiano has 106M followers but ranks 24th. @centralreality has 1.3M followers and ranks 6th with 2.5x more engagement. Follower count is not engagement.

Pull This Data Yourself

The LunarCrush API exposes creator-level data for any public X account.

const API_KEY = process.env.LUNARCRUSH_API_KEY;

async function getCreator(network, username) {
  const res = await fetch(
    `https://lunarcrush.com/api4/public/creator/${network}/${username}/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function main() {
  const creator = await getCreator("twitter", "PopBase");

  console.log(`\n${creator.data?.display_name} (@${creator.data?.screen_name})`);
  console.log(`Followers: ${creator.data?.followers_count?.toLocaleString()}`);
  console.log(`Engagements (24h): ${creator.data?.interactions?.toLocaleString()}`);
  console.log(`CreatorRank: ${creator.data?.creator_rank}`);
  console.log(`Posts tracked: ${creator.data?.num_posts}`);
}

main();

Compare Creators Head-to-Head

async function compareCreators(usernames) {
  const results = [];

  for (const username of usernames) {
    const data = await getCreator("twitter", username);
    results.push({
      name: data.data?.display_name || username,
      username,
      followers: data.data?.followers_count || 0,
      engagements: data.data?.interactions || 0,
      rank: data.data?.creator_rank || 0,
    });
  }

  results.sort((a, b) => b.engagements - a.engagements);

  console.log(`\nCreator Comparison:\n`);
  for (const r of results) {
    const ratio = r.followers > 0
      ? (r.engagements / r.followers).toFixed(1) : "N/A";
    console.log(
      `  @${r.username.padEnd(20)} ` +
      `${r.engagements.toLocaleString().padStart(15)} eng | ` +
      `${r.followers.toLocaleString().padStart(12)} followers | ` +
      `ratio: ${ratio}x | rank: #${r.rank}`
    );
  }
}

compareCreators([
  "PopBase", "Cristiano", "FabrizioRomano",
  "Rainmaker1973", "Polymarket", "NASA"
]);

The engagement-to-follower ratio reveals who is actually resonating. @PopBase gets 2,638x its follower count in engagements. @nasa gets 9.2x. Both are valuable, but they serve completely different purposes.

Build an Alert for Rising Creators

const fs = require("fs");
const STATE_FILE = "./creator_state.json";

function loadState() {
  try { return JSON.parse(fs.readFileSync(STATE_FILE)); }
  catch { return {}; }
}

function saveState(state) {
  fs.writeFileSync(STATE_FILE, JSON.stringify(state, null, 2));
}

async function checkForRising(usernames) {
  const state = loadState();

  for (const username of usernames) {
    const data = await getCreator("twitter", username);
    const current = data.data?.interactions || 0;
    const previous = state[username]?.engagements || 0;

    if (previous > 0 && current > previous * 1.5) {
      console.log(
        `RISING: @${username} engagements jumped ` +
        `${previous.toLocaleString()} -> ${current.toLocaleString()} ` +
        `(${((current/previous - 1) * 100).toFixed(0)}% increase)`
      );
    }

    state[username] = {
      engagements: current,
      timestamp: new Date().toISOString()
    };
  }

  saveState(state);
}

checkForRising([
  "PopBase", "MarioNawfal", "Rainmaker1973",
  "Polymarket", "KobeissiLetter", "sciencegirl"
]);

Run on a cron. When a creator's engagement spikes 50%+, you get alerted. Useful for spotting breaking news, viral moments, or emerging narratives.

Try It

API key: lunarcrush.com/developers
API docs: lunarcrush.com/developers/api
MCP server (use with Claude): lunarcrush.com/products/lunarcrush-mcpserver

I'm building a bunch of small projects like this. Follow along if you're into this kind of thing.

Building a Prediction Market Signal Detector with Social Sentiment Data

Joe Vezzani — Thu, 09 Apr 2026 16:20:15 +0000

Prediction markets are having a moment. Polymarket hit 112M engagements in a single day last week. Kalshi is expanding into sports. Bonding curve platforms are popping up everywhere.

