Tweet Moodifier: Towards giving emotional awareness to Twitter users
Name
1907.11741.pdf
Description
Accepted version
Size
2.97 MB
Format
Unknown
Checksum (MD5)
113edffd708d788aee5937def680f8ba
Author(s)
Saldias, F. Belen
Picard, Rosalind W.
Date Issued
September 2019
Journal
2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019
Publisher
IEEE
Citation
Saldias, F. Belen and Picard, Rosalind W. 2019. "Tweet Moodifier: Towards giving emotional awareness to Twitter users." 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019.
Version
Author's final manuscript
Abstract
© 2019 IEEE. Emotional contagion in online social networks has been of great interest over the past years. Previous studies have focused mainly on finding evidence of affect contagion in homophilic atmospheres. However, these studies have overlooked users' awareness of the sentiments they share and consume online. In this paper, we present an experiment with Twitter users that aims to help them better understand which emotions they experience on this social network. We introduce Tweet Moodifier (T-Moodifier), a Google Chrome extension that enables Twitter users to filter and make explicit (through colored visual marks) the emotional content in their News Feed. We compare behavioral changes between 55 participants and 5089 of their public 'friends.' The comparison period spans from two weeks before installing T-Moodifier to one week thereafter. The results suggest that the use of T-Moodifier might help Twitter users increase their emotional awareness: T-Moodifier users who had access to emotional statistics about their posts produced a significantly higher percentage of neutral content. This behavioral change suggests that people could behave differently while using real-time mechanisms that increase their affect reflection. Also, post-experience, those who completed both pre-and post-surveys could assert more confidently the main emotions they shared and perceived on Twitter. This shows T-Moodifier's potential to effectively make users reflect on their News Feed.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/acii.2019.8925533
