{"id":619,"date":"2025-05-18T22:04:59","date_gmt":"2025-05-18T22:04:59","guid":{"rendered":"https:\/\/conferences.css-fr.org\/?page_id=619"},"modified":"2025-06-22T23:25:25","modified_gmt":"2025-06-22T23:25:25","slug":"parallel-sessions-4","status":"publish","type":"page","link":"https:\/\/conferences.css-fr.org\/?page_id=619","title":{"rendered":"Parallel Sessions 4"},"content":{"rendered":"<div id='full_slider_1'  class='avia-fullwidth-slider main_color avia-shadow   avia-builder-el-0  el_before_av_one_full  avia-builder-el-first   container_wrap fullsize'  ><div  class='avia-slideshow av-mau6s4pk-491be1bf4852e9ab6ab739f7be3c5730 avia-slideshow-featured av_slideshow_full avia-slide-slider av-slideshow-ui av-control-default av-slideshow-manual av-loop-once av-loop-manual-endless av-default-height-applied   avia-slideshow-1' data-slideshow-options=\"{&quot;animation&quot;:&quot;slide&quot;,&quot;autoplay&quot;:false,&quot;loop_autoplay&quot;:&quot;once&quot;,&quot;interval&quot;:5,&quot;loop_manual&quot;:&quot;manual-endless&quot;,&quot;autoplay_stopper&quot;:false,&quot;noNavigation&quot;:false,&quot;bg_slider&quot;:false,&quot;keep_padding&quot;:false,&quot;hoverpause&quot;:false,&quot;show_slide_delay&quot;:0}\"  itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><ul class='avia-slideshow-inner ' style='padding-bottom: 29.675638371291%;'><li  class='avia-slideshow-slide av-mau6s4pk-491be1bf4852e9ab6ab739f7be3c5730__0  av-single-slide slide-1 slide-odd'><div data-rel='slideshow-1' class='avia-slide-wrap '   ><div class='av-slideshow-caption av-mau6s4pk-491be1bf4852e9ab6ab739f7be3c5730__0 caption_fullwidth caption_bottom'><div class=\"container caption_container\"><div class=\"slideshow_caption\"><div class=\"slideshow_inner_caption\"><div class=\"slideshow_align_caption\"><h2 class='avia-caption-title '  itemprop=\"name\" >Parallel Sessions 4<\/h2><div class='avia-caption-content '  itemprop=\"description\" ><p>Wednesday 25th, Sunbelt 2025, Sorbonne University<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-627 avia-img-lazy-loading-not-627\"  src=\"https:\/\/conferences.css-fr.org\/wp-content\/uploads\/2025\/05\/digeing_A_text-mining_contest_with_semantic_maps_flashy_adverst_02b6b727-9b3f-4330-a323-ed01ffb267db-1449x430.png\" width=\"1449\" height=\"430\" title='digeing_A_text-mining_contest_with_semantic_maps_flashy_adverst_02b6b727-9b3f-4330-a323-ed01ffb267db' alt=''  itemprop=\"thumbnailUrl\"   \/><\/div><\/li><\/ul><\/div><\/div><div id='after_full_slider_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-619'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-2yuxf-960dfaa371db54c1624241d0c3570011 av_one_full  avia-builder-el-1  el_after_av_slideshow_full  el_before_av_one_third  avia-builder-el-first  first flex_column_div  '     ><section  class='av_textblock_section av-mau6tq2p-8e9a2cf2e31df95b7d849845c4abc79f '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><p>These sessions will take place at <a href=\"https:\/\/conferences.css-fr.org\/?page_id=418\" target=\"_blank\" rel=\"noopener\">Sorbonne Universit\u00e9<\/a>. Details of the Sunbelt 2025 program are available on its website <a href=\"http:\/\/sunbelt2025.org\" target=\"_blank\" rel=\"noopener\">http:\/\/sunbelt2025.org<\/a><\/p>\n<p>You will find the Sunbelt 2025 interactive program below and on the conference&#8217;s website. You can search for your name or one of the session&#8217;s name to find the exact schedule.<\/p>\n<\/div><\/section><\/div><div  class='flex_column av-j7zqr-93bf4e900def54018667bf60cf3af53d av_one_third  avia-builder-el-3  el_after_av_one_full  el_before_av_one_third  first flex_column_div  column-top-margin'     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mau6ugkn-54a59d5f62288d5a062ea3b0cca39ed2\">\n#top .av-special-heading.av-mau6ugkn-54a59d5f62288d5a062ea3b0cca39ed2{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-mau6ugkn-54a59d5f62288d5a062ea3b0cca39ed2 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-mau6ugkn-54a59d5f62288d5a062ea3b0cca39ed2 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-mau6ugkn-54a59d5f62288d5a062ea3b0cca39ed2 