1,721,088 research outputs found
Social Network Analysis with Python
No full textThis set of tutorials provides an introduction to Social Network Analysis in Python with networkx.
Topics covered:
* Introduction to NetworkX
* NDlib: Network Diffusion library
* NDlib-REST: remote network diffusion experiments
* Community Discovery
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Trends and topics: Characterizing echo chambers’ topological stability and in-group attitudes
Full text linkNowadays, online debates focusing on a wide spectrum of topics are often characterized by clashes of polarized communities, each fiercely supporting a specific stance. Such debates are sometimes fueled by the presence of echo chambers, insulated systems whose users’ opinions are exacerbated due to the effect of repetition and by the active exclusion of opposite views. This paper offers a framework to explore how echo chambers evolve through time, considering their users’ interaction patterns and the content/attitude they convey while addressing specific controversial issues. The framework is then tested on three Reddit case studies focused on sociopolitical issues (gun control, American politics, and minority discrimination) during the first two years and a half of Donald Trump’s presidency and on an X/Twitter dataset involving BLM discussion tied to the EURO 2020 football championship. Analytical results unveil that polarized users will likely keep their affiliation to echo chambers in time. Moreover, we observed that the attitudes conveyed by Reddit users who joined risky epistemic enclaves are characterized by a slight inclination toward a more negative or neutral attitude when discussing particularly sensitive issues (e.g., fascism, school shootings, or police violence) while X/Twitter ones often tend to express more positive feelings w.r.t. those involved into less polarized communities
Attributed Stream-Hypernetwork Analysis: Homophilic Behaviors in Pairwise and Group Political Discussions on Reddit
No full textComplex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups. Yet even such higher-order representations ignore the importance of the rich attributes carried by the nodes. In this work we introduce ASH, an Attributed Stream-Hypernetwork framework to model higher-order temporal networks with attributes on nodes. We leverage ASH to study pairwise and group political discussions on the well-known Reddit platform. Our analysis unveils different patterns while looking at either a pairwise or a higher-order structure for the same phenomena. In particular, we find out that Reddit users tend to surround themselves by like-minded peers with respect to their political leaning when online discussions are proxied by pairwise interactions; conversely, such a tendency significantly decreases when considering nodes embedded in higher-order contexts - that often describe heterophilic discussions
Quantifying Attraction to Extreme Opinions in Online Debates
No full textOpinion polarization and political segregation are key societal concerns, especially on social media. Although these phenomena have been traditionally attributed to homophily—preference for like-minded individuals—recent work in social psychology suggests that acrophily—preference for extreme rather than moderate opinions—might play a role as well. In this work, we introduce a methodology to estimate the degree of preference for connecting with users who hold strong opinions on social media. Our framework is composed of four phases: (i) opinion estimation, (ii) opinion thresholding, (iii) network construction, and (iv) acrophily estimation. We apply it to study the climate change debate on Reddit and find that users show higher-than-expected acrophilic patterns, especially if they are climate skeptics or have extreme opinions. Acrophilic patterns are stable over time, while polarization gradually leaves space for pluralism
Beyond Boundaries: Capturing Social Segregation on Hypernetworks
No full textIn recent years, the study of complex social systems has been fueled by the renewed interest in higher-order topologies, thus leading to the emergence of hypernetwork science. A critical and interesting phenomenon often characterizing social complex systems is segregation, i.e., the extent to which network entities are separated or clustered based on certain semantic attributes or features. This paper introduces a novel approach to studying segregation in hypernetworks. Firstly, we propose a general framework to extend classical segregation measures from dyadic to polyadic network structures. Then, we introduce a novel segregation measure called “Random Walk HyperSegregation” (RWHS), which exploits random walkers to estimate segregation at multiple scales. Through an extensive experimental study involving synthetic and real-world case studies, we illustrate the applicability and effectiveness of our measure. Moreover, we highlight the limits of classical segregation measures when extended to high-order topologies—conversely from RWHS, which effectively captured highly-segregated scenarios
FairNet: A Genetic Framework to Reduce Marginalization in Social Networks
No full textDiscrimination in social networks often assumes the form of marginalization against nodes with specific features, e.g., segregation of/against minorities. In this work, we propose a metric that proxies social discrimination based on salient node features in a social network. Under the assumption that in a fair social system, all individuals should be enclosed in similar social circles representing the network in its entirety, our metric assigns a marginalization score to each node in the network, identifying if they are marginalized by similar nodes (e.g., a man marginalized by other men), by different nodes (e.g., a man marginalized by women), or not marginalized at all (i.e., the node has a fair neighborhood). Moreover, we introduce FairNet, a two-fold framework that aims to reduce network marginalization in partially- and fully-attributed networks by employing genetic algorithms. We evaluate our framework on networks emerging from online social interactions and find that the two components of FairNet are able to consistently reduce marginalization
The italian music superdiversity: Geography, emotion and language: one resource to find them, one resource to rule them all
Full text linkGlobalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices
Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences
Full text linkNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors
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