#procrastination Wednesday:
Everforest color scheme for Micro text editor. (https://github.com/zyedidia/micro). Just enough colors without too many distractions.
| GitHub | https://github.com/atomashevic |
| Website | https://www.atomasevic.com/ |
Aleksandar Tomašević
@atomasevic
- 4 Followers
- 389 Following
- 20 Posts
Color scheme is here: https://github.com/atomashevic/everforest-micro
@JASPStats sticker is feeling lonely, I need to find it some company
SIPS#REPOPSI is an open repository at @LiraLab_Bgd that researchers and students can freely use to access and share psychological scales and tests translated/adapted into Serbian or originally developed by local scientists (in any language). https://www.repopsi.f.bg.ac.rs/en/
Barney HWelcome to the fediverse! It took me a month of tweaking and building a network to get a feed I really liked. I don’t know if there’s a list of accounts on here that might appeal to you: https://github.com/nathanlesage/academics-on-mastodon
I absolutely love it here. Feels like ‘early days internet’ to me, with everyone excited to be on the platform meeting new people.
RE: https://mastodon.social/users/urban_formation/statuses/110926335363353251
I absolutely love it here. Feels like ‘early days internet’ to me, with everyone excited to be on the platform meeting new people.
RE: https://mastodon.social/users/urban_formation/statuses/110926335363353251
@damonyoung Love it. I read his biography (don't rember which one) and once he was visiting a university and staying over at colleague's house. They asked what would he like for breakfast and his answer was something like "I don't care as long its the same thing every day"
🚨 New preprint! Our work on machine learning approach to facial emotion detection in political speeches in on ArXiv.
https://arxiv.org/abs/2304.09914
We processed 77 hours of video and found that populist leaders express negative emotions more often than their non-populist counterparts.
The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning
Populist rhetoric employed on online media is characterized as deeply impassioned and often imbued with strong emotions. The aim of this paper is to empirically investigate the differences in affective nonverbal communication of political leaders. We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries, analyze their facial expressions of emotion and then examine differences in average emotion scores representing the relative presence of 6 emotional states (anger, disgust, fear, happiness, sadness, and surprise) and a neutral expression for each frame of the YouTube video. Based on a sample of manually coded images, we find that this deep-learning approach has 53-60\% agreement with human labels. We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
@lwpembleton Love the esthetics, definitely someting pleasurable there. Never found the space/time to do this, but I need this.
@harriorrihar Amazing, thanks for sharing. Your posts are the #1 thing I look forward to when I open Mastodon.