But here's the thing about prediction markets: the prices move after the crowd forms an opinion. And the crowd forms its opinion on social media before it places bets.

I built a Node.js tool that compares real-time social sentiment data against prediction market topics to find those early signals -- the moments when social volume spikes or sentiment shifts before the market catches up.

GitHub repo: JoeVezzani/prediction-market-signals

The thesis

Prediction market contracts are priced by supply and demand. When a big event happens -- a policy announcement, an earnings surprise, a geopolitical shift -- social media reacts first. Millions of people start posting, sharing, arguing. That wave of activity carries a measurable sentiment signal.

If you can detect that signal early enough, you know where the prediction market is likely to move next.

LunarCrush processes 50M+ social posts per hour across X, Reddit, TikTok, YouTube, Instagram, and 10K+ news sources. That gives us the raw material to build this.

What we're building

A signal detector that:

Tracks social sentiment and volume for prediction market topics in real time
Detects anomalies -- sudden spikes in volume, sharp sentiment shifts
Compares multiple topics side by side to spot divergences
Logs signals over time so you can backtest against actual market moves

Setup

mkdir prediction-market-signals && cd prediction-market-signals
npm init -y
touch index.js

Grab an API key at lunarcrush.com/developers.

The core: pulling social data for any topic

const API_KEY = process.env.LUNARCRUSH_API_KEY;
const BASE = "https://lunarcrush.com/api4/public/topic";

async function getTopicData(keyword) {
  const res = await fetch(
    `${BASE}/${encodeURIComponent(keyword)}/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function getTimeSeries(keyword) {
  const res = await fetch(
    `${BASE}/${encodeURIComponent(keyword)}/time-series/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function getTopPosts(keyword) {
  const res = await fetch(
    `${BASE}/${encodeURIComponent(keyword)}/posts/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

Three endpoints, three views into the same conversation. Topic data gives you the current snapshot. Time series gives you the trend. Posts give you the actual content driving sentiment.

Pick your prediction market topics

The best signals come from topics where prediction markets are active and social conversation is loud. Right now, three topics fit perfectly:

Tariffs -- Will new tariffs stick? What's the economic impact? Polymarket and Kalshi both have active tariff-related contracts.
Bitcoin -- Price prediction contracts are everywhere. BTC social data is the deepest dataset LunarCrush has.
Recession -- "Will the US enter a recession in 2026?" is one of the most traded prediction market questions right now.

const TOPICS = [
  { keyword: "tariffs", label: "Tariffs / Trade Policy" },
  { keyword: "bitcoin", label: "Bitcoin Price Direction" },
  { keyword: "recession", label: "US Recession Odds" },
];

Build the signal detector

Here's where it gets interesting. We pull social data for each topic, calculate whether sentiment or volume is moving abnormally, and flag anything that looks like a leading indicator.

const fs = require("fs");
const STATE_FILE = "./signal_state.json";
const SIGNAL_LOG = "./signals.json";

function loadJSON(path) {
  try { return JSON.parse(fs.readFileSync(path, "utf-8")); }
  catch { return {}; }
}

function saveJSON(path, data) {
  fs.writeFileSync(path, JSON.stringify(data, null, 2));
}

function detectSignals(current, previous, label) {
  const signals = [];

  // Sentiment shift detection
  if (previous?.sentiment && current.sentiment) {
    const delta = current.sentiment - previous.sentiment;
    if (Math.abs(delta) >= 8) {
      signals.push({
        type: "SENTIMENT_SHIFT",
        topic: label,
        direction: delta > 0 ? "BULLISH" : "BEARISH",
        previous: previous.sentiment,
        current: current.sentiment,
        delta,
        strength: Math.abs(delta) >= 15 ? "STRONG" : "MODERATE",
      });
    }
  }

