arXiv subjects

Edoardo Di Martino

Publications and source records attributed to Edoardo Di Martino.

7 recordsLinked to original sources

arXiv2026

Platform Sorting Drives Ideological Fragmentation in the Social Media Ecosystem

Ideological asymmetries in online political communication are often studied as localized phenomena emerging within communities. Here, we show that fragmentation instead operates at the level of entire platforms, consistent with a process of platform sorting in which users increasingly align with ideologically congruent environments. We analyze political information dynamics across Bluesky, Facebook, Reddit, Truth Social, Twitter/X, and YouTube during the 2020 and 2024 US presidential elections, combining measures of content sharing, engagement allocation, and user-level ideological orientation. Across platforms, ideological fragmentation emerges consistently and persists over time. Platforms exhibit distinct ideological profiles that persist across the two election cycles, ranging from strongly left-leaning to strongly right-leaning environments. Longitudinal analyses further reveal limited ideological variability among persistent user cohorts, indicating that apparent changes within single platforms reflect ecosystem-level sorting rather than convergence toward neutrality. Taken together, our results show that the dynamics of platform sorting is not a transient reaction to political events or moderation interventions, but a persistent structural feature of the social media ecosystem.

cs.SI↗

arXiv2026

Persistent Structural Inequality of Online Interactions Across Platforms

User interactions on social media platforms are unevenly distributed: a small subset of users consistently captures most of the activity, while the majority remains marginal. Although this pattern is well known and often described by power-law distributions, its consistency across time, platforms, and interaction types has not been systematically assessed. In this study, we analyze user-post bipartite networks from multiple social media platforms. We consider both active contributions (posts) and passive engagement (likes and comments), and quantify distributional properties and inequality using a KL-divergence-based model comparison, an inverse coefficient of variation, and a log-transformed Gini index. Our results show that interaction inequality remains stable over time within each platform. This holds across systems with different sizes, topical focuses, and governance models. These findings indicate that inequality in online engagement is not incidental but reflects structural constraints that shape how visibility and participation are distributed in digital environments.

cs.SI↗

arXiv2026

Z-Dip: a standardized measure for data modality assessment

Detecting multimodality in empirical distributions is a fundamental problem in statistics and data analysis, with applications ranging from clustering to the study of complex systems. In practice, however, assessing departures from unimodality in a consistent and comparable way remains challenging. Widely used methods such as Hartigan and Hartigan's Dip Test illustrate these difficulties, as the interpretation of their statistics depends strongly on sample size, requires calibration to determine significance, and, for large samples, exhibit increasing sensitivity, leading to rejection of unimodality for arbitrarily small deviations from the null. We introduce Z-Dip, a standardized measure of multimodality that addresses these limitations. By treating the Dip statistic as a random variable under the null hypothesis of unimodality and standardizing its observed value, the proposed approach yields scores that are directly comparable across datasets of different sizes. Using simulation-based calibration, we derive a universal decision threshold that closely reproduces classical Dip Test decisions without requiring sample-size-specific adjustments. Extensive validation on simulated data and on more than 88,000 empirical opinion distributions shows near-perfect agreement with the classical Dip Test while providing a more interpretable and comparable measure of modality. Finally, we propose a downsampling-based correction that mitigates residual sensitivity in extremely large samples. Open-source software and reference tables are provided to facilitate practical adoption.

stat.ME↗

arXiv2025

Patterns, Models, and Challenges in Online Social Media: A Survey

The rise of digital platforms has enabled the large scale observation of individual and collective behavior through high resolution interaction data. This development has opened new analytical pathways for investigating how information circulates, how opinions evolve, and how coordination emerges in online environments. Yet despite a growing body of research, the field remains fragmented and marked by methodological heterogeneity, limited model validation, and weak integration across domains. This survey offers a systematic synthesis of empirical findings and formal models. We examine platform-level regularities, assess the methodological architectures that generate them, and evaluate the extent to which current modeling frameworks account for observed dynamics. The goal is to consolidate a shared empirical baseline and clarify the structural constraints that shape inference in this domain, laying the groundwork for more robust, comparable, and actionable analyses of online social systems.

cs.SI↗

arXiv2025

Involvement drives complexity of language in online debates

Language is a fundamental aspect of human societies, continuously evolving in response to various stimuli, including societal changes and intercultural interactions. Technological advancements have profoundly transformed communication, with social media emerging as a pivotal force that merges entertainment-driven content with complex social dynamics. As these platforms reshape public discourse, analyzing the linguistic features of user-generated content is essential to understanding their broader societal impact. In this paper, we examine the linguistic complexity of content produced by influential users on Twitter across three globally significant and contested topics: COVID-19, COP26, and the Russia-Ukraine war. By combining multiple measures of textual complexity, we assess how language use varies along four key dimensions: account type, political leaning, content reliability, and sentiment. Our analysis reveals significant differences across all four axes, including variations in language complexity between individuals and organizations, between profiles with sided versus moderate political views, and between those associated with higher versus lower reliability scores. Additionally, profiles producing more negative and offensive content tend to use more complex language, with users sharing similar political stances and reliability levels converging toward a common jargon. Our findings offer new insights into the sociolinguistic dynamics of digital platforms and contribute to a deeper understanding of how language reflects ideological and social structures in online spaces.

cs.CL↗

arXiv2025

Ideological Fragmentation of the Social Media Ecosystem: From echo chambers to echo platforms

The entertainment-driven nature of social media encourages users to engage with like-minded individuals and consume content aligned with their beliefs, limiting exposure to diverse perspectives. Simultaneously, users migrate between platforms, either due to moderation policies like de-platforming or in search of environments better suited to their preferences. These dynamics drive the specialization of the social media ecosystem, shifting from internal echo chambers to "echo platforms"--entire platforms functioning as ideologically homogeneous niches. To systematically analyze this phenomenon in political discussions, we propose a quantitative approach based on three key dimensions: platform centrality, news consumption, and user base composition. We analyze 117 million posts related to the 2020 US Presidential elections from nine social media platforms--Facebook, Reddit, Twitter, YouTube, BitChute, Gab, Parler, Scored, and Voat. Our findings reveal significant differences among platforms in their centrality within the ecosystem, the reliability of circulated news, and the ideological diversity of their users, highlighting a clear divide between mainstream and alt-tech platforms. The latter occupy a peripheral role, feature a higher prevalence of unreliable content, and exhibit greater ideological uniformity. These results highlight the key dimensions shaping the fragmentation and polarization of the social media landscape.

cs.CY↗

arXiv2025

Quantifying Polarization: A Comparative Study of Measures and Methods

Political polarization, a key driver of social fragmentation, has drawn increasing attention for its role in shaping online and offline discourse. Despite significant efforts, accurately measuring polarization within ideological distributions remains a challenge. This study evaluates five widely used polarization measures, testing their strengths and weaknesses with synthetic datasets and a real-world case study on YouTube discussions during the 2020 U.S. Presidential Election. Building on these findings, we present a novel adaptation of Kleinberg's burst detection algorithm to improve mode detection in polarized distributions. By offering both a critical review and an innovative methodological tool, this work advances the analysis of ideological patterns in social media discourse.

cs.CY↗