We have two new publications out!

1. Detecting bias in algorithms used to disseminate information in social networks and mitigating it using multiobjective optimization, by Vedran Sekara, Ivan Dotu, Manuel Cebrian, Esteban Moro, Manuel Garcia−Herranz published in PNAS Nexus
   
   Based on extensive computer simulations on synthetic and 10 diverse real-world social networks we show that seeding information in social networks using state-of-the-art influence maximization methods creates information gaps. Our results show that these algorithms select influencers who do not disseminate information equitably, threatening to create an increasingly unequal society. To overcome this issue, we devise a multiobjective algorithm which both maximizes influence and information equity. Our results demonstrate it is possible to reduce vulnerability at a relatively low trade-off with respect to spread. This highlights that in our search for maximizing the spread of information we do not need to compromise on information equality.
2. Adaptive continuity-preserving simplification of street networks, by Martin Fleischmann, Anastassia Vybornova, James D. Gaboardi, Anna Brázdová, Daniela Dančejová, published in Computers, Environment and Urban Systems
   
   Street network data is widely used to study human-based activities and urban structure. Often, these data are geared towards transportation applications, which require highly granular, directed graphs that capture the complex relationships of potential traffic patterns. While this level of network detail is critical for certain fine-grained mobility models, it represents a hindrance for studies concerned with the morphology of the street network. For the latter case, street network simplification — the process of converting a highly granular input network into its most simple morphological form — is a necessary, but highly tedious preprocessing step, especially when conducted manually. In this manuscript, we develop and present a novel adaptive algorithm for simplifying street networks that is both fully automated and able to mimic results obtained through a manual simplification routine. The algorithm — available in the neatnet Python package — outperforms current state-of-the-art procedures when comparing those methods to manually, human-simplified data, while preserving network continuity.
   See also the neatnet package: https://github.com/uscuni/neatnet

We have three new publications out, as always on a variety of topics!

1. Estimating affective polarization on a social network, by Marilena Hohmann and Michele Coscia, published in PLOS ONE.
   
   Concerns about polarization and hate speech on social media are widespread. Affective polarization, i.e., hostility among partisans, is crucial in this regard as it links political disagreements to hostile language online. However, only a few methods are available to measure how affectively polarized an online debate is, and the existing approaches do not investigate jointly two defining features of affective polarization: hostility and social distance. To address this methodological gap, we propose a network-based measure of affective polarization that combines both aspects – which allows them to be studied independently. We show that our measure accurately captures the relation between the level of disagreement and the hostility expressed towards others (affective component) and whom individuals choose to interact with or avoid (social distance component). Applying our measure to a large-scale Twitter data set on COVID-19, we find that affective polarization was low in February 2020 and increased to high levels as more users joined the Twitter discussion in the following months.
   See also Michele’s blog post: https://www.michelecoscia.com/?p=2466
2. Leveraging VLLMs for Visual Clustering: Image-to-Text Mapping Shows Increased Semantic Capabilities and Interpretability, by Luigi Arminio, Matteo Magnani, Matías Piqueras, Luca Rossi, and Alexandra Segerberg published in Social Science Computer Review.
   
   We test an approach that leverages the ability of Vision-and-Large-Language-Models (VLLMs) to generate image descriptions that incorporate connotative interpretations of the input images. In particular, we use a VLLM to generate connotative textual descriptions of a set of images related to climate debate, and cluster the images based on these textual descriptions. In parallel, we cluster the same images using a more traditional approach based on CNNs. In doing so, we compare the connotative semantic validity of clusters generated using VLLMs with those produced using CNNs, and assess their interpretability. The results show that the approach based on VLLMs greatly improves the quality score for connotative clustering. Moreover, VLLM-based approaches, leveraging textual information as a step towards clustering, offer a high level of interpretability of the results.
3. Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring, by Mesut Kaya and Toine Bogers, published in RecSys ’25: Proceedings of the Nineteenth ACM Conference on Recommender Systems
   
   Past analyses of fairness in algorithmic hiring have been restricted to single-side fairness, ignoring the perspectives of the other stakeholders. In this paper, we address this gap and present a multi-stakeholder approach to fairness in a candidate recommender system that recommends relevant candidate CVs to human recruiters in a human-in-the-loop algorithmic hiring scenario. We conducted semi-structured interviews with 40 different stakeholders (job seekers, companies, recruiters, and other job portal employees). We used these interviews to explore their lived experiences of unfairness in hiring, co-design definitions of fairness as well as metrics that might capture these experiences. Finally, we attempt to reconcile and map these different (and sometimes conflicting) perspectives and definitions to existing (categories of) fairness metrics that are relevant for our candidate recommendation scenario.