  // Volume spike detection
  if (previous?.posts && current.posts) {
    const ratio = current.posts / previous.posts;
    if (ratio >= 1.5) {
      signals.push({
        type: "VOLUME_SPIKE",
        topic: label,
        previous: previous.posts,
        current: current.posts,
        multiplier: ratio.toFixed(2) + "x",
        strength: ratio >= 3 ? "STRONG" : "MODERATE",
      });
    }
  }

  // Engagement explosion
  if (previous?.engagements && current.engagements) {
    const ratio = current.engagements / previous.engagements;
    if (ratio >= 2) {
      signals.push({
        type: "ENGAGEMENT_SURGE",
        topic: label,
        previous: previous.engagements,
        current: current.engagements,
        multiplier: ratio.toFixed(2) + "x",
      });
    }
  }

  return signals;
}

The main loop

This pulls data for all your tracked topics, compares against the last reading, and logs any signals it finds.

async function scan() {
  const state = loadJSON(STATE_FILE);
  const signalLog = loadJSON(SIGNAL_LOG);
  if (!Array.isArray(signalLog.signals)) signalLog.signals = [];

  const timestamp = new Date().toISOString();
  console.log(`\n--- Prediction Market Signal Scan: ${timestamp} ---\n`);

  for (const { keyword, label } of TOPICS) {
    const data = await getTopicData(keyword);
    const topic = data.data || {};

    const current = {
      sentiment: topic.sentiment,
      posts: topic.num_posts,
      engagements: topic.interactions,
      contributors: topic.num_contributors,
      timestamp,
    };

    console.log(`${label}`);
    console.log(`  Sentiment:    ${current.sentiment}%`);
    console.log(`  Posts (24h):  ${(current.posts || 0).toLocaleString()}`);
    console.log(`  Engagements:  ${(current.engagements || 0).toLocaleString()}`);
    console.log(`  Contributors: ${(current.contributors || 0).toLocaleString()}`);

    const previous = state[keyword];
    const signals = detectSignals(current, previous, label);

    if (signals.length > 0) {
      console.log(`  ** ${signals.length} SIGNAL(S) DETECTED **`);
      for (const sig of signals) {
        console.log(`    [${sig.strength || ""}] ${sig.type}: ${sig.direction || sig.multiplier}`);
        signalLog.signals.push({ ...sig, timestamp });
      }
    } else {
      console.log(`  No signals (within normal range)`);
    }

    state[keyword] = current;
    console.log();
  }

  saveJSON(STATE_FILE, state);
  saveJSON(SIGNAL_LOG, signalLog);
  console.log(`State saved. ${signalLog.signals.length} total signals logged.`);
}

scan();

Run it

LUNARCRUSH_API_KEY=your_key node index.js

First run establishes your baseline. Second run (and every run after) compares against the previous reading and flags anomalies:

--- Prediction Market Signal Scan: 2026-04-06T14:32:00.000Z ---

Tariffs / Trade Policy
  Sentiment:    71%
  Posts (24h):  18,075
  Engagements:  14,264,207
  Contributors: 12,744
  ** 1 SIGNAL(S) DETECTED **
    [MODERATE] SENTIMENT_SHIFT: BULLISH

Bitcoin Price Direction
  Sentiment:    75%
  Posts (24h):  172,155
  Engagements:  135,115,413
  Contributors: 68,883
  No signals (within normal range)

US Recession Odds
  Sentiment:    54%
  Posts (24h):  2,212
  Engagements:  8,258,373
  Contributors: 1,855
  ** 1 SIGNAL(S) DETECTED **
    [STRONG] SENTIMENT_SHIFT: BEARISH

State saved. 2 total signals logged.

That recession signal is exactly the kind of thing that matters. Sentiment dropping while engagement surges means people are increasingly bearish and talking about it more. On a prediction market, that's likely to push "recession" contract prices up.

