MBDef: media-bias definitions from the scientific literature

Version 1.0.0. Paper · Code · MBDef Explorer · Project page · MBDef-Gold

Researchers write about media bias and the concepts around it (framing, gatekeeping, hostile media effect, fake news, stance and many more), and they define these terms differently. MBDef collects those definitions as the papers state them, so that the field's vocabulary can be compared and organised into a taxonomy.

It holds 34,916 terms with 103,476 definitions from 6,115 papers. The MBDef Explorer shows it as a web page. The hand-labelled ground truth is a dataset of its own, MBDef-Gold. The folder explorer/ holds the Explorer's page data (150 files, listed with their SHA-256 in explorer/SHA256SUMS). It holds exactly the rows of these tables, and besides what they hold it gives every term-in-paper row a place on the map, a topic and its nearest rows, all computed on these rows alone.

The tables

Start with terms: one row per term with everything the papers say about it. The other four tables hold the rows it is built from and share ids, so any merged item can be traced to the papers that state it.

Table	One row is	Rows
terms	a term with all its definitions, description, synonyms, related terms, subtypes, supertypes and examples, merged across papers	34,916
concepts	a term: every term-in-paper whose label is the same up to spelling	34,916
instances	a term as one paper defines it ("term-in-paper"), with its relevance scores	62,066
definitions	one definition text, quoted from its paper	103,476
papers	a source paper: DOI, title, year, open-access status, licence	6,115

Every term-in-paper that the relevance filter keeps is included. 50,037 of them also pass a stricter cut (the column strict; the paper says how the cuts were set).

from datasets import load_dataset

terms = load_dataset("mediabiasgroup/MBDef", "terms", split="corpus")  # start here
concepts = load_dataset("mediabiasgroup/MBDef", "concepts", split="corpus")
instances = load_dataset("mediabiasgroup/MBDef", "instances", split="corpus")
definitions = load_dataset("mediabiasgroup/MBDef", "definitions", split="corpus")
papers = load_dataset("mediabiasgroup/MBDef", "papers", split="corpus")

Read more

Each page covers one thing; the paper has the details.

Page	What it answers
What one row is	What the five tables hold, with a worked example
Columns	What every column means
How it was made	The five steps, from the literature search to the kept terms
Which terms are kept	How a term-in-paper is scored and kept or dropped
Term merging	How spellings become one term, and how to trace a merge
The terms view	The merged items of terms and the other extracted fields
Licence and rights	What is licensed how

Limits

• No person has checked the corpus terms yet. The filter keeps 92.1% of them.
• Definitions are machine-extracted. The filter judges the term, not each text.

The paper has the evaluation and the full limits.

Licence

Our annotations are licensed CC BY 4.0. The definition texts are quotations: their copyright stays with the papers' authors and publishers. If you are a rights holder and want a quotation removed, open a discussion on this repository. Details: Licence and rights.

Citation

Citation details will follow.

Version

This card describes version 1.0.0. Each version is a commit of this repository, tagged with its number, so a version can be loaded by its revision. provenance.json lists every input file with its SHA-256, the code commit and the command that built this version.