# Predicting Rising Follower Counts on Twitter Using Profile Information

## TL;DR

This study analyzes how profile information, especially first names, influences follower growth on Twitter by classifying users likely to increase followers within a month, achieving high predictive accuracy.

## Contribution

It introduces a classifier that predicts follower count increase based on profile features, focusing on the impact of names and words in user profiles.

## Key findings

- Classifier achieves AUC above 0.800
- Names and words in profiles influence discoverability
- Profiles with certain features predict follower growth

## Abstract

When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.