Add a cross-topic divergence detector

Sometimes the most interesting signal isn't within a single topic -- it's between topics. If Bitcoin sentiment is surging while recession fears are spiking, that's a divergence worth watching.

function detectDivergences(snapshots) {
  const divergences = [];
  const entries = Object.entries(snapshots);

  for (let i = 0; i < entries.length; i++) {
    for (let j = i + 1; j < entries.length; j++) {
      const [labelA, dataA] = entries[i];
      const [labelB, dataB] = entries[j];

      if (dataA.sentiment && dataB.sentiment) {
        const gap = Math.abs(dataA.sentiment - dataB.sentiment);
        if (gap >= 20) {
          divergences.push({
            type: "SENTIMENT_DIVERGENCE",
            topicA: labelA,
            sentimentA: dataA.sentiment,
            topicB: labelB,
            sentimentB: dataB.sentiment,
            gap,
          });
        }
      }
    }
  }

  return divergences;
}

Drop this into your scan loop after collecting all the snapshots:

// After the main loop, check for cross-topic divergences
const snapshots = {};
for (const { keyword, label } of TOPICS) {
  snapshots[label] = state[keyword];
}

const divergences = detectDivergences(snapshots);
if (divergences.length > 0) {
  console.log("Cross-topic divergences:");
  for (const d of divergences) {
    console.log(
      `  ${d.topicA} (${d.sentimentA}%) vs ${d.topicB} (${d.sentimentB}%) -- ${d.gap}pt gap`
    );
  }
}

Right now this would flag:

Cross-topic divergences:
  Bitcoin Price Direction (75%) vs US Recession Odds (54%) -- 21pt gap

Bitcoin sentiment is bullish while recession sentiment is bearish. That's a real tension -- and exactly the kind of thing prediction market traders are betting on.

Run it on a schedule

Set up a cron to scan every hour and build up your signal log:

# crontab -e
0 * * * * cd /path/to/prediction-market-signals && LUNARCRUSH_API_KEY=your_key node index.js >> scan.log 2>&1

After a few days, you'll have a dataset of timestamped signals you can compare against actual prediction market price movements. That's when it gets really useful -- you can start measuring which signal types actually lead market moves and by how much.

Dig into the posts driving the signal

When a signal fires, you want to know why. The posts endpoint shows you the actual social content behind the numbers:

async function investigateSignal(keyword, label) {
  const posts = await getTopPosts(keyword);
  const topPosts = (posts.data || []).slice(0, 5);

  console.log(`\nTop posts driving "${label}" sentiment:\n`);
  for (const post of topPosts) {
    const platform = post.network || "unknown";
    const engagement = (post.interactions_total || 0).toLocaleString();
    const text = (post.body || post.title || "").slice(0, 120);
    console.log(`  [${platform}] ${engagement} engagements`);
    console.log(`  ${text}...`);
    console.log();
  }
}

// Call it when a signal fires
investigateSignal("recession", "US Recession Odds");

This is the part that turns a quantitative signal into something you can actually reason about. A sentiment drop is just a number until you see that the top 5 posts are all reacting to the same PMI report or the same oil price spike.

Why social data works for this

Prediction markets aggregate opinions into prices. Social media is where those opinions form. The sequence is:

Something happens (policy announcement, earnings, geopolitical event)
Social media reacts -- volume spikes, sentiment shifts, creators amplify
People form opinions and update their beliefs
Those beliefs get priced into prediction market contracts

LunarCrush is sitting at step 2, processing 50M+ posts per hour across X, Reddit, TikTok, YouTube, Instagram, and 10K+ news sources. By the time a prediction market contract moves, the social signal has usually already fired.

The tool we built here is simple on purpose. The point isn't to build an automated trading bot -- it's to build a detection system that tells you when something is happening in the social conversation before the market fully reflects it.

Try it yourself

Get an API key: lunarcrush.com/developers
Full API docs: lunarcrush.com/developers/api
MCP server (use with Claude): lunarcrush.com/mcp

Swap in any topic you want. Elections, Fed rate decisions, crypto launches, sports outcomes -- if people are talking about it online and betting on it in a prediction market, you can build a signal from it.

