Analysis

Interactive overviews and figures generated from Chapter 1 - Final results - Results.csv (the cleaned, classified finding rows). Every figure is downloadable as a CSV, SVG, or 300 ppi PNG from its own footer.

1. Overview

Table 2. Headline counts of the final corpus (Chapter 1 - Final results - Results.csv).
⬇ Download CSV Four headline counts: items in master_bibliography.csv; YES + MAYBE master items; the final-corpus item count; and how many of those have a PDF on disk.
Figure 1. Where each language looks — alluvial flow from the publication's detected Language, through every Country it discusses, into the Topic (or, in the "by CM Sub-topic" mode, the Content-moderation Sub-topic) it engages with.
⬇ Download CSV Three-stage alluvial: Language → Country → Topic (or CM Sub-topic). Each ribbon weight is the number of items that link those three values. An item that mentions several countries contributes once to each country flow; items whose Country column is blank land under "(unspecified)". The middle column is capped at the twenty most-mentioned countries; the rest are rolled into "Other (N)" so the figure fits in one viewport. The "by CM Sub-topic" mode restricts to items whose Topic is Content moderation.
Figure 2. Unique items flowing from Discipline to Topic, with a third hop to the four Sub-topics of Content moderation.
⬇ Download CSV Three-stage Sankey diagram: each ribbon is the number of unique items whose master Discipline (left) is classified by the v2 taxonomy into a given Topic (middle), and — for items whose Topic is Content moderation — into one of its four Sub-topics (right). Bands are proportional to the unique-item count and labels show the count at each stage.
Figure 3. Items per Discipline, per year, per Topic — node size = Citations. Switch to a Bump view (publications per Topic per year).
Years –
⬇ Download CSV Bubble plot. X = publication year; Y = Topic; each circle is one unique item, area scaled by citation count; colour = the item's most-frequent Discipline (top-10 highlighted, the rest grey). The Bump view aggregates the same data into one line per Topic, Y = that Topic's share of all unique items published that year (lines sum to 100 % per year) — so corpus growth doesn't dominate the trend.
Figure 4. Items per Discipline, per year, per Sub-topic — Censorship or Content-moderation framings of the WHAT taxonomy. Switch to a Bump view (publications per Sub-topic per year).
Years –
⬇ Download CSV Bubble plot of items grouped on Y by their WHAT Sub-category. The Censorship tab restricts to items whose dominant WHAT category is Censorship (lanes: Constitutive, Infrastructural, Regulative); the Content moderation tab restricts to items whose dominant WHAT category is Content moderation. X = publication year, size = Citations, colour = top-10 Discipline. The Bump view shows one line per Sub-topic, Y = that Sub-topic's share of items in the chosen Topic published that year (lines sum to 100 % per year).
Figure 5. Media categories per Topic / per Content-moderation Sub-topic — Sankey view, or per-year Bump view (each medium's share of that year's top-10 medium mentions).
⬇ Download CSV Sankey view: ribbons size = items associated with that medium. Bump view: one line per medium (top 10 in the chosen scope / slice), Y = items per publication year that mention it. The "by CM Sub-topic" mode restricts both views to items whose Topic is Content moderation. Source: the Media category column in Chapter 1 - Final results - Results.csv.

2. Analysis

Figure 6. Beeswarm per Topic — categories by year, coloured by Discipline. Switch to a Gantt / matrix view to see mentions per Category per year.
Years –
⬇ Download CSV Each dot is one finding for one item. X = publication year; vertical jitter clusters dots by Category; colour = top-10 Discipline (rest in grey); size = Citations. Hover any dot for Mentioned item, Page, Title, Author, Discipline.
Figure 7. Beeswarm for Content-moderation Sub-topics — same encodings as Fig 7.
Years –
⬇ Download CSV Same encodings as Figure 7, restricted to items whose Topic is Content moderation; vertical groups are the four Sub-topics.
Figure 8. Network — Topic → WHO → HOW → WHAT → WHY co-occurrence within items.
⬇ Edges CSV ⬇ Nodes CSV Layered network: each item links its Topic to the WHO categories it names, those WHO categories to HOW, HOW to WHAT, WHAT to WHY. Edge weight = number of unique items the link occurs in. Drag nodes; adjust the minimum edge weight to declutter.
Figure 9. Network — Content-moderation Sub-topic → WHO → HOW → WHAT → WHY.
⬇ Edges CSV ⬇ Nodes CSV Same encoding as Figure 9, restricted to items whose Topic is Content moderation; the GROUP layer holds the four Sub-topics.
Figure 10. Topic → Medium → How — three-stage alluvial mapping each Topic to the media categories it discusses, and on to the HOW mechanisms (techniques of moderation / censorship) those items describe.
⬇ Download CSV Three stages: Topic (left) → Medium (centre) → How (right). Each item contributes one unit to every (Topic × Medium × How) triple it appears in — so an item that mentions two media and annotates three HOW techniques contributes 6 units total. Source: the Type = HOW rows of Chapter 1 - Final results - Results.csv.
Figure 11. Top-10 content-moderation how techniques over time — one line per HOW category, restricted to items whose Topic is Content moderation.
Years –
⬇ Download CSV Each HOW category (Filtering, Removal, Review, Enframing, …) gets one line; Y-axis is that category's share of all top-10 HOW findings published in that year — so the ten lines at any given year sum to 100 %. Rows are taken from the Type = HOW slice of the content-moderation beeswarm CSV; an item can contribute multiple HOW lines if it codes several techniques.
Figure 12. Content-moderation who → how — alluvial of the top-10 actors against the top-10 HOW techniques most used by platforms.
⬇ Download CSV Each ribbon is an (Actor × Technique) co-occurrence within an item. The HOW column is the ten techniques most associated with Platforms across the corpus; the WHO column is the ten most-mentioned actors among items that deploy any of those techniques. Width = number of items where the pair co-occurs. Source: cm_who_how.csv (derived from beeswarm_by_cm_subtopic.csv).
Figure 14. Keyword Venn — two-word terms from the findings that are specific to one topic or shared across topics (censorship, content moderation, debate management).
⬇ Download CSV Three-set Venn over the publications' topics. Set elements are two-word terms (stopwords and generic words removed) from the verbatim quotes of the findings of the selected type, counted once per publication, in English-language publications only — so that a term is not specific to a topic merely because that literature is written in another language. A term belongs to a topic when at least three of its publications use it. Each region shows the most used terms. Closed-set taxonomy categories of the same topics are in venn_categories.csv. Source: venn_keywords.csv.