av-special-heading-h3  avia-builder-el-4  el_before_av_toggle_container  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Opinion dynamics : from data to models and back<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<div  class='togglecontainer av-mau6xhhx-381d701c8aaedd6635b120bd3ccf84d0  avia-builder-el-5  el_after_av_heading  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-mau6v73n-54bef86bbaab0f2d457ce5e62b9a0349'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-1' data-fake-id='#toggle-id-1' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-1' data-slide-speed=\"200\" data-title=\"Measuring the Complexity of Interactions in the Language System, Quentin Feltgen\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Measuring the Complexity of Interactions in the Language System, Quentin Feltgen\" data-aria_expanded=\"Click to collapse: Measuring the Complexity of Interactions in the Language System, Quentin Feltgen\">Measuring the Complexity of Interactions in the Language System, Quentin Feltgen<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-1' aria-labelledby='toggle-toggle-id-1' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>What is the structure of language interactions? We already know that language is heavily structured by Zifp\u2019s law (Zipf 1935), be it at the general vocabulary level (Condon 1928), for higher n-grams (Ha et al. 2009), or over the syntactic dependency network (Ferrer i Cancho et al. 2004). This behavior is also found at a more local level: for lexical niches like natural entities or numbers (Piantadosi 2014), for individual semi-schematic constructions (Feltgen 2020), for argument structure constructions (Ellis 2012), etc. The emergence of Zipf\u2019s law at the general level is believed to reflect a sharing of the coding and decoding efforts between speaker and hearer (Ferrer i Cancho &#038; Sol\u00e9 2003), while Zipf\u2019s law at the individual level has been related to learning mechanisms (Goldberg et al. 2004) and to the scale-free structure of semantic networks (Ellis et al. 2014). However, these works do not address the structure of the interaction between two such Zipfian paradigms, like the noun and adjective ones in the epithet construction, even though combination is crucial to generate a meaningful, creative, and diverse language output.<\/p>\n<p>In this contribution, we aim to address general properties of the syntactic interactions between two paradigmatic slots. To do so, we shall focus on the epithet construction in contemporary French (1980-2024), based on data extracted from the Frantext corpus associated with that period (ATILF 1998-2025). To reduce the volume of occurrences and ensure a better homogeneity in the output, we focus on indefinite contexts (e.g. une joie sinc\u00e8re), yielding 171,000 interactions between 12,000 nouns and 9,000 adjectives, for a total of 111,000 different combinations.<\/p>\n<p>To assess the richness of the interaction structure, we may consider computing the mutual information between the two slots. This mutual information varies between 0 (all nouns combine with every adjective in equal measure) and the minimum of the two slots\u2019 entropies (each noun combines with a unique and therefore entirely predictable adjective). None of these extreme values correspond to interesting structures in a communication perspective, which is why, following Santamar\u00eda-Bonfil et al. (2016), we rather consider the complexity coefficient, striking a balance between these extremes, and defined as C = 4I(1-I), where I is the mutual information. Computing this complexity score requires normalizing the mutual information, so that it varies between 0 and 1. In this paper, we define precise bounds for the mutual information to return a min-max transform of it, ensuring that the complexity score is robust with sample size variations.<\/p>\n<p>We find a complexity score of 0.84 for the epithet construction system. Interestingly, if we redraw the links of the system, therefore only keeping the Zipfian structure of each of the two slots, the complexity drops to 0.51, and the number of interactions increases to 156,000. If we add the information of the degrees of each noun and each adjective to match the observed number of combinations, such that the tokens are randomly re-drawn to reinforce existing links based on the respective Zipfian frequencies of the nodes, the complexity increases to 0.62, which is still far from the observed complexity score.<\/p>\n<p>This discrepancy reveals that the interactions are highly structured beyond the random associations of the two slots, even when accounting for their frequency and degree distributions. An examination of the noun-adjective constructs\u2019 paradigm shows that the observed Zipf\u2019s law over these constructs is far steeper than the Zipf\u2019s law for the randomly redrawn system, whichever structural constraints are taken into account. These findings highlight the over-abundance of formulaic elements in language use, and echo with the observation that language production is highly reliant on prefabricated contents (Erman &#038; Warren 2000); more surprisingly, our results evidence that this formulaic character is precisely the property that guarantees the high degree of complexity of language\u2019s syntactic interactions.<\/p>\n<p>References<br \/>\nATILF. (1998-2025). Base textuelle Frantext (Online). ATILF-CNRS &#038; Universit\u00e9 de Lorraine. https:\/\/www.frantext.fr\/<br \/>\nCondon, E. U. (1928). Statistics of vocabulary. Science, 67(1733), 300-300.<br \/>\nEllis, N. C. (2012). Formulaic language and second language acquisition: Zipf and the phrasal teddy bear. Annual review of applied linguistics, 32, 17-44.<br \/>\nEllis, N. C., O\u2019Donnell, M. B., &#038; R\u00f6mer, U. (2014). Does Language Zipf Right Along? In J. Connor-Linton &#038; L. Wander Amoroso (Eds.), Measured language: Quantitative studies of acquisition, assessment, and variation (pp. 33\u201350). Georgetown University Press.<br \/>\nErman, B., &#038; Warren, B. (2000). The idiom principle and the open choice principle. Text &#038; Talk, 20(1), 29-62.<br \/>\nFeltgen, Q. (2020). Diachronic emergence of Zipf-like patterns in construction-specific frequency distributions: A quantitative study of the way too construction. Lexis, 16.<br \/>\nFerrer i Cancho, R., &#038; Sol\u00e9, R. V. (2003). Least effort and the origins of scaling in human language. Proceedings of the National Academy of Sciences, 100(3), 788-791.<br \/>\nFerrer i Cancho, R., Sol\u00e9, R. V., &#038; K\u00f6hler, R. (2004). Patterns in syntactic dependency networks. Physical Review E\u2014Statistical, Nonlinear, and Soft Matter Physics, 69(5), 051915.<br \/>\nGoldberg, A. E., Casenhiser, D. M., &#038; Sethuraman, N. (2004). Learning argument structure generalizations.<br \/>\nHa, L. Q., Hanna, P., Ming, J., &#038; Smith, F. J. (2009). Extending Zipf\u2019s law to n-grams for large corpora. Artificial Intelligence Review, 32, 101-113.<br \/>\nPiantadosi, S. T. (2014). Zipf\u2019s word frequency law in natural language: A critical review and future directions. Psychonomic bulletin &#038; review, 21, 1112-1130.<br \/>\nSantamar\u00eda-Bonfil, G., Fern\u00e1ndez, N., &#038; Gershenson, C. (2016). Measuring the complexity of continuous distributions. Entropy, 18(3), 72.<br \/>\nZipf, G. K. (1935). The Psycho-Biology of Language. Houghton Mifflin Company.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau6vixs-18128696bd669898da4264b2970cc9a9'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-2' data-fake-id='#toggle-id-2' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-2' data-slide-speed=\"200\" data-title=\"Synchronisation entre les partisan\u00b7es des m\u00e9dias et les sympathisant\u00b7es politiques lors d\u2019un processus \u00e9lectoral : vers une \u00e9tude en temps r\u00e9el, R\u00e9mi Perrier, Laura Hern\u00e1ndez, J. Ignacio Alvarez-Hamelin, Mariano G. Beir\u00f3 and Dimitris Kotzinos\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Synchronisation entre les partisan\u00b7es des m\u00e9dias et les sympathisant\u00b7es politiques lors d\u2019un processus \u00e9lectoral : vers une \u00e9tude en temps r\u00e9el, R\u00e9mi Perrier, Laura Hern\u00e1ndez, J. Ignacio Alvarez-Hamelin, Mariano G. Beir\u00f3 and Dimitris Kotzinos\" data-aria_expanded=\"Click to collapse: Synchronisation entre les partisan\u00b7es des m\u00e9dias et les sympathisant\u00b7es politiques lors d\u2019un processus \u00e9lectoral : vers une \u00e9tude en temps r\u00e9el, R\u00e9mi Perrier, Laura Hern\u00e1ndez, J. Ignacio Alvarez-Hamelin, Mariano G. Beir\u00f3 and Dimitris Kotzinos\">Synchronisation entre les partisan\u00b7es des m\u00e9dias et les sympathisant\u00b7es politiques lors d\u2019un processus \u00e9lectoral : vers une \u00e9tude en temps r\u00e9el, R\u00e9mi Perrier, Laura Hern\u00e1ndez, J. Ignacio Alvarez-Hamelin, Mariano G. Beir\u00f3 and Dimitris Kotzinos<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-2' aria-labelledby='toggle-toggle-id-2' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Les r\u00e9seaux sociaux sont devenus un terrain d&#8217;\u00e9tude courant de la diffusion des avis politiques. Les partis politiques et les m\u00e9dias traditionnels utilisent les discussions sur des plateformes sp\u00e9cialis\u00e9es pour \u00e9valuer les tendances de l&#8217;opinion sociale, malgr\u00e9 les pr\u00e9occupations concernant la repr\u00e9sentativit\u00e9 des donn\u00e9es des r\u00e9seaux en ligne. La presse papier, les programmes de radio, ou encore les \u00e9missions de t\u00e9l\u00e9vision discutent souvent de ces conversations en ligne, diffusant ainsi leur contenu au-del\u00e0 des plateformes en ligne.<\/p>\n<p>La plupart des travaux empiriques sur l&#8217;opinion sociale en ligne sont bas\u00e9s sur l&#8217;\u00e9tude des usages d&#8217;un ensemble de mots-cl\u00e9s choisis a priori, en fonction du sujet \u00e9tudi\u00e9. Notre travail, bas\u00e9 sur les publications Twitter (d\u00e9sormais X) collect\u00e9es entre septembre 2021 et juin 2022, suit une approche enti\u00e8rement automatis\u00e9e [1]. Nous construisons d&#8217;abord un r\u00e9seau pond\u00e9r\u00e9 de hashtags avec le lien pond\u00e9r\u00e9 repr\u00e9sentant le nombre d&#8217;utilisateur\u22c5ices uniques ayant utilis\u00e9 deux hashtags dans le m\u00eame tweet. Ensuite, nous d\u00e9terminons les sujets de discussion sur la plateforme par d\u00e9tection de communaut\u00e9s sur ce r\u00e9seau s\u00e9mantique. Cette proc\u00e9dure a \u00e9t\u00e9 appliqu\u00e9e avec succ\u00e8s pour \u00e9tudier les discussions politiques pendant une p\u00e9riode \u00e9lectorale en Argentine [2], ainsi que pour \u00e9tudier la dynamique des interactions entre un m\u00e9dia traditionnel, le journal New York Times, et ses abonn\u00e9s sur Twitter [3], en utilisant un r\u00e9seau s\u00e9mantique statique bas\u00e9 sur les donn\u00e9es collect\u00e9es sur toute la p\u00e9riode \u00e9tudi\u00e9e. En cons\u00e9quence, la d\u00e9termination des sujets discut\u00e9s \u00e0 un moment donn\u00e9 inclut des informations provenant du futur, ce qui n&#8217;est pas pertinent si l&#8217;on souhaite suivre les \u00e9v\u00e9nements en temps r\u00e9el. Dans ce travail, nous pr\u00e9sentons une m\u00e9thode qui permet de le faire. Nous traitons des r\u00e9seaux s\u00e9mantiques \u00e9volutifs, ce qui implique un compromis entre l&#8217;instantan\u00e9it\u00e9 des informations collect\u00e9es et une quantit\u00e9 raisonnable de donn\u00e9es requises pour les rendre robustes. Ici, nous comparons deux proc\u00e9dures diff\u00e9rentes de construction du r\u00e9seau s\u00e9mantique. Dans un cas, nous cumulons les donn\u00e9es du premier mois, puis nous ajoutons les nouvelles donn\u00e9es chaque semaine, ce qui conserve la m\u00e9moire de toutes les discussions sur Twitter depuis le d\u00e9but de la capture. Dans l&#8217;autre, nous partons du m\u00eame r\u00e9seau qu&#8217;auparavant, et nous utilisons une fen\u00eatre glissante d&#8217;une semaine pour int\u00e9grer les nouvelles donn\u00e9es et supprimer les plus anciennes, ce qui entra\u00eene une perte de m\u00e9moire chaque semaine. Dans les deux cas, pour chaque nouveau r\u00e9seau, nous d\u00e9terminons les communaut\u00e9s qui constituent les sujets de discussion de la semaine. Pour un groupe donn\u00e9 (partisan\u22c5es d&#8217;un m\u00e9dia ou sympathisant\u22c5es d&#8217;un\u22c5e candidat\u22c5e), nous cr\u00e9ons un vecteur o\u00f9 chaque composante rel\u00e8ve le niveau de participation dans chaque sujet. La similarit\u00e9 entre deux groupes est alors calcul\u00e9e en comparant leur vecteur dans l&#8217;espace des sujets. Nous montrons qu&#8217;en g\u00e9n\u00e9ral, les deux proc\u00e9dures donnent qualitativement le m\u00eame comportement des courbes de similarit\u00e9 dynamique entre les partisan\u22c5es de diff\u00e9rents candidat\u22c5es. Les rares exceptions concernent des situations atypiques que nous caract\u00e9risons.<\/p>\n<p>De plus, puisque notre approche nous am\u00e8ne \u00e0 mettre \u00e0 jour le paysage s\u00e9mantique, en reconstruisant le graphe de cooccurrence sur une base hebdomadaire, nous sommes en mesure de caract\u00e9riser et de suivre l&#8217;\u00e9volution des sujets eux-m\u00eames. Nous utilisons une proc\u00e9dure dynamique pour suivre les communaut\u00e9s au fil du temps [4], et nous observons comment les sujets de discussion croissent, diminuent (voire disparaissent), se divisent ou fusionnent avec d&#8217;autres. Dans la Fig.1, nous montrons que pour des sujets controvers\u00e9s comme les politiques de vaccination, au fil du temps, les sujets int\u00e8grent de nouveaux hashtags tr\u00e8s utilis\u00e9s qui tendent vers des positions extr\u00e9mistes.<\/p>\n<p>[1] F. M. Cardoso, S. Meloni, A. Santanch\u00e8, and Y. Moreno, \u201cTopical alignment in online social systems\u201d, Frontiers in Physics 7, 58 (2019).<br \/>\n[2] T. Mussi Reyero, M. G. Beir\u00f3, J. I. Alvarez-Hamelin, L. Hern\u00e1ndez, and D. Kotzinos, \u201cEvolution of the political opinion landscape during electoral periods\u201d, EPJ Data Science 10, 31 (2021).<br \/>\n[3] H. Schawe, M.G. Beir\u00f3, J.I. Alvarez-Hamelin et al. \u201cUnderstanding who talks about what: comparison between the information treatment in traditional media and online discussions\u201d. Sci Rep 13, 3809 (2023).<br \/>\n[4] D. Greene, D. Doyle, and P. Cunningham, \u201cTracking the evolution of communities in dynamic social networks\u201d, in 2010 international conference on advances in social networks analysis and mining (Aug. 2010), pp. 176\u2013183.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau6vv15-27285ddb81e99fe737c1b4c577ec7f25'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-3' data-fake-id='#toggle-id-3' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-3' data-slide-speed=\"200\" data-title=\"Theoretical models of opinion dynamics can accurately identify individual political preferences from online interaction data, Antoine Vendeville\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Theoretical models of opinion dynamics can accurately identify individual political preferences from online interaction data, Antoine Vendeville\" data-aria_expanded=\"Click to collapse: Theoretical models of opinion dynamics can accurately identify individual political preferences from online interaction data, Antoine Vendeville\">Theoretical models of opinion dynamics can accurately identify individual political preferences from online interaction data, Antoine Vendeville<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-3' aria-labelledby='toggle-toggle-id-3' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Models of opinion dynamics describe how opinions are shaped in various environments. While these models are able to replicate macroscopical opinion distributions observed in real-world scenarios, their capacity to align with data at the microscopical level remains mostly untested. We evaluate the capacity of the celebrated voter model to capture individual opinions in a fine-grained Twitter dataset collected during the 2017 French Presidential elections. Our findings reveal a strong correspondence between individual opinion distributions in the equilibrium state of the model and ground-truth political leanings of the users. Additionally, we demonstrate that discord probabilities accurately identify pairs of like-minded users. These results emphasize the validity