particle_podcast_get_share_of_voice

Split a person’s, company’s or entity’s podcast mentions by who said them: the speakers’ occupations (SOC 2018) or fields of research (ANZSRC 2020). It answers “who is talking about NVIDIA: investors, engineers, journalists?” in one call. For each group you get its mention lines, its share, and the distinct speakers and episodes over a window, optionally bucketed by day, week or month.

speakers="guests" leaves hosts out.
level="detailed" splits into detailed occupations or ANZSRC groups instead of SOC major groups or ANZSRC divisions.
podcast_slug or publisher_slug narrows the split to one show or network.

Shares are of the mentions spoken by someone with an expertise read. A person listed under several codes counts in each, so shares can sum past 100%. Ad reads are left out unless include_ads is set: ad copy is not a speaker’s voice. Each group’s code is a guest_occupation (or guest_field) value. Pass it to particle_podcast_find_mentions with the same subject to read what that group said. For the mention lines themselves, use particle_podcast_find_mentions. To compare two brands’ volume over time, use particle_podcast_get_episode_timeseries.

Inputs

Field	Type	Required	Default	Description
`person_slug`	string	one of	—	Person slug from `particle_person_resolve` (e.g. `"sam-altman"`). Exactly one of `person_slug`, `company_slug` or `entity_slug` is required.
`company_slug`	string	one of	—	Company slug or domain from `particle_company_resolve` (e.g. `"nvidia"`). Resolves to the company’s linked entity.
`entity_slug`	string	one of	—	Knowledge-graph entity slug from `particle_entity_resolve`, for anything that isn’t a person or company (e.g. `"germany"`).
`by`	string	no	`occupation`	Group the speakers by `occupation` (SOC 2018) or `field` (ANZSRC 2020 fields of research).
`level`	string	no	`major`	Group level: `major` (SOC major groups or ANZSRC divisions) or `detailed` (detailed occupations or ANZSRC groups).
`speakers`	string	no	`all`	Whose lines count: `all` (hosts included) or `guests` (guests and panelists only).
`podcast_slug`	string	no	—	Only mentions inside this podcast (slug from `particle_podcast_resolve`).
`publisher_slug`	string	no	—	Only mentions on podcasts of this publisher (network), by slug.
`include_ads`	boolean	no	`false`	Count mentions inside ad reads too.
`published_after`	string	no	30 days before `published_before`	Only episodes published on or after this date (`YYYY-MM-DD`). The window may span at most 365 days.
`published_before`	string	no	now	Only episodes published on or before this date (`YYYY-MM-DD`).
`interval`	string	no	—	Add each group’s mentions per `day`, `week` or `month`.
`limit`	integer (1–50)	no	10	Groups to return, most mentioned first.

Passing no subject returns missing_parameter, and passing two returns invalid_input: call once per subject. A window longer than 365 days returns invalid_parameter. A subject slug that matches nothing returns unresolved_reference, so an unknown name is never read as “nobody mentions it”.

Output

A markdown document headed ## Share of voice — <Name> (<slug>) by <occupation|field> (<major|detailed>), then two rows:

**Window:** — start to end (end excluded). The window is [start, end), so a published_before date shows as the day after it.
**Mentions:** — the total mention lines, and how many of them were spoken by someone with an expertise read, with that share. Group shares are of the attributed lines.

A ### Groups section follows, with numbered lines formatted N. Title (code) — M mentions, share S%, P speakers, E episodes. With interval, each group gets an indented line of bucket-start count pairs joined by ·. A closing paragraph repeats that shares can sum past 100% and names the guest_occupation (or guest_field) parameter that reads a group’s lines. When no mention in the window was spoken by someone with an expertise read, a single paragraph says so and suggests widening the window. A person with no linked knowledge-graph entity gets a note instead of a split. Sample (company_slug="nvidia", speakers="guests", limit=3, truncated):

## Share of voice — NVIDIA (nvidia) by occupation (major)

**Window:** 2026-09-10 to 2026-10-11 (end excluded)
**Mentions:** 4812 lines, 2967 spoken by someone with an expertise read (61.66%); shares are of those

### Groups

1. Business and Financial Operations Occupations (13-0000) — 1194 mentions, share 40.24%, 412 speakers, 803 episodes
2. Management Occupations (11-0000) — 1071 mentions, share 36.10%, 389 speakers, 731 episodes
3. Computer and Mathematical Occupations (15-0000) — 402 mentions, share 13.55%, 141 speakers, 287 episodes

A person listed under several codes counts in each, so shares can sum past 100%. Read a group's lines with particle_podcast_find_mentions and guest_occupation=<code>.

Example

Agent calls: particle_podcast_get_share_of_voice {
  "company_slug": "nvidia",
  "by": "occupation",
  "level": "detailed",
  "speakers": "guests",
  "interval": "week"
}

Then read what the leading group said:

Agent calls: particle_podcast_find_mentions {
  "company_slug": "nvidia",
  "guest_occupation": "13-2051"
}

REST equivalent: GET /v1/podcasts/mentions/share-of-voice.
Read a group’s mention lines with particle_podcast_find_mentions and its guest_ filters.
Look up an occupation or field with particle_expertise_resolve.

Get started

Tools

particle_podcast_get_share_of_voice

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​Related

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