arXiv subjects

Guglielmo Cola

Publications and source records attributed to Guglielmo Cola.

4 recordsLinked to original sources

arXiv2025

From Toxicity to Conformity: Adaptive user behavior to social norms in Telegram communities

Toxic and antisocial user behavior on social media platforms has received considerable scholarly attention due to its detrimental effects on society. This study takes a holistic perspective on the phenomenon of online toxicity by investigating the impact of local community norms on toxic expression. By using six large-scale datasets, comprising over 500 million Telegram messages collected between 2015 and 2024, we analyze toxic user behavior across multiple chats and languages. We introduce a methodological framework that models user adaptation through a conformity index, capturing conformist, anti-conformist, and independent behavioral tendencies. Our findings show that most users tend to conform to local normative environments, adjusting their toxicity to match the toxicity levels of the chats in which they participate. These patterns are consistent across datasets and languages, suggesting that community norms and social influence play a decisive role in shaping user behavior online. Furthermore, we demonstrate that exposure to these norms, in terms of increased user participation in chats, is associated with a stronger tendency toward conformity with the surrounding social contexts. Collectively, these findings contribute to a deeper understanding of toxic online behavior and highlight the importance of contextualized approaches to content moderation.

cs.SI↗

arXiv2024

From Tweet to Theft: Tracing the Flow of Stolen Cryptocurrency

This paper presents a case study of a cryptocurrency scam that utilized coordinated and inauthentic behavior on Twitter. In 2020, 143 accounts sold by an underground merchant were used to orchestrate a fake giveaway. Tweets pointing to a fake blog post lured victims into sending Uniswap tokens (UNI) to designated addresses on the Ethereum blockchain, with the false promise of receiving more tokens in return. Using one of the scammer's addresses and leveraging the transparency and immutability of the Ethereum blockchain, we traced the flow of stolen funds through various addresses, revealing the tactics adopted to obfuscate traceability. The final destination of the funds involved two deposit addresses. The first, managed by a well-known cryptocurrency exchange, was likely associated with the scammer's own account on that platform and saw deposits exceeding $3.5 million. The second address was linked to a popular cryptocurrency swap service. These findings highlight the critical need for more stringent measures to verify the source of funds and prevent illicit activities.

cs.SI↗

arXiv2024

Unveiling Online Conspiracy Theorists: a Text-Based Approach and Characterization

In today's digital landscape, the proliferation of conspiracy theories within the disinformation ecosystem of online platforms represents a growing concern. This paper delves into the complexities of this phenomenon. We conducted a comprehensive analysis of two distinct X (formerly known as Twitter) datasets: one comprising users with conspiracy theorizing patterns and another made of users lacking such tendencies and thus serving as a control group. The distinguishing factors between these two groups are explored across three dimensions: emotions, idioms, and linguistic features. Our findings reveal marked differences in the lexicon and language adopted by conspiracy theorists with respect to other users. We developed a machine learning classifier capable of identifying users who propagate conspiracy theories based on a rich set of 871 features. The results demonstrate high accuracy, with an average F1 score of 0.88. Moreover, this paper unveils the most discriminating characteristics that define conspiracy theory propagators.

cs.SI↗

arXiv2023

Modularity-based approach for tracking communities in dynamic social networks

Community detection is a crucial task to unravel the intricate dynamics of online social networks. The emergence of these networks has dramatically increased the volume and speed of interactions among users, presenting researchers with unprecedented opportunities to explore and analyze the underlying structure of social communities. Despite a growing interest in tracking the evolution of groups of users in real-world social networks, the predominant focus of community detection efforts has been on communities within static networks. In this paper, we introduce a novel framework for tracking communities over time in a dynamic network, where a series of significant events is identified for each community. Our framework adopts a modularity-based strategy and does not require a predefined threshold, leading to a more accurate and robust tracking of dynamic communities. We validated the efficacy of our framework through extensive experiments on synthetic networks featuring embedded events. The results indicate that our framework can outperform the state-of-the-art methods. Furthermore, we utilized the proposed approach on a Twitter network comprising over 60,000 users and 5 million tweets throughout 2020, showcasing its potential in identifying dynamic communities in real-world scenarios. The proposed framework can be applied to different social networks and provides a valuable tool to gain deeper insights into the evolution of communities in dynamic social networks.

cs.SI↗