Open-source relational map of South Korea's 22nd National Assembly — automated extraction of inter-legislator conflict from news text. Looking for methodological critique.

[u/Impressive-Judge-357]

Hi Guys. Re Hi~

I've been building a public dashboard that treats the 296 sitting members of South Korea's National Assembly as a graph, and infers edges between them from Korean-language news coverage rather than from roll-call votes. It's live and open source, and I'd genuinely like this sub to poke holes in the method.

Live board: https://korea-politician.vercel.app

Source: https://github.com/showjihyun/KoreaPolitician

What the graph contains right now — 296 Member nodes, 8 Party nodes, 296 affiliation edges, 39 directed sentiment edges (36 conflict, 3 alliance), 368 co-mention edges.

How edges are derived — A nightly job pulls articles from the politics, economy, and society sections of a major Korean news portal and resolves which legislators are named. Name resolution took the most work: Korean given names are short and frequently appear inside unrelated words, so I use longest-match masking plus a boundary check rather than substring matching.

Two different time bases, deliberately — the design decision I'd most like feedback on:

- Relations are cumulative. A conflict, once observed, is never dropped.

- Attention is a 7-day rolling window. News mentions plus log-normalized YouTube views.

Mixing a cumulative measure and a rolling one in one view risks misreading, so each panel carries its window as a visible label and a tooltip. I'm not sure that's sufficient.

Attention scoring — YouTube views are log-compressed (1k → 0, 10M → 100) before being added to the news component, and an article naming n legislators contributes 1/√n to each, so roundup pieces don't inflate everyone equally.

Where I know it's weak — the dataset is new (collection started 2026-08-30, so "cumulative" is days, not years); no inter-coder reliability on the sentiment edges; one news portal; coverage ≠ influence; conflict outnumbers alliance 36:3, which may be real or may be an extraction artifact.

What I'd like input on —
(1) Is media co-occurrence defensible as a complement to vote-based measures like W-NOMINATE, or is coverage bias fatal?
(2) What's the minimum validation before sentiment edges count as data — a hand-coded n=200 with reported kappa?
(3) Has anyone handled the cumulative-vs-rolling presentation problem well?

[u/albacore_futures]

the dataset is new (collection started 2026-08-30, so "cumulative" is days, not years); no inter-coder reliability on the sentiment edges; one news portal; coverage ≠ influence; conflict outnumbers alliance 36:3, which may be real or may be an extraction artifact.

Of these, the newness of the dataset is the biggest, with the news portal being the second-biggest. The conflict vs alliance thing is a clear error, and is likely a result of your arbitrary relationship coding categories. These are the same issues mentioned in your last post about American politics, in which you attempted to do the same thing.

The relationship categorization issue gets to the real issue this project faces, which is its reliance on media reports. You are effectively relying on secondary sources: you are collating the opinions and editorial decisions of reporters, some of which may be informed but many of which won't be. If you don't account for this - or for the outsized influence of a few individual reporters, with their implicit biases - then any analysis you run will be pointless. You must also remember that media reports exaggerate conflict to drive engagement and views, another weakness with the media-centric approach. Finally, the relationship categories you're manually assigning things to are pre-defined, by you, as opposed to being derived from a methodological framework. Those categories add another layer of abstraction on top of those we already have, which only compounds the issues.

You got pretty poor reviews on your last post because of the major methodological issues with it. The same problems still apply here. Political science theory will help inform your work, or better yet a few graduate level research methods courses. Taking the model you have now, and simply changing the countries it's applied to, will not improve your outcomes.

[u/Impressive-Judge-357 → u/albacore_futures]

Thank you for your excellent feedback.

While I don't have a background in media or political science, I launched this project after wondering, "Why isn't there information that maps out connections between politicians, business leaders, and international figures?" Relying on biased media coverage or specific news outlets can naturally lead to bias, though finding a truly unbiased source is extremely difficult. If you know of any research papers or articles that address this issue, I would be happy to review them. Please understand that I am limited to using publicly available information, but I will try to incorporate relevant details when time permits.

Thanks again for your input.