25 Tweets to Know You: A New Model to Predict Personality with Social Media

04/18/2017
by   Pierre-Hadrien Arnoux, et al.
0

Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state of the art techniques with 8 times fewer data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/19/2018

A Comparative Analysis of Content-based Geolocation in Blogs and Tweets

The geolocation of online information is an essential component in any g...
research
06/26/2013

Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media

There is a large amount of interest in understanding users of social med...
research
05/24/2023

Text Conditional Alt-Text Generation for Twitter Images

In this work we present an approach for generating alternative text (or ...
research
08/06/2021

A Graph Approach to Simulate Twitter Activities with Hawkes Processes

The rapid growth of social media has been witnessed during recent years ...
research
01/25/2019

Computational landscape of user behavior on social media

With the increasing abundance of 'digital footprints' left by human inte...
research
09/07/2016

Using Gaussian Processes for Rumour Stance Classification in Social Media

Social media tend to be rife with rumours while new reports are released...

Please sign up or login with your details

Forgot password? Click here to reset