Data Skeptic10 Apr 2015

Computer-based Personality Judgments

Guest Youyou Wu discuses the work she and her collaborators did to measure the accuracy of computer based personality judgments. Using Facebook "like" data, they found that machine learning approaches could be used to estimate user's self assessment of the "big five" personality traits: openness, agreeableness, extraversion, conscientiousness, and neuroticism. Interestingly, the computer-based assessments outperformed some of the assessments of certain groups of human beings. Listen to the episode to learn more.

The original paper Computer-based personality judgements are more accurate than those made by humansappeared in the January 2015 volume of the Proceedings of the National Academy of Sciences (PNAS).

For her benevolent Youyou recommends Private traits and attributes are predictable from digital records of human behavior by Michal Kosinski, David Stillwell, and Thore Graepel. It's a similar paper by her co-authors which looks at demographic traits rather than personality traits.

And for her self-serving recommendation, Youyou has a link that I'm very excited about. You can visitApplyMagicSauce.com to see how this model evaluates your personality based on your Facebook like information. I'd love it if listeners participated in this research and shared your perspective on the results via The Data Skeptic Podcast Facebook page. I'm going to be posting mine there for everyone to see.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(601)

Github Collaboration Network

In this episode we discuss the GitHub Collaboration Network with Behnaz Moradi-Jamei, assistant professor at James Madison University. As a network scientist, Behnaz created and analyzed a network of...

11 Nov 202442min

Graphs and ML for Robotics

We are joined by Abhishek Paudel, a PhD Student at George Mason University with a research focus on robotics, machine learning, and planning under uncertainty, using graph-based methods to enhance rob...

4 Nov 202441min

Graphs for HPC and LLMs

We are joined by Maciej Besta, a senior researcher of sparse graph computations and large language models at the Scalable Parallel Computing Lab (SPCL). In this episode, we explore the intersection of...

29 Okt 202452min

Graph Databases and AI

In this episode, we sit down with Yuanyuan Tian, a principal scientist manager at Microsoft Gray Systems Lab, to discuss the evolving role of graph databases in various industries such as fraud detect...

21 Okt 202435min

Network Analysis in Practice

Our new season "Graphs and Networks" begins here! We are joined by new co-host Asaf Shapira, a network analysis consultant and the podcaster of NETfrix – the network science podcast. Kyle and Asaf di...

14 Okt 202429min

Animal Intelligence Final Exam

Join us for our capstone episode on the Animal Intelligence season. We recap what we loved, what we learned, and things we wish we had gotten to spend more time on. This is a great episode to see how ...

7 Okt 202430min

Process Mining with LLMs

David Obembe, a recent University of Tartu graduate, discussed his Masters thesis on integrating LLMs with process mining tools. He explained how process mining uses event logs to create maps that ide...

24 Sep 202426min

Open Animal Tracks

Our guest today is Risa Shinoda, a PhD student at Kyoto University Agricultural Systems Engineering Lab, where she applies computer vision techniques. She talked about the OpenAnimalTracks dataset and...

17 Sep 202422min