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.
The canonical topics with the most engagement on social media during Week of 13 Jul 2026. 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.
reducing household energy expenses for vulnerable families
a heartfelt expression of cultural identity and commitment
criticised as reckless and detached from national needs
lack of basic personal services is a burden
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.