NZ political social media
This week, on Twitter, Reddit, YouTube and Facebook
Topics, framings, and platform patterns derived from the social
corpus — tweets, Reddit posts, YouTube videos, Facebook page
posts and their comment threads, plus Meta Ad Library disclosures — joined
on the same canonical topics as
discourse, press,
and audio. Cards rank by engagement-weighted volume
(likes + 2×shares + 3×replies), so a single viral
post lifts a topic the way it does on the platform itself.
Click any card for the topic detail.
Top topics — Last 7 days
The canonical topics with the most engagement on social media
in the trailing 7 days — a sliding window, so the cards keep working through the Monday handover.
The trend figure compares the selected period to its prior 4-week mean.
Stance bars (where visible) show how the AI classifier read the per-edge
framings; sentiment bars show whole-post emotional valence. Distinct
signals: a post can be supportive of a topic and negative in
tone (an angry supporter), and the lens disentangles them.
Free
Sign in free to see the AI sample framing plus the stance and sentiment bars on every top social topic.
Sign in free
Log in
How this lens works
Posts are polled from each platform's public API on a cadence
(Twitter Basic tier hourly, Reddit every 30 min, YouTube and
Facebook daily). Each
post's text runs through a topic extractor that maps it onto
the same canonical topic taxonomy used by discourse + press +
audio — so a topic that spikes here will show up on the
other lenses too. Two AI classifiers run in sequence: a 5-class
stance classifier on each (post, topic) edge
(supportive / critical / dismissive / neutral-explainer /
mocking) and a 3-class sentiment classifier on each
post (positive / neutral / negative). The two together capture
texture short-form posts compress into very few words.
