Clicks, comments, consequences: Are content creators' socio-structural and platform characteristics shaping the exposure to negative sentiment, offensive language, and hate speech on YouTube?
- URL: http://arxiv.org/abs/2504.07676v1
- Date: Thu, 10 Apr 2025 11:58:56 GMT
- Title: Clicks, comments, consequences: Are content creators' socio-structural and platform characteristics shaping the exposure to negative sentiment, offensive language, and hate speech on YouTube?
- Authors: Sarah Weißmann, Aaron Philipp, Roland Verwiebe, Chiara Osorio Krauter, Nina-Sophie Fritsch, Claudia Buder,
- Abstract summary: This study investigates how socio-structural characteristics such as the age, gender, and race of CCs but also platform features play a role.<n>We conduct a comprehensive analysis combining digital trace data, enhanced with hand-coded variables to include socio-structural characteristics in social media data.<n>Contrary to existing studies our findings indicate that female content creators are confronted with less negative communication.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Receiving negative sentiment, offensive comments, or even hate speech is a constant part of the working experience of content creators (CCs) on YouTube - a growing occupational group in the platform economy. This study investigates how socio-structural characteristics such as the age, gender, and race of CCs but also platform features including the number of subscribers, community strength, and the channel topic shape differences in the occurrence of these phenomena on that platform. Drawing on a random sample of n=3,695 YouTube channels from German-speaking countries, we conduct a comprehensive analysis combining digital trace data, enhanced with hand-coded variables to include socio-structural characteristics in social media data. Publicly visible negative sentiment, offensive language, and hate speech are detected with machine- and deep-learning methods using N=40,000,000 comments. Contrary to existing studies our findings indicate that female content creators are confronted with less negative communication. Notably, our analysis reveals that while BIPoC, who work as CCs, receive significantly more negative sentiment, they aren't exposed to more offensive comments or hate speech. Additionally, platform characteristics also play a crucial role, as channels publishing content on conspiracy theories or politics are more frequently subject to negative communication.
Related papers
- The Monetisation of Toxicity: Analysing YouTube Content Creators and Controversy-Driven Engagement [1.3108652488669736]
This paper presents a quantitative analysis of controversial content on YouTube, focusing on the relationship between controversy, toxicity, and monetisation.
We introduce a curated dataset comprising 20 controversial YouTube channels extracted from Reddit discussions, including 16,349 videos and more than 105 million comments.
We identify and categorise monetisation cues from video descriptions into various models, including affiliate marketing and direct selling.
Our findings reveal that while toxic comments correlate with higher engagement, they negatively impact monetisation, indicating that controversy-driven interaction does not necessarily lead to financial gain.
arXiv Detail & Related papers (2024-08-01T13:10:35Z) - Exploratory Data Analysis on Code-mixed Misogynistic Comments [0.0]
We present a novel dataset of YouTube comments in mix-code Hinglish.
These comments have been weak labelled as Misogynistic' and Non-misogynistic'
arXiv Detail & Related papers (2024-03-09T23:21:17Z) - Shifting Climates: Climate Change Communication from YouTube to TikTok [0.0]
We studied the video content produced by 21 prominent YouTube creators who have expanded their influence to TikTok as information disseminators.
We found that creators use a more emotionally resonant, self-referential, and action-oriented language compared to YouTube.
We also observed a strong semantic alignment between videos and comments, with creators who excel at diversifying their TikTok content from YouTube typically receiving responses that more closely align with their produced content.
arXiv Detail & Related papers (2023-12-08T11:10:10Z) - 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) - CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a
Context Synergized Hyperbolic Network [52.85130555886915]
CoSyn is a context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations.
We show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.
arXiv Detail & Related papers (2023-03-02T17:30:43Z) - Automated Sentiment and Hate Speech Analysis of Facebook Data by
Employing Multilingual Transformer Models [15.823923425516078]
We analyse the statistical distribution of hateful and negative sentiment contents within a representative Facebook dataset.
We employ state-of-the-art, open-source XLM-T multilingual transformer-based language models to perform sentiment and hate speech analysis.
arXiv Detail & Related papers (2023-01-31T14:37:04Z) - Countering Malicious Content Moderation Evasion in Online Social
Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems.
This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
arXiv Detail & Related papers (2022-12-27T16:08:49Z) - Classifying YouTube Comments Based on Sentiment and Type of Sentence [0.0]
We address the challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models.
The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.
arXiv Detail & Related papers (2021-10-31T18:08:10Z) - 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) - Information Consumption and Social Response in a Segregated Environment:
the Case of Gab [74.5095691235917]
This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic.
We find that there are no strong statistical differences in the social response to questionable and reliable content.
Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
arXiv Detail & Related papers (2020-06-03T11:34:25Z) - Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media
during the COVID-19 Crisis [51.39895377836919]
COVID-19 has sparked racism and hate on social media targeted towards Asian communities.
We study the evolution and spread of anti-Asian hate speech through the lens of Twitter.
We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
arXiv Detail & Related papers (2020-05-25T21:58:09Z)
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.