1,721,018 research outputs found
Evaluating relevance and redundancy to quantify how binary node metadata interplay with the network structure
No full text: Networks are real systems modelled through mathematical objects made up of nodes and links arranged into peculiar and deliberate (or partially deliberate) topologies. Studying these real-world topologies allows for several properties of interest to be revealed. In real networks, nodes are also identified by a certain number of non-structural features or metadata. Given the current possibility of collecting massive quantity of such metadata, it becomes crucial to identify automatically which are the most relevant for the observed structure. We propose a new method that, independently from the network size, is able to not only report the relevance of binary node metadata, but also rank them. Such a method can be applied to networks from any domain, and we apply it in two heterogeneous cases: a temporal network of technology transfer and a protein-protein interaction network. Together with the relevance of node metadata, we investigate the redundancy of these metadata displaying by the results on a Redundancy-Relevance diagram, which is able to highlight the differences among vectors of metadata from both a structural and a non-structural point of view. The obtained results provide insights of a practical nature into the importance of the observed node metadata for the actual network structure
Post-hoc Evaluation of Nodes Influence in Information Cascades: The Case of Coordinated Accounts
Full text linkIn the last years, social media has gained an unprecedented amount of attention, playing a pivotal role in shaping the contemporary landscape of communication and connection. However, Coordinated inauthentic Behaviour (CIB), defined as orchestrated efforts by entities to deceive or mislead users about their identity and intentions, has emerged as a tactic to exploit the online discourse. In this study, we quantify the efficacy of CIB tactics by defining a general framework for evaluating the influence of a subset of nodes in a directed tree. We design two algorithms that provide optimal and greedy post-hoc placement strategies that lead to maximising the configuration influence. We then consider cascades from information spreading on Twitter to compare the observed behaviour with our algorithms. The results show that, according to our model, coordinated accounts are quite inefficient in terms of their network influence, thus suggesting that they may play a less pivotal role than expected. Moreover, the causes of these poor results may be found in two separate aspects: a bad placement strategy and a scarcity of resources
Followers do not dictate the virality of news outlets on social media
Full text linkInitially conceived for entertainment, social media platforms have profoundly transformed the dissemination of information and consequently reshaped the dynamics of agenda-setting. In this scenario, understanding the factors that capture audience attention and drive viral content is crucial. Employing Gibrat's Law, which posits that an entity's growth rate is unrelated to its size, we examine the engagement growth dynamics of news outlets on social media. Our analysis includes the Facebook historical data of over a thousand news outlets, encompassing approximately 57 million posts in four European languages from 2008 to the end of 2022. We discover universal growth dynamics according to which news virality is independent of the traditional size of the outlet. Moreover, our analysis reveals a significant long-term impact of news source reliability on engagement growth, with engagement induced by unreliable sources decreasing over time. We conclude the paper by presenting a statistical model replicating the observed growth dynamics
Users Volatility on Reddit and Voat
No full textSocial media platforms behave like giant arenas where users can rely on different content and express their opinions through likes, comments, and shares. However, do users welcome different perspectives or only listen to their preferred narratives? This article examines how users explore the digital space and allocate their attention among communities on two social networks, Voat and Reddit. By analyzing a massive dataset of about 215 million comments posted by about 16 million users on Voat and Reddit in 2019, we find that most users tend to explore new communities at a decreasing rate, meaning they have a limited set of preferred groups they visit regularly. Moreover, we provide evidence that preferred communities of users tend to cover similar topics throughout the year. We also find that communities have a high turnover of users, meaning that users come and go frequently showing a high volatility that strongly departs from a null model simulating users’ behavior
The influence of coordinated behavior on toxicity
Full text linkIn the intricate landscape of social media, genuine content dissemination may be altered by a number of threats. Coordinated Behavior (CB), defined as orchestrated efforts by entities to deceive or mislead users about their identity and intentions, emerges as a tactic to exploit or manipulate online discourse. This study delves into the relationship between CB and toxic conversation on X (formerly known as Twitter). Using a dataset of 11 million tweets from 1 million users preceding the 2019 UK general election, we show that users displaying CB typically disseminate less harmful content, irrespective of political affiliation. However, distinct toxicity patterns emerge among different coordinated cohorts. Compared to their non-CB counterparts, CB participants show marginally higher toxicity levels only when considering their original posts. We further show the effects of CB-driven toxic content on non-CB users, gauging its impact based on political leanings. Our findings suggest that CB only has a limited impact on the toxicity of digital discourse.Please cite published version: https://doi.org/10.1016/j.osnem.2024.10028
From Polarization to Pro-Sociality: Measuring Beneficence in Controversial Online Conversations
No full textThis study presents a novel computational approach to quantifying beneficence, defined as a pro-social attitude that positively influences others, in the polarized context of online debates on controversial topics. Starting from a dataset of conversations on Facebook pages on controversial and polarized topics such as vaccination, we used semantic proximity measures to analyze the linguistic landscape, such as confidence, normalized Google distance, and pointwise mutual information. We built an undirected weighted co-occurrence network in which two users are connected if they both comment on the same post. We analyzed polarization trends toward the semantics of beneficence from the point of view of comments, users, and the neighborhood. We found that the formation of echo chambers on the vaccination topic did not correspond to echo chambers on beneficence, with both groups of pro-vax and anti-vax users exhibiting similar levels of beneficence in their discourse. These findings highlight the challenges of bridging the communicative gaps in communities around controversial topics that form echo chambers, showing that opposing parties can share similar beneficence levels. Future research should explore the dynamics of opinion formation and the role of beneficence in preventing and managing hate speeches
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The Drivers of Global News Spreading Patterns
Full text linkThe web radically changed the dissemination of information and the global
spread of news. In this study, we aim to reconstruct the connectivity patterns
within nations shaping news propagation globally in 2022. We do this by
analyzing a dataset of unprecedented size, containing 140 million news articles
from 183 countries and related to 37,802 domains in the GDELT database. Unlike
previous research, we focus on the sequential mention of events across various
countries, thus incorporating a temporal dimension into the analysis of news
dissemination networks. Our results show a significant imbalance in online news
spreading. We identify news superspreaders forming a tightly interconnected
rich club, exerting significant influence on the global news agenda. To further
investigate the mechanisms underlying news dissemination and the shaping of
global public opinion, we model countries' interactions using a gravity model,
incorporating economic, geographical, and cultural factors. Consistent with
previous studies, we find that countries' GDP is one of the main drivers to
shape the worldwide news agenda
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