How to Build a Social Proof Aggregator: Track Mentions Across Twitter, Reddit, and HN

#python #tutorial #webdev #programming

Social proof drives conversions. Aggregate product mentions from Reddit and HN automatically.

Reddit Search

import requests
from bs4 import BeautifulSoup
import time
from datetime import datetime, timedelta

class SocialProof:
    def __init__(self, keywords):
        self.kw = keywords
        self.mentions = []
        self.s = requests.Session()
        self.s.headers["User-Agent"] = "SocialProofBot/1.0"

    def reddit(self, limit=100):
        found = []
        for kw in self.kw:
            r = self.s.get("https://www.reddit.com/search.json",
                params={"q":kw,"sort":"new","limit":min(limit,100),"t":"month"})
            if r.status_code != 200: continue
            for p in r.json().get("data",{}).get("children",[]):
                d = p["data"]
                found.append({"platform":"reddit","text":d.get("title",""),
                    "url":f"https://reddit.com{d.get('permalink','')}",
                    "score":d.get("score",0),
                    "date":datetime.fromtimestamp(d["created_utc"]).isoformat()})
            time.sleep(1)
        return found

    def hn(self, limit=50):
        found = []
        for kw in self.kw:
            r = self.s.get("https://hn.algolia.com/api/v1/search",
                params={"query":kw,"tags":"(story,comment)","hitsPerPage":limit,
                    "numericFilters":f"created_at_i>{int((datetime.now()-timedelta(days=30)).timestamp())}"})
            for h in r.json().get("hits",[]):
                text = h.get("title") or h.get("comment_text","")
                if "<" in text: text = BeautifulSoup(text,"html.parser").get_text()
                if text: found.append({"platform":"hn","text":text[:300],
                    "url":f"https://news.ycombinator.com/item?id={h['objectID']}",
                    "score":h.get("points",0) or 0})
            time.sleep(0.5)
        return found

    def sentiment(self, text):
        t = text.lower()
        p = sum(1 for w in ["love","great","amazing","best","recommend","awesome"] if w in t)
        n = sum(1 for w in ["hate","terrible","worst","avoid","broken","scam"] if w in t)
        return "positive" if p>n else ("negative" if n>p else "neutral")

    def run(self):
        self.mentions = self.reddit() + self.hn()
        for m in self.mentions: m["sent"] = self.sentiment(m["text"])
        self.mentions.sort(key=lambda x:x.get("score",0), reverse=True)
        sent = {"positive":0,"negative":0,"neutral":0}
        for m in self.mentions: sent[m["sent"]] += 1
        print(f"{len(self.mentions)} mentions | +{sent['positive']} ~{sent['neutral']} -{sent['negative']}")
        for m in [m for m in self.mentions if m["sent"]=="positive"][:5]:
            print(f"  [{m['platform']}] {m['text'][:80]}")

SocialProof(["your-product"]).run()