Every day, millions of people say what they think out loud — across platforms, in the open. Buried in that noise is the answer to almost any question about how a topic really lives in the world. The hard part isn't finding opinions; it's turning a firehose of scattered posts into a conclusion you can actually trust.
That's what this project does: it listens to social media around a specific topic or question, and works it up into validated insight.
The pipeline, end to end:
- Gather the raw signal. Data extraction across platforms — scraping, crawling, and tapping each host's own APIs — to collect what's being said about the topic in question.
- Map it as a network. The conversation becomes a graph: topics and contexts wired together, so you can see the structure of a discussion instead of scrolling through it.
- Find the communities. Graph-based community detection and clustering surface the groups inside the crowd — who talks to whom, and around what.
- Add the two axes that give it meaning: time and platform. The same topic is analyzed at different moments and on different platforms — including cross-platform — so patterns aren't mistaken for coincidences.
- Extract the trend. Comparing those snapshots over time is where trends come from: what's rising, what's fading, what shifted after an event.
- Validate before concluding. The final and most important step — checking conclusions back against the real data, so what comes out is valid, not just plausible.
What you get is not a word cloud. It's a defensible reading of how a topic moves through the public conversation — measured across time, compared across platforms, and grounded in the actual posts.
Under the hood (for the engineers): multi-platform data acquisition (scraping / crawling / API integration), graph construction and graph-based community detection, temporal and cross-platform trend analysis, and a validation pass against source data.
Why it matters (for everyone else): it turns "everyone's talking about it" into "here's exactly how, where, and since when — and here's the evidence."
Skills: Data Science · Natural Language Processing (NLP) · Graph Analysis · Web Scraping