# Profession Categories for Deepfake Adapter Classification

## Overview

The LLM annotation uses **9 specific profession categories** to classify people found in deepfake adapter datasets. These categories cover the most common types of public figures targeted by deepfake technology.

## The 9 Categories

### 1. **actor**
Film, TV, and theater performers who primarily work in scripted dramatic content.

**Examples:**
- Emma Watson (film actor)
- Scarlett Johansson (film actor)
- Bryan Cranston (TV actor)

**Keywords:** actor, actress, film, movie, cinema, theatrical, performer, drama

---

### 2. **adult performer**
People working in the adult entertainment industry.

**Examples:**
- Adult film actors
- OnlyFans creators
- Cam models

**Keywords:** adult entertainer, onlyfans, cam model, camgirl, webcam, pornographic actor, porn, pornstar

**Note:** This category is important for research on unauthorized deepfake usage, which disproportionately affects adult performers.

---

### 3. **singer/musician**
Vocalists, instrumentalists, and music performers across all genres.

**Examples:**
- IU (K-pop singer)
- Taylor Swift (singer/songwriter)
- DJ Khaled (DJ/producer)

**Keywords:** singer, musician, rapper, rap artist, band, vocalist, songwriter, composer, DJ, producer, kpop, jpop

---

### 4. **model**
Fashion, runway, and photoshoot models.

**Examples:**
- Gigi Hadid (fashion model)
- Tyra Banks (supermodel)
- Ashley Graham (plus-size model)

**Keywords:** model, fashion, runway, photoshoot, supermodel

---

### 5. **online personality**
Digital content creators, streamers, and influencers.

**Includes:**
- Streamers (Twitch, YouTube)
- Cosplayers
- YouTubers
- Instagram influencers
- Content creators
- E-girls/E-boys
- Gaming personalities

**Examples:**
- Belle Delphine (online personality, cosplayer)
- Pokimane (Twitch streamer)
- MrBeast (YouTuber)

**Keywords:** influencer, streamer, twitch, youtuber, youtube, content creator, instagrammer, instagram, e-girl, egirl, e-boy, eboy, cosplayer, gamer

---

### 6. **public figure**
Politicians, activists, journalists, authors, and other public-facing professionals not in entertainment.

**Examples:**
- Barack Obama (politician)
- Greta Thunberg (activist)
- Gordon Ramsay (chef)
- J.K. Rowling (author)

**Keywords:** politician, activist, public speaker, journalist, author, writer, chef, famous, celebrity (when not in entertainment)

---

### 7. **voice actor/ASMR**
Voice performers for animation, games, and ASMR content creators.

**Examples:**
- Tara Strong (voice actress)
- Troy Baker (voice actor)
- ASMR artists

**Keywords:** voice actor, voice actress, asmr creator, asmr

---

### 8. **sports professional**
Professional athletes and sports competitors.

**Examples:**
- Cristiano Ronaldo (soccer player)
- Serena Williams (tennis player)
- LeBron James (basketball player)

**Keywords:** athlete, sports, player, professional, competitor, olympian

---

### 9. **tv personality**
TV hosts, presenters, reality TV stars, and broadcast personalities.

**Examples:**
- Oprah Winfrey (talk show host)
- Kim Kardashian (reality TV)
- Jimmy Fallon (late night host)

**Keywords:** tv host, tv moderator, talk show host, talkshow, radio host, media personality, reality tv star, reality, comedian, presenter, broadcaster, anchor

---

## Multi-Category Classification

Many public figures work across multiple categories. The LLM can assign **up to 3 professions** per person, ordered by relevance.

### Examples:

| Person | Professions | Explanation |
|--------|-------------|-------------|
| **Emma Watson** | `actor, public figure` | Primarily an actor, but also known for activism |
| **IU (Lee Ji-eun)** | `singer/musician, actor` | K-pop singer who also acts in TV dramas |
| **Belle Delphine** | `online personality, adult performer` | Internet personality with adult content |
| **Jamie Foxx** | `actor, singer/musician` | Actor who also has a music career |
| **Dwayne Johnson** | `actor, sports professional` | Former wrestler, now primarily an actor |

## Category Selection Guidelines

### For the LLM:

1. **Choose most specific category first**
   - ✅ `"actor"` for film performers
   - ❌ `"public figure"` for someone primarily known as an actor

2. **Order by relevance**
   - Most important role first
   - Secondary roles after
   - Maximum 3 categories

3. **Be inclusive for online personalities**
   - Streamers → `"online personality"`
   - Cosplayers → `"online personality"`
   - Influencers → `"online personality"`

4. **Distinguish TV personalities from actors**
   - ✅ `"tv personality"` for talk show hosts
   - ✅ `"actor"` for scripted TV drama performers
   - Some can be both!

## Why These 9 Categories?

These categories were chosen based on:

1. **Common targets of deepfake technology**
   - Celebrities and public figures are most frequently deepfaked
   - Adult performers are disproportionately affected

2. **Clear distinctions**
   - Each category represents a distinct professional domain
   - Minimal overlap between categories

3. **Research relevance**
   - Important for analyzing demographic patterns in deepfake usage
   - Helps understand which professions are most at risk

4. **Comprehensive coverage**
   - Covers the vast majority of people in the deepfake adapter dataset
   - Includes both traditional and digital-native celebrities

## Output Format

The LLM returns professions as a **comma-separated list**:

```
"singer/musician, actor"
"online personality"
"actor, public figure, online personality"
"sports professional"
```

## Validation

After annotation, we can analyze:
- Distribution across categories
- Multi-category patterns
- Correlation with countries/regions
- Demographic patterns

Example analysis:
```python
# Count by category
df['profession_llm'].value_counts()

# Most common combinations
df['profession_llm'].value_counts().head(20)

# Filter by specific category
actors = df[df['profession_llm'].str.contains('actor', na=False)]
```

## Edge Cases

### Fictional Characters
**Handling:** Return `"Unknown"` for all fields

**Example:**
- Input: `"Elsa from Frozen"`
- Output: `profession_llm: "Unknown"`

### Unclear/Ambiguous Cases
**Handling:** Use best judgment based on primary public recognition

**Example:**
- Input: `"Elon Musk"`
- Primary recognition: Business/technology
- Category: `"public figure"`

### Multiple Equally Important Roles
**Handling:** List all relevant categories (up to 3)

**Example:**
- Input: `"Donald Glover / Childish Gambino"`
- Categories: `"actor, singer/musician, tv personality"`

## Notes for Researchers

When analyzing the annotated data:

1. **Category frequency** indicates which professions are most targeted
2. **Multi-category entries** show crossover between industries
3. **Online personality prevalence** indicates rise of digital-native celebrities in deepfakes
4. **Adult performer numbers** highlight ongoing issues with non-consensual deepfakes

## Version History

- **v1.0** (Current): Initial 9-category system
  - Refined from original detailed profession list
  - Focused on clear, distinct categories
  - Added comprehensive documentation