I'll be back: Examining Restored Accounts On Twitter
Arnav Kapoor, Rishi Raj Jain, Avinash Prabhu, Tanvi Karandikar and, Ponnurangam Kumaraguru

TL;DR
This study introduces a novel dataset and methodology to identify and analyze restored Twitter accounts, revealing key profile features influencing suspension and showing behavioral changes post-restoration.
Contribution
It provides the first dataset and predictive model for restored accounts, highlighting important profile features and behavioral insights before and after suspension reversal.
Findings
Profile features like account age and retweet ratio are key for classification.
Restored accounts post-restoration tweet less, indicating reduced spam behavior.
Content similarity remains consistent pre- and post-restoration.
Abstract
Online social networks like Twitter actively monitor their platform to identify accounts that go against their rules. Twitter enforces account level moderation, i.e. suspension of a Twitter account in severe cases of platform abuse. A point of note is that these suspensions are sometimes temporary and even incorrect. Twitter provides a redressal mechanism to 'restore' suspended accounts. We refer to all suspended accounts who later have their suspension reversed as 'restored accounts'. In this paper, we release the firstever dataset and methodology 1 to identify restored accounts. We inspect account properties and tweets of these restored accounts to get key insights into the effects of suspension.We build a prediction model to classify an account into normal, suspended or restored. We use SHAP values to interpret this model and identify important features. SHAP (SHapley Additive…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Spam and Phishing Detection · Internet Traffic Analysis and Secure E-voting