Track World Cup 2026 Social Buzz With a Simple Node.js Script

Joe Vezzani — Tue, 07 Apr 2026 15:10:58 +0000

The 2026 FIFA World Cup starts June 11 across the US, Canada, and Mexico. 48 teams for the first time ever. More matches, more upsets, more chaos -- and a ridiculous amount of social media activity.

Headlines will tell you who won. Social data tells you who the internet is actually talking about, which fan bases are the loudest, and which underdog is going viral before the pundits catch on.

I built a tracker that pulls real-time social intelligence on World Cup teams using LunarCrush's API. It processes 50M+ social posts per hour across X, Reddit, TikTok, YouTube, Instagram, and 10K+ news sources.

Here's how to build it yourself.

GitHub repo: JoeVezzani/world-cup-tracker

What we're building

A Node.js script that:

Tracks social volume, sentiment, and engagement for any World Cup team
Compares teams head-to-head (USA vs Mexico, Argentina vs Brazil, etc.)
Ranks all 48 teams by social buzz to find who's trending
Surfaces the most viral posts driving each team's conversation

Setup

mkdir world-cup-tracker && cd world-cup-tracker
npm init -y

Grab an API key at lunarcrush.com/developers.

The core functions

Two endpoints do all the heavy lifting -- one for metrics, one for posts:

const API_KEY = process.env.LUNARCRUSH_API_KEY;
const BASE = "https://lunarcrush.com/api4/public/topic";

async function getTopicData(keyword) {
  const res = await fetch(
    `${BASE}/${encodeURIComponent(keyword)}/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function getTopicPosts(keyword) {
  const res = await fetch(
    `${BASE}/${encodeURIComponent(keyword)}/posts/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

Track a single team

Start simple. Pull the social pulse on any team:

async function teamReport(team) {
  const topic = await getTopicData(team);
  const d = topic.data || {};

  console.log(`\nSOCIAL REPORT: ${team.toUpperCase()}`);
  console.log("=".repeat(50));
  console.log(`Posts (24h):       ${(d.num_posts || 0).toLocaleString()}`);
  console.log(`Engagements:       ${(d.interactions || 0).toLocaleString()}`);
  console.log(`Sentiment:         ${d.sentiment || "N/A"}% bullish`);
  console.log(`Contributors:      ${(d.num_contributors || 0).toLocaleString()}`);

  const posts = await getTopicPosts(team);
  console.log(`\nTop Posts:`);
  for (const post of (posts.data || []).slice(0, 3)) {
    const platform = post.network || "unknown";
    const engagement = (post.interactions_total || 0).toLocaleString();
    const text = (post.body || post.title || "").slice(0, 100);
    console.log(`  [${platform}] ${engagement} engagements: ${text}...`);
  }
}

teamReport("USA World Cup");

LUNARCRUSH_API_KEY=your_key node index.js

Head-to-head comparison

This is where it gets fun. Pit two rival fan bases against each other:

async function headToHead(teamA, teamB) {
  const [a, b] = await Promise.all([
    getTopicData(teamA),
    getTopicData(teamB),
  ]);

  const dataA = a.data || {};
  const dataB = b.data || {};

  console.log(`\nHEAD TO HEAD: ${teamA.toUpperCase()} vs ${teamB.toUpperCase()}`);
  console.log("=".repeat(55));

  const metrics = [
    ["Posts (24h)", "num_posts"],
    ["Engagements", "interactions"],
    ["Sentiment", "sentiment"],
    ["Contributors", "num_contributors"],
  ];

  for (const [label, key] of metrics) {
    const valA = dataA[key] || 0;
    const valB = dataB[key] || 0;
    const winner = valA > valB ? teamA : valA < valB ? teamB : "TIE";
    const format = (v) => key === "sentiment" ? `${v}%` : v.toLocaleString();
    console.log(
      `${label.padEnd(18)} ${format(valA).padStart(12)}  vs  ${format(valB).padEnd(12)}  --> ${winner}`
    );
  }
}

headToHead("USA soccer", "Mexico soccer");

Sample output:

HEAD TO HEAD: USA SOCCER vs MEXICO SOCCER
=======================================================
Posts (24h)              48,291  vs  63,817        --> Mexico soccer
Engagements           1,203,441  vs  2,891,003     --> Mexico soccer
Sentiment                   72%  vs  68%           --> USA soccer
Contributors             31,204  vs  44,109        --> Mexico soccer

Run it for any matchup. Argentina vs Brazil. England vs France. Japan vs South Korea. The data is there for all of them.

Rank all 48 teams by buzz

Here's the full leaderboard. Throw every team in, sort by social volume, see who's dominating the conversation:

const TEAMS = [
  "Argentina World Cup", "Brazil World Cup", "France World Cup",
  "England World Cup", "Germany World Cup", "Spain World Cup",
  "USA World Cup", "Mexico World Cup", "Canada World Cup",
  "Japan World Cup", "South Korea World Cup", "Australia World Cup",
  "Netherlands World Cup", "Portugal World Cup", "Belgium World Cup",
  "Croatia World Cup", "Morocco World Cup", "Senegal World Cup",
  "Uruguay World Cup", "Colombia World Cup", "Ecuador World Cup",
  "Saudi Arabia World Cup", "Qatar World Cup", "Iran World Cup",
];

async function buzzLeaderboard(teams) {
  const results = [];

  for (const team of teams) {
    const data = await getTopicData(team);
    const d = data.data || {};
    results.push({
      team,
      posts: d.num_posts || 0,
      engagements: d.interactions || 0,
      sentiment: d.sentiment || 0,
    });
    // Small delay to be polite to the API
    await new Promise((r) => setTimeout(r, 200));
  }

  results.sort((a, b) => b.posts - a.posts);

  console.log(`\nWORLD CUP 2026 - SOCIAL BUZZ LEADERBOARD`);
  console.log("=".repeat(65));
  console.log(
    `${"#".padEnd(4)}${"Team".padEnd(28)}${"Posts".padStart(10)}${"Engagements".padStart(14)}${"Sentiment".padStart(10)}`
  );
  console.log("-".repeat(65));

  results.forEach((r, i) => {
    const name = r.team.replace(" World Cup", "");
    console.log(
      `${String(i + 1).padEnd(4)}${name.padEnd(28)}${r.posts.toLocaleString().padStart(10)}${r.engagements.toLocaleString().padStart(14)}${(r.sentiment + "%").padStart(10)}`
    );
  });
}

buzzLeaderboard(TEAMS);

Add or remove teams from the array as the tournament bracket fills out. You can also swap "World Cup" for more specific queries like "USMNT" or "Les Bleus" depending on what fan communities call their team.

Find trending moments

The most interesting thing during a tournament isn't steady buzz -- it's spikes. An upset, a red card, a last-minute goal. This function checks for teams with unusual social activity:

const fs = require("fs");
const STATE_FILE = "./buzz_state.json";

function loadState() {
  try { return JSON.parse(fs.readFileSync(STATE_FILE)); }
  catch { return {}; }
}

function saveState(state) {
  fs.writeFileSync(STATE_FILE, JSON.stringify(state, null, 2));
}

async function detectSpikes(teams) {
  const state = loadState();
  const spikes = [];

  for (const team of teams) {
    const data = await getTopicData(team);
    const current = data.data?.num_posts || 0;
    const previous = state[team]?.posts || 0;

    if (previous > 0 && current > previous * 2) {
      spikes.push({
        team,
        previous,
        current,
        multiplier: (current / previous).toFixed(1),
      });
    }

    state[team] = { posts: current, timestamp: new Date().toISOString() };
    await new Promise((r) => setTimeout(r, 200));
  }

  saveState(state);

  if (spikes.length === 0) {
    console.log("\nNo major spikes detected. Check back after a match.");
    return;
  }

  spikes.sort((a, b) => b.multiplier - a.multiplier);
  console.log(`\nSPIKE ALERT - Something is happening:`);
  console.log("=".repeat(55));
  for (const s of spikes) {
    const name = s.team.replace(" World Cup", "");
    console.log(
      `${name}: ${s.previous.toLocaleString()} -> ${s.current.toLocaleString()} posts (${s.multiplier}x increase)`
    );
  }
}

detectSpikes(TEAMS);

Run this on a cron every 30 minutes during match days:

# crontab -e
*/30 * * * * cd /path/to/world-cup-tracker && LUNARCRUSH_API_KEY=your_key node spikes.js >> spikes.log

When Japan pulls off an upset against Germany (again), you'll see the spike before ESPN runs the highlight reel.

Why social data matters for sports

Traditional sports coverage tells you who scored. Social data tells you:

Which teams have the most passionate fan bases -- not just the biggest
Where the conversation is happening -- is it TikTok memes or Reddit analysis?
Sentiment shifts in real time -- a team can go from beloved to villain in 90 minutes
Underdog stories forming -- viral moments surface in social data before they hit mainstream media

With 48 teams across three countries, this World Cup is going to generate more social activity than any sporting event in history. Having a way to actually measure and compare that activity is more interesting than refreshing a score app.

Try it

API key: lunarcrush.com/developers
Full API docs: lunarcrush.com/developers/api
MCP server (use with Claude): lunarcrush.com/mcp

I Built a Tariff Impact Tracker That Shows What the Internet Actually Thinks

Joe Vezzani — Mon, 06 Apr 2026 22:07:38 +0000

Tariffs are dominating the news cycle right now. But the headlines only tell you what journalists think. What does the actual internet think?

I built a quick tracker that pulls real-time social data on tariffs -- volume, sentiment, top posts, which platforms are driving the conversation -- using LunarCrush's API. It processes 50M+ social posts per hour across X, Reddit, TikTok, YouTube, Instagram, and 10K+ news sources.

Here's what I found and how to build it yourself.

GitHub repo: JoeVezzani/tariff-tracker

What we're building

A simple Node.js script that:

Tracks social volume and sentiment for any keyword (tariffs, trade war, specific companies)
Compares how different platforms feel about the same topic
Finds the most viral posts driving the conversation
Runs on a cron to alert you when sentiment shifts

Setup (2 minutes)

mkdir tariff-tracker && cd tariff-tracker
npm init -y

Grab an API key at lunarcrush.com/developers.

Pull the data

const API_KEY = process.env.LUNARCRUSH_API_KEY;

async function getTopicData(keyword) {
  const res = await fetch(
    `https://lunarcrush.com/api4/public/topic/${encodeURIComponent(keyword)}/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function getTopicPosts(keyword) {
  const res = await fetch(
    `https://lunarcrush.com/api4/public/topic/${encodeURIComponent(keyword)}/posts/v1`,
    { headers: { Authorization: `Bearer ${API_KEY}` } }
  );
  return res.json();
}

async function main() {
  const topic = await getTopicData("tariffs");

  console.log(`\n📊 TARIFFS - Social Intelligence Report`);
  console.log(`${"=".repeat(50)}`);
  console.log(`Total posts (24h):    ${topic.data?.num_posts?.toLocaleString()}`);
  console.log(`Total engagements:    ${topic.data?.interactions?.toLocaleString()}`);
  console.log(`Sentiment:            ${topic.data?.sentiment}% bullish`);
  console.log(`Contributors:         ${topic.data?.num_contributors?.toLocaleString()}`);

  // Get the posts driving the conversation
  const posts = await getTopicPosts("tariffs");

  console.log(`\n🔥 Top Posts Right Now:`);
  for (const post of (posts.data || []).slice(0, 5)) {
    const platform = post.network || "unknown";
    const engagement = post.interactions_total?.toLocaleString() || "0";
    const text = (post.body || post.title || "").slice(0, 100);
    console.log(`  [${platform}] ${engagement} engagements: ${text}...`);
  }
}

main();

Run it

LUNARCRUSH_API_KEY=your_key node index.js

Output looks something like:

📊 TARIFFS - Social Intelligence Report
==================================================
Total posts (24h):    847,291
Total engagements:    2,341,882,104
Sentiment:            38% bullish
Contributors:         412,847

🔥 Top Posts Right Now:
  [twitter] 1,247,382 engagements: Breaking: EU announces retaliatory tariffs...
  [reddit] 892,411 engagements: Can someone ELI5 why tariffs make everything...
  [youtube] 743,219 engagements: The REAL impact of tariffs on your wallet...
  [tiktok] 521,883 engagements: POV: you work in manufacturing and just heard...
  [news] 412,092 engagements: Apple warns tariffs could increase iPhone price...

Make it interesting: compare companies

Want to see how tariffs are hitting different stocks in the social conversation?

async function compareTariffImpact(tickers) {
  const results = [];

  for (const ticker of tickers) {
    const data = await getTopicData(ticker);
    results.push({
      ticker,
      sentiment: data.data?.sentiment,
      volume: data.data?.num_posts,
      engagements: data.data?.interactions,
    });
  }

  // Sort by most negative sentiment
  results.sort((a, b) => (a.sentiment || 0) - (b.sentiment || 0));

  console.log(`\n📉 Tariff Sentiment Impact by Company:`);
  console.log(`${"Ticker":<10} ${"Sentiment":<12} ${"Posts (24h)":<15} ${"Engagements"}`);
  console.log("-".repeat(55));

  for (const r of results) {
    const mood = r.sentiment < 40 ? "😰" : r.sentiment < 60 ? "😐" : "😎";
    console.log(
      `${mood} ${r.ticker:<8} ${r.sentiment || "N/A":<12} ${(r.volume || 0).toLocaleString():<15} ${(r.engagements || 0).toLocaleString()}`
    );
  }
}

compareTariffImpact(["apple", "nike", "tesla", "walmart", "amazon"]);

Add a sentiment shift alert

Run this on a cron and get notified when public opinion changes:

const fs = require("fs");
const STATE_FILE = "./sentiment_state.json";

function loadState() {
  try { return JSON.parse(fs.readFileSync(STATE_FILE)); } 
  catch { return {}; }
}

function saveState(state) {
  fs.writeFileSync(STATE_FILE, JSON.stringify(state, null, 2));
}

async function checkForShifts(keywords) {
  const state = loadState();

  for (const keyword of keywords) {
    const data = await getTopicData(keyword);
    const current = data.data?.sentiment;
    const previous = state[keyword]?.sentiment;

    if (previous && Math.abs(current - previous) > 10) {
      const direction = current > previous ? "📈 UP" : "📉 DOWN";
      console.log(
        `🚨 ALERT: "${keyword}" sentiment shifted ${direction} ` +
        `(${previous}% -> ${current}%, change: ${current - previous > 0 ? "+" : ""}${current - previous}%)`
      );
    }

    state[keyword] = { sentiment: current, timestamp: new Date().toISOString() };
  }

  saveState(state);
}

checkForShifts(["tariffs", "trade war", "apple", "tesla", "nvidia"]);

Set it up as a cron job to run every hour:

# crontab -e
0 * * * * cd /path/to/tariff-tracker && LUNARCRUSH_API_KEY=your_key node alert.js >> alerts.log

What's cool about this

The LunarCrush API isn't just price data or news feeds. It's aggregating the actual social conversation -- 50M+ posts per hour -- across every major platform. You get:

Sentiment that's calculated from real posts, not just positive/negative keyword matching
Cross-platform comparison -- is Reddit bearish while TikTok is bullish?
Creator-level data -- who's driving the conversation?
Galaxy Score and AltRank -- proprietary metrics that rank social health

This works for crypto, stocks, topics, people -- basically anything the internet talks about.

Try it

API key: lunarcrush.com/developers
Full API docs: lunarcrush.com/developers/api
MCP server (use with Claude): lunarcrush.com/mcp

I'm building a bunch of small projects like this. Follow along if you're into this kind of thing.