---
title: Difference between Explicit and Implicit data in Audiense
description: "Glossary of terms:    Enrichment Table: Explicit vs Inferred Enrichment name (All based on Twitter Data) Description Explicit/Inferred Activity balance of activity of the account in terms of how much"
---

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# Difference between Explicit and Implicit data in Audiense

**Glossary of terms:**

- **Explicit** - clearly stated, so there is no room for confusion or questions.
- **Implicit** – implied or suggested, but not clearly stated.
- **Inference** – a conclusion made based on both information/evidence and reasoning.
- **Extrapolation** - In broad terms, extrapolation can be likened to deduction or inference, which involves drawing conclusions about unknown factors from known information. 

![Screenshot 2023-10-24 at 10.28.03](https://help.audiense.com/hs-fs/hubfs/Screenshot%202023-10-24%20at%2010.28.03.png?width=688&height=408&name=Screenshot%202023-10-24%20at%2010.28.03.png)

 

Enrichment Table: Explicit vs Inferred

| **Enrichment name (All based on Twitter Data)** | **Description** | **Explicit/Inferred** |
| --- | --- | --- |
| Activity | balance of activity of the account in terms of how much original content (Tweets), conversation (mentions) and sharing (Retweets) they have published | Explicit |
| Automation | balance of activity of the account in terms of how many tweets are published manually or automatically | Explicit |
| Client App | most used Twitter client app | Explicit |
| Days | what days of the week the user is most active | Explicit |
| Devices | most used devices | Explicit |
| Engagement | amplification ratio (Retweets received/total Tweets posted) and applause (favorites received/total Tweets posted) | Explicit |
| Hours | at what time the user is most active | Explicit |
| Languages | most used languages | Explicit |
| Operating systems | most used operating systems | Explicit |
| Platforms | most used platforms | Explicit |
| admin3\_code | other administration levels, sometimes the postcode | Inferred/Modeled |
| admin4\_code | other administration levels | Inferred/Modeled |
| city |   | Inferred/Modeled |
| country | Country name | Inferred/Modeled |
| country\_code | ISO-3166 2-letter country code, 2 characters | Inferred/Modeled |
| precision | precision level, i.e. country, state, province or city | Inferred/Modeled |
| province | Province name. 2nd level in the country, administrative level | Inferred/Modeled |
| province\_code | Province code. 2nd level in the country, administrative level | Inferred/Modeled |
| state | State name. 1st level in the country administrative level | Inferred/Modeled |
| state\_code | State code. 1st level in the country administrative level | Inferred/Modeled |
| Interests | IAB interests taxonomy. 5 levels of categorization | Inferred/Modeled |
| Gender |   | Inferred/Modeled |
| Entity type |   | Inferred/Modeled |
| Affinities Influencer/Brands | Following % | Explicit |
| User ID | Twitter user ID | Explicit |
| Twitter Handle | to be translated and consistency check | Explicit |
| Name |   | Explicit |
| Location | to be updated in-app / twitter bio location | Explicit |
|   |   |   |
| User Bio |   | Explicit |
| Language |   | Explicit |
| Time zone |   | Explicit |
| Has avatar |   | Explicit |
| URL |   | Explicit |
| Has URL |   | Explicit |
| Is protected |   | Explicit |
| Is verified |   | Explicit |
| Time since last tweet |   | Explicit |
| Followers |   | Explicit |
| Following |   | Explicit |
| Tweets count |   | Explicit |
| Listed count |   | Explicit |
| Followers ratio |   | Explicit |
| Tweets per day |   | Explicit |
| Affinity |   | Explicit |
| Age | IBM Watson age recognition + Internal Heuristics | Inferred/Modeled |
| Personality Insights (Below) | IBM Watson personality insights | Inferred/Modeled |
| Consumer Preferences (Below) | IBM Watson personality insights | Inferred/Modeled |

 

 

 

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