# Celebrity Profiling 2019

## Synopsis

• Task: Given a celebrity's Twitter feed, determine its owner's age, fame, gender, and occupation.
• Input: [data]
• Output: [verifier]
• Evaluation: [code]
• Submission: [submit]
• Baselines: none.

Celebrities are among the most prolific users of social media, promoting their personas and rallying followers. This activity is closely tied to genuine writing samples, rendering them worthy research subjects in many respects, not least author profiling.

The Celebrity Profiling task this year is to predict four traits of a celebrity from their social media communication. The traits are the degree of fame, occupation, age, and gender. The social media communication is given as the teaser messages from past tweets. The goal is to develop a piece of software which predicts celebrity traits from the teaser history.

Total Dataset Size 48,335 User Profiles
text size 2,181 Tweets avg. per User
novel traits Fame and Occupation
New Attributes Detailed Birthyears and Nonbinary Gender.

## Data

The training dataset contains of two files: a feeds.ndjson as input and a labels.ndjson as output. Each file lists all celebrities as JSON objects, one per line and identified by the id key.

### Input Format

The input file contains the cid and a list of all teaser messages for each celebrity.

{"id": 1234, "text": ["a tweet", "another tweet", ...]}
{"id": 5678, "text": ["a tweet", "another tweet", ...]}
...

feeds.ndjson

### Output Format

The output file contains the cid and and a value for each trait for each celebrity from the input file.

{"id": 1234, "fame": "star", "occupation": "sports", "gender": "female", "birthyear": 2002}
{"id": 5678, "fame": "rising", "occupation": "professional", "gender": "male", "birthyear": 1990}
...

labels.ndjson
The following values are possible for each of the traits:

fame        := {rising, star, superstar}
occupation  := {sports, performer, creator, politics, manager,
science, professional, religious}
birthyear   := {1940, ..., 2012}
gender      := {male, female, nonbinary}

possible value instances for each label

## Evaluation

Submissions are judged by a combined metric cRank, which is the harmonic mean of each label's metric. $$\text{cRank} = {4 \over {\frac{1}{\text{F}_{1, \text{fame}}} + \frac{1}{\text{F}_{1, \text{occupation}}} + \frac{1}{\text{F}_{1, \text{gender}}} + \frac{1}{\text{F}_{1, \text{age}}}}}$$ All traits are judged by their respective F1. Precision and recall of birthyear are calculated leniently. If a prediction is within an m-window of the truth, it is counted as correct: $$\text{true birthyear} - m \le \text{predicted birthyear} \le \text{true birthyear} + m$$ The window size m is based on the birth year and increases linearly from about 2 years for 2012 to about 9 years for 1940.

## Submission

For evaluation, your software will read a feeds.ndjson file from a given directory and write a valid labels.ndjson with your predictions to a given output directory.

## Results

Extended Overview

team test-dataset1 test-dataset2
cRank gender age fame occup cRank gender age fame occup
radivchev 0.593 0.726 0.618 0.551 0.515 0.559 0.609 0.657 0.548 0.461
morenosandoval 0.541 0.644 0.518 0.563 0.469 0.497 0.561 0.516 0.518 0.418
martinc 0.462 0.580 0.361 0.517 0.449 0.465 0.594 0.347 0.507 0.486
fernquist 0.424 0.447 0.339 0.493 0.449 0.413 0.465 0.467 0.482 0.300
petrik 0.377 0.595 0.255 0.480 0.340 0.441 0.555 0.360 0.526 0.385
asif - - - - - 0.402 0.588 0.254 0.504 0.427
pelzer 0.331 0.244 0.418 0 0.178
bryan - - - - - 0.231 0.335 0.207 0.289 0.165
baseline-rand 0.223 0.344 0.123 0.341 0.125 - - - - -
baseline-uniform 0.138 0.266 0.117 0.099 0.152 - - - - -
baseline-majority 0.136 0.278 0.071 0.285 0.121 - - - - -
• Matti Wiegmann, Benno Stein, Martin Potthast. Celebrity Profiling. To appear in 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), July 2019. Association for Computational Linguistics.