Setting the Record Straighter on Shadow Banning

• URL: http://arxiv.org/abs/2012.05101v2
• Date: Tue, 13 Apr 2021 12:08:39 GMT
• Title: Setting the Record Straighter on Shadow Banning
• Authors: Erwan Le Merrer and Benoit Morgan and Gilles Tr\'edan
• Abstract summary: Shadow banning consists for an online social network in limiting the visibility of some of its users, without them being aware of it. Twitter declares that it does not use such a practice, sometimes arguing about the occurrence of "bugs" to justify restrictions on some users. This paper is the first to address the plausibility or not of shadow banning on a major online platform, by adopting both a statistical and a graph topological approach.
• Score: 3.9103337761169943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shadow banning consists for an online social network in limiting the visibility of some of its users, without them being aware of it. Twitter declares that it does not use such a practice, sometimes arguing about the occurrence of "bugs" to justify restrictions on some users. This paper is the first to address the plausibility or not of shadow banning on a major online platform, by adopting both a statistical and a graph topological approach. We first conduct an extensive data collection and analysis campaign, gathering occurrences of visibility limitations on user profiles (we crawl more than 2.5 million of them). In such a black-box observation setup, we highlight the salient user profile features that may explain a banning practice (using machine learning predictors). We then pose two hypotheses for the phenomenon: i) limitations are bugs, as claimed by Twitter, and ii) shadow banning propagates as an epidemic on user-interactions ego-graphs. We show that hypothesis i) is statistically unlikely with regards to the data we collected. We then show some interesting correlation with hypothesis ii), suggesting that the interaction topology is a good indicator of the presence of groups of shadow banned users on the service.

Related papers

• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• BeMap: Balanced Message Passing for Fair Graph Neural Network [50.910842893257275]
  We show that message passing could amplify the bias when the 1-hop neighbors from different demographic groups are unbalanced. We propose BeMap, a fair message passing method, that balances the number of the 1-hop neighbors of each node among different demographic groups.
  arXiv  Detail & Related papers  (2023-06-07T02:16:36Z)
• Analyzing Norm Violations in Live-Stream Chat [49.120561596550395]
  We study the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. Our results show that appropriate contextual information can boost moderation performance by 35%.
  arXiv  Detail & Related papers  (2023-05-18T05:58:27Z)
• Explainable Abuse Detection as Intent Classification and Slot Filling [66.80201541759409]
  We introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.
  arXiv  Detail & Related papers  (2022-10-06T03:33:30Z)
• A deep dive into the consistently toxic 1% of Twitter [9.669275987983447]
  This study spans 14 years of tweets from 122K Twitter profiles and more than 293M tweets. We selected the most extreme profiles in terms of consistency of toxic content and examined their tweet texts, and the domains, hashtags, and URLs they shared. We found that these selected profiles keep to a narrow theme with lower diversity in hashtags, URLs, and domains, they are thematically similar to each other, and have a high likelihood of bot-like behavior.
  arXiv  Detail & Related papers  (2022-02-16T04:21:48Z)
• Characterizing, Detecting, and Predicting Online Ban Evasion [9.949354222717773]
  Malicious users can easily create a new account to evade online bans. We conduct the first data-driven study of ban evasion, i.e., the act of circumventing bans on an online platform. We find that evasion child accounts demonstrate similarities with respect to their banned parent accounts on several behavioral axes.
  arXiv  Detail & Related papers  (2022-02-10T18:58:19Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights [11.266863752167678]
  Hate speech is one of the crucial issues plaguing the online social media. We run a detailed exploration of the problem space and investigate an array of models using Graph Neural Networks (GNN) We perform detailed error analysis on the best performing text and graph based models and observe that hateful users have unique network neighborhood signatures.
  arXiv  Detail & Related papers  (2021-08-01T19:13:58Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Misleading Repurposing on Twitter [3.0254442724635173]
  We present the first in-depth and large-scale study of misleading repurposing. A malicious user changes the identity of their social media account via, among other things, changes to the profile attributes in order to use the account for a new purpose while retaining their followers. We propose a definition for the behavior and a methodology that uses supervised learning on data mined from the Internet Archive's Twitter Stream Grab to flag repurposed accounts.
  arXiv  Detail & Related papers  (2020-10-20T20:19:01Z)
• Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models [2.3322477552758234]
  We propose a weak supervision deep learning model that quantitatively uncover hateful users and (ii) present a novel qualitative analysis to uncover indirect hateful conversations. We evaluate our model on 19.2M posts and show that our weak supervision model outperforms the baseline models in identifying indirect hateful interactions. We also analyze a multilayer network, constructed from two types of user interactions in Gab(quote and reply) and interaction scores from the weak supervision model as edge weights, to predict hateful users.
  arXiv  Detail & Related papers  (2020-07-24T18:13:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.