Mapping the Italian Telegram Ecosystem
- URL: http://arxiv.org/abs/2504.19594v1
- Date: Mon, 28 Apr 2025 08:58:18 GMT
- Title: Mapping the Italian Telegram Ecosystem
- Authors: Lorenzo Alvisi, Serena Tardelli, Maurizio Tesconi,
- Abstract summary: We conduct a large-scale analysis of the Italian Telegram sphere, leveraging a dataset of 186 million messages from 13,151 chats collected in 2023.<n>Using network analysis, Large Language Models, and toxicity detection tools, we examine how different thematic communities form, align ideologically, and engage in harmful discourse.<n>We find that Italian discourse primarily targets Black people, Jews, and gay individuals independently of the topic.
- Score: 0.20482269513546458
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Telegram has become a major space for political discourse and alternative media. However, its lack of moderation allows misinformation, extremism, and toxicity to spread. While prior research focused on these particular phenomena or topics, these have mostly been examined separately, and a broader understanding of the Telegram ecosystem is still missing. In this work, we fill this gap by conducting a large-scale analysis of the Italian Telegram sphere, leveraging a dataset of 186 million messages from 13,151 chats collected in 2023. Using network analysis, Large Language Models, and toxicity detection tools, we examine how different thematic communities form, align ideologically, and engage in harmful discourse within the Italian cultural context. Results show strong thematic and ideological homophily. We also identify mixed ideological communities where far-left and far-right rhetoric coexist on particular geopolitical issues. Beyond political analysis, we find that toxicity, rather than being isolated in a few extreme chats, appears widely normalized within highly toxic communities. Moreover, we find that Italian discourse primarily targets Black people, Jews, and gay individuals independently of the topic. Finally, we uncover common trend of intra-national hostility, where Italians often attack other Italians, reflecting regional and intra-regional cultural conflicts that can be traced back to old historical divisions. This study provides the first large-scale mapping of the Italian Telegram ecosystem, offering insights into ideological interactions, toxicity, and identity-targets of hate and contributing to research on online toxicity across different cultural and linguistic contexts on Telegram.
Related papers
- Talking Point based Ideological Discourse Analysis in News Events [62.18747509565779]
We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.
Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion.
We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
arXiv Detail & Related papers (2025-04-10T02:52:34Z) - Polarized Patterns of Language Toxicity and Sentiment of Debunking Posts on Social Media [5.301808480190602]
The rise of misinformation and fake news in online political discourse poses significant challenges to democratic processes and public engagement.<n>We examined over 86 million debunking tweets and more than 4 million Reddit debunking comments to investigate the relationship between language toxicity, pessimism, and social polarization in debunking efforts.<n>We show that platform architecture affects informational complexity of user interactions, with Twitter promoting concentrated, uniform discourse and Reddit encouraging diverse, complex communication.
arXiv Detail & Related papers (2025-01-10T08:00:58Z) - Community Shaping in the Digital Age: A Temporal Fusion Framework for Analyzing Discourse Fragmentation in Online Social Networks [45.58331196717468]
This research presents a framework for analyzing the dynamics of online communities in social media platforms.
By combining text classification and dynamic social network analysis, we uncover mechanisms driving community formation and evolution.
arXiv Detail & Related papers (2024-09-18T03:03:02Z) - Anti-woke agenda, gender issues, revisionism and hate speech communities on Brazilian Telegram: from harmful reactionary speech to the crime of glorifying Nazism and Hitler [0.0]
This study is part of a series of seven studies whose main objective is to understand and characterize Brazilian conspiracy theory communities on Telegram.
Anti-woke communities emerge as central forces in the Brazilian conspiracy ecosystem.
During crises, mentions of hate speech and revisionism have increased significantly.
The interconnectivity between anti-woke, anti-gender and revisionism strengthens the ecosystem of hate.
arXiv Detail & Related papers (2024-08-31T01:56:19Z) - The Constant in HATE: Analyzing Toxicity in Reddit across Topics and Languages [2.5398014196797605]
Toxic language remains an ongoing challenge on social media platforms.
This paper provides a cross-topic and cross-lingual analysis of toxicity in Reddit conversations.
arXiv Detail & Related papers (2024-04-29T14:14:33Z) - Echo-chambers and Idea Labs: Communication Styles on Twitter [51.13560635563004]
This paper investigates the communication styles and structures of Twitter (X) communities within the vaccination context.
By shedding light on the nuanced nature of communication within social networks, this study emphasizes the significance of understanding the diversity of perspectives within online communities.
arXiv Detail & Related papers (2024-03-28T13:55:51Z) - Evolving linguistic divergence on polarizing social media [0.0]
We quantify divergence in topics of conversation and word frequencies, messaging sentiment, and lexical semantics of words and emoji.
While US American English remains largely intelligible within its large speech community, our findings point at areas where miscommunication may arise.
arXiv Detail & Related papers (2023-09-04T15:21:55Z) - The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.
We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
arXiv Detail & Related papers (2023-04-19T18:32:49Z) - Collective moderation of hate, toxicity, and extremity in online
discussions [1.114199733551736]
We analyze a large corpus of more than 130,000 discussions on Twitter over four years.
We identify different dimensions of discourse that might be related to the probability of hate speech in subsequent tweets.
We find that expressing simple opinions, not necessarily supported by facts, relates to the least hate in subsequent discussions.
arXiv Detail & Related papers (2023-03-01T09:35:26Z) - Annotators with Attitudes: How Annotator Beliefs And Identities Bias
Toxic Language Detection [75.54119209776894]
We investigate the effect of annotator identities (who) and beliefs (why) on toxic language annotations.
We consider posts with three characteristics: anti-Black language, African American English dialect, and vulgarity.
Our results show strong associations between annotator identity and beliefs and their ratings of toxicity.
arXiv Detail & Related papers (2021-11-15T18:58:20Z) - ExtremeBB: A Database for Large-Scale Research into Online Hate,
Harassment, the Manosphere and Extremism [12.647120939857635]
We introduce ExtremeBB, a textual database of over 53.5M posts made by 38.5k users on 12 extremist bulletin board forums promoting online hate, harassment, the manosphere and other forms of extremism.
It enables large-scale analyses of qualitative and quantitative historical trends going back two decades.
ExtremeBB comes with a robust ethical data-sharing regime that allows us to share data with academics worldwide.
arXiv Detail & Related papers (2021-11-08T13:15:25Z) - Reaching the bubble may not be enough: news media role in online
political polarization [58.720142291102135]
A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations.
This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
arXiv Detail & Related papers (2021-09-18T11:34:04Z) - Analysing Social Media Network Data with R: Semi-Automated Screening of
Users, Comments and Communication Patterns [0.0]
Communication on social media platforms is increasingly widespread across societies.
Fake news, hate speech and radicalizing elements are part of this modern form of communication.
A basic understanding of these mechanisms and communication patterns could help to counteract negative forms of communication.
arXiv Detail & Related papers (2020-11-26T14:52:01Z) - 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) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
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.