Graduation Semester and Year
2022
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Shirin Nilizadeh
Abstract
Social media platforms have brought people from different backgrounds, ethnicity, race, gender, etc together to form a platform to share ideas and opinions and discuss news events among other social events. Unfortunately, these platforms have also been a safe haven for abusive users who harass, bully other users or spread misinformation and disinformation. Social media platforms have a huge incentive to police these abusive users and keep them in check to allow other genuine users to use their platform. Social media platforms employ several different content moderation techniques to perform this task. These techniques vary across platforms, for example, Parler believes in using the least restrictive moderation policies and having open discussion spaces for their users. These policies were used by several members responsible for the 2021 US Capitol Riots. On January 12, 2021, Parler a social media platform popular among conservative users was removed from the Apple App store, the Google Play Store, and Amazon Web Services. This was blamed on Parler’s refusal to remove posts inciting violence following the 2021 US Capitol Riots. To return to the app stores, Parler would have to modify their moderation policies drastically. Shortly before being banned from Amazon Web Services, a Twitter user, donk_enby, published frameworks and methodology for scraping Parler using their open API service. Studies like Aliapoulios et al. used this opportunity to collect a dataset of posts from Parler and record user information. After a month of downtime, with a new cloud service provider and a new set of user guidelines, Parler was back online. Our study looks into the moderation changes performed by Parler and studies any noticeable differences in user behavior. Using Google’s Perspective API, we notice a decrease in the toxicity content shared in posts. We also notice similar trends in other labels such as identity attack, insult, severe toxicity, profanity, and threats. We study the most popular topics being talked about on Parler and compare other topics to uncover any changes in the topics of discussion. Finally, the Media Bias Fact Check service also checks the factuality of a sample of news websites being shared. We find an increase in the factuality in the news sites being shared. We also notice a decrease in the number of questionable sources and conspiracy or pseudoscience sources being shared.
Keywords
Social media, Privacy, Capitol riots, Online moderation, Perspective api, Media bias fact check, Deplatforming
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Kumarswamy, Nihal, "“Strict Moderation?” The Impact of Increased Moderation on Parler Content and User Behavior" (2022). Computer Science and Engineering Theses. 460.
https://mavmatrix.uta.edu/cse_theses/460
Comments
Degree granted by The University of Texas at Arlington