Text & Content Analysis
This page analyzes how a selected category, entity, or narrative frame appears in recent news coverage within the specified time window. Metrics and breakdowns are derived from the active article corpus.
Explore by Category
Corpus KPIs
Entity
telegram
Articles
12
From
2026-08-14 14:51:01+00
To
2026-09-12 14:06:10+00
Range
2026-08-13 22:49:26.297373+00 β
2026-09-12 22:49:26.297373+00
Whoβs being talked about
Top Entities
| Entity | Label | Mentions | Actions |
|---|---|---|---|
| telegram | PERSON | 12 | π π 𧲠|
| u.s. | GPE | 6 | π π 𧲠|
| ai | WORK_OF_ART | 4 | π π 𧲠|
| sends download | FAC | 4 | π π 𧲠|
| china | GPE | 3 | π π 𧲠|
| ORG | 3 | π π 𧲠| |
| google news | ORG | 3 | π π 𧲠|
| linkedin pinterest | ORG | 3 | π π 𧲠|
| one | CARDINAL | 3 | π π 𧲠|
| share facebook | PERSON | 3 | π π 𧲠|
| eu | ORG | 2 | π π 𧲠|
| india | GPE | 2 | π π 𧲠|
| indian | NORP | 2 | π π 𧲠|
| lev shevtsov | PERSON | 2 | π π 𧲠|
| materials news world | WORK_OF_ART | 2 | π π 𧲠|
| russia | GPE | 2 | π π 𧲠|
| science | ORG | 2 | π π 𧲠|
| 10d ago | DATE | 1 | π π 𧲠|
| 1d ago | DATE | 1 | π π 𧲠|
| 1 million | MONEY | 1 | π π 𧲠|
| 17 million | MONEY | 1 | π π 𧲠|
| 3 billion | MONEY | 1 | π π 𧲠|
| 3 million | MONEY | 1 | π π 𧲠|
| 31 million | MONEY | 1 | π π 𧲠|
| 50 million | MONEY | 1 | π π 𧲠|
Whoβs talking
Top Sources (Domains)
| Domain | Articles | % | Actions |
|---|---|---|---|
|
|
4 | 33.33 | π π° 𧲠|
|
|
3 | 25.00 | π π° 𧲠|
|
|
1 | 8.33 | π π° 𧲠|
|
|
1 | 8.33 | π π° 𧲠|
|
|
1 | 8.33 | π π° 𧲠|
|
|
1 | 8.33 | π π° 𧲠|
|
|
1 | 8.33 | π π° 𧲠|
How the stories are being framed
Top Narrative Frames
| Frame | Weight | Actions |
|---|---|---|
| Technology | 3.2600 | π 𧲠|
| Environment, Climate | 3.2600 | π 𧲠|
| Government | 1.6000 | π 𧲠|
| Business, Companies | 1.4600 | π 𧲠|
| Civil Unrest, Conflict | 1.1900 | π 𧲠|
| Friends and Family | 0.5900 | π 𧲠|
| Political Theatre | 0.2600 | π 𧲠|
| Food and Beverage | 0.1700 | π 𧲠|
| Other | 0.1600 | β |
Articles included
Top entities
Top sources
Top narrative frames