See which sentences use
propaganda techniques

PropaLens is a Chrome extension that underlines sentences in a news article that match one of 14 known propaganda techniques, such as or . It flags rhetoric, not whether a claim is true. Think of each highlight as a signal to pause and check a claim, helping you notice persuasive language before it shapes your view. The analysis runs on a small model inside your browser, so the page text stays on your device.

Add to Chrome See how it works Try a sample article

Free. Experimental: the model makes mistakes, so treat highlights as a prompt to read more closely.

What it does

Underlines in the page

Article text underlined in different colours to mark detected propaganda techniques

Each flagged sentence gets a coloured underline. The colour shows the technique.

Short explanation on hover

Hover or tap an underline to see the technique name, a one-line definition and a Low, Medium or High confidence level.

Page summary

Hover card explaining appeal to fear or prejudice, with a confidence label and definition

The popup lists the techniques found with counts. You can hide a technique or jump to the next example.

Adjustable strictness

PropaLens settings with the strictness slider set to 50 percent and model options

A slider trades coverage for reliability: fewer highlights that are more often correct, or more highlights that include more false alarms.

Local. Models are bundled with the extension and run in the browser. No page text is uploaded.
Opt-in. Nothing is scanned until you click "Analyse this page" or enable a site.
Multilingual options. Several model choices, including multilingual ones, are available in settings.

How well does it work?

We evaluated the three bundled models on 2,087 held-out sentences derived from the PTC corpus. The original corpus labels text spans; for this evaluation, we assigned those span annotations to sentences. The chart shows the share of highlighted sentences that were labelled correctly at each strictness setting. A higher setting gives fewer highlights that are more often right. Results on other kinds of text will differ.

Line chart of precision by strictness setting for three models. Precision rises from roughly 30 to 70 percent at low strictness to 75 to 90 percent at high strictness. — Precision by strictness setting (50% is the default). 31% is what highlighting every sentence would score. Measured on an English validation set: real-world performance, especially in other languages, may vary.

Training data and method

The models are fine-tuned classifiers built from two public datasets. We changed the data as follows:

SemEval-2020 Task 11 annotates text spans. We converted these to sentence-level examples with one technique per sentence.
PropXplain texts were mapped to the SemEval technique classes with a language model, and we checked a sample by hand.
Classes with too few examples were extended with synthetic examples generated by Gemini 3.8 Flash.

SemEval-2020 Task 11 (PTC corpus): Da San Martino, Barrón-Cedeño, Wachsmuth, Petrov and Nakov, 2020. Dataset, paper. Licensed CC BY 4.0.
PropXplain: Alam et al., Findings of EMNLP 2025. Dataset, paper. MIT License.
We modified the data as described above. This project is not endorsed by the dataset authors.

Planned

PropaLens Cloud

We are considering an optional hosted service that uses a larger model and explains why a sentence was flagged. It would cost roughly $1 to $3 per month; the price and features are not final. The local version stays free. Leave your email if you want to hear when there is something to try. We will only use it for this.

We only use your email to tell you about PropaLens Cloud. See the .

See which sentences usepropaganda techniques