Election Commission
org Body which oversees elections. Wikidata
Named in 1886 articles by 27 newsrooms, in 680 stories, on 14 of the last 14 days, first on 10-15.
Most exclusively tied top 30 · ties that pass the test on the right, most exclusive first
The names that appear with Election Commission in a large share of their own coverage: this name's own company. Exclusivity runs from 0, two names that meet no more than chance would have them, to 1, two names that only ever appear together. A name that is in everything, a president, is exclusive to no one, so the big names fall away here and the names that belong to this one stay. Only ties that pass the strength test count, so a pair seen twice is not a tie. Places are context and have no page.
| Name | Kind | Exclusivity | Together |
|---|---|---|---|
| Gyanesh Kumar | person | 0.696 | 1014 |
| Bharatiya Janata Party | org | 0.612 | 372 |
| SIR | org | 0.606 | 410 |
| CEC | org | 0.566 | 376 |
| Narendra Modi | person | 0.56 | 481 |
| Special Intensive Revision | org | 0.56 | 269 |
| Cockroach Janta Party | org | 0.551 | 415 |
| Scroll | org | 0.55 | 126 |
| Opposition | org | 0.545 | 178 |
| BJP | org | 0.537 | 516 |
| ANI | org | 0.532 | 98 |
| Connaught Place | org | 0.528 | 179 |
| Election Commission of India | org | 0.521 | 161 |
| Bench | org | 0.514 | 132 |
| Rahul Gandhi | person | 0.511 | 454 |
| Delhi | gpe | 0.492 | 831 |
| Nicobar Islands | gpe | 0.489 | 13 |
| ECI | org | 0.482 | 134 |
| Delhi Police | org | 0.479 | 394 |
| Abhijeet Dipke | person | 0.475 | 321 |
| Assam | gpe | 0.468 | 60 |
| Jantar Mantar | person | 0.464 | 457 |
| INDIA bloc | loc | 0.464 | 45 |
| Melaka | org | 0.46 | 95 |
| Neha Bora | person | 0.445 | 150 |
| Hindustan Times | org | 0.434 | 49 |
| Union Home | org | 0.425 | 76 |
| Das | org | 0.423 | 73 |
| CJP | org | 0.422 | 475 |
| NDA | org | 0.416 | 36 |
Most strongly tied the ties chance explains least
How sure the record is that two names appear together for a reason. Strength is a standard statistical test (log-likelihood): above 10.8, chance would produce the pairing less than once in a thousand times. It grows with the number of articles, so the biggest names come first here; the list on the left is where this name's own company shows.
| Name | Strength | Together |
|---|---|---|
| Gyanesh Kumar | 8648 | 1014 |
| Delhi | 4660 | 831 |
| BJP | 3389 | 516 |
| Narendra Modi | 3362 | 481 |
| SIR | 3254 | 410 |
| Bharatiya Janata Party | 3032 | 372 |
| Cockroach Janta Party | 2897 | 415 |
| Rahul Gandhi | 2837 | 454 |
| CEC | 2755 | 376 |
| India | 2679 | 696 |
| Jantar Mantar | 2520 | 457 |
| Delhi Police | 2303 | 394 |
| CJP | 2296 | 475 |
| Congress | 2242 | 488 |
| Special Intensive Revision | 2037 | 269 |
Articles per day
The hubs' circles share of each hub's 30 closest names that are also linked to this one
The eight most-named figures each have an inner circle: the thirty names most tied to them. This is how much of each circle this name is also tied to. A count of shared company in the coverage, nothing more.
Shared with Congress: AFP, ANI, Aam Aadmi Party, Abhijeet Dipke, Amit Shah, Andhra Pradesh, Assembly, BJP, Bandra than Goa, Bench, Bharatiya Janata Party, Bill, Bombay High Court, Budget, CBI, CCTV, CEC, CJP, Cabinet, Chennai.
As the sources put it relations a model read from the stories' sources · shown when two or more stories say it
The words are the sources' words, read out by a model; nothing here reads intent. A relation from one story alone is not shown.
- 2 storiesElection Commission headed by as chief election commissioner Gyanesh Kumar CJP founder Abhijeet Dipke released after preventive detention in Delhi
The year 3204 articles on 45 days, 2025-10-01 to 2026-10-28 · weekly bars · 14 months on record
Left is a year ago, right is this week. Older days come from headlines only, so their counts run lower than live days.
Named together, month by month · by surprise, among that month's articles · names with a page
Stories that name Election Commission newest edition first · 40 shown
- 28 Oct
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Names are found by a language model trained to spot them, joined to one spelling per person, and counted per article. The relations are the model's reading of the sources. Everything else is a count of who was written about together; it says nothing about why.