{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T05:34:44Z","timestamp":1768109684414,"version":"3.49.0"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"13","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p>\n            Detecting niches of polarization in social media is a first step towards deploying mitigation strategies and avoiding radicalization. In this paper, we model polarization niches as close-knit dense communities of users, which are under the influence of some well-known sources of misinformation, and isolated from authoritative information sources. Based on this intuition we define the problem of finding a subgraph that maximizes a combination of (\n            <jats:italic>i<\/jats:italic>\n            ) density, (\n            <jats:italic>ii<\/jats:italic>\n            ) proximity to a small set of nodes\n            <jats:italic>A<\/jats:italic>\n            (named\n            <jats:italic>Attractors<\/jats:italic>\n            ), and (\n            <jats:italic>iii<\/jats:italic>\n            ) distance from another small set of nodes\n            <jats:italic>R<\/jats:italic>\n            (named\n            <jats:italic>Repulsers<\/jats:italic>\n            ).\n          <\/jats:p>\n          <jats:p>\n            Deviating from the bulk of the literature on detecting polarization, we do not exploit text mining or sentiment analysis, nor we track the propagation of information: we only exploit the network structure and the background knowledge about the sets\n            <jats:italic>A<\/jats:italic>\n            and\n            <jats:italic>R<\/jats:italic>\n            , which are given as input. We build on recent algorithmic advances in supermodular maximization to provide an iterative greedy algorithm, dubbed\n            <jats:italic>Down in the Hollow<\/jats:italic>\n            (dith), that converges fast to a near-optimal solution. Thanks to a novel theoretical upper bound, we are able to equip dith with a practical device that allows to terminate as soon as a solution with a user-specified approximation factor is found, making our algorithm very efficient in practice. Our experiments on very large networks confirm that our algorithm always returns a solution with an approximation factor better or equal to the one specified by the user, and it is scalable. Our case-studies in polarized settings, confirm the usefulness of our algorithmic primitive in detecting polarization niches.\n          <\/jats:p>","DOI":"10.14778\/3565838.3565843","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T23:09:56Z","timestamp":1674256196000},"page":"3883-3896","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Discovering Polarization Niches via Dense Subgraphs with Attractors and Repulsers"],"prefix":"10.14778","volume":"15","author":[{"given":"Adriano","family":"Fazzone","sequence":"first","affiliation":[{"name":"CENTAI Institute, Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tommaso","family":"Lanciano","sequence":"additional","affiliation":[{"name":"Sapienza University, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"Denni","sequence":"additional","affiliation":[{"name":"Sapienza University, Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charalampos E.","family":"Tsourakakis","sequence":"additional","affiliation":[{"name":"Boston University and ISI Foundation, Turin, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco","family":"Bonchi","sequence":"additional","affiliation":[{"name":"CENTAI Institute, Turin, Italy and Eurecat, Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Jon Kleinberg, Zhibin Liang, David Parkes, Mauro Sozio, and Charalampos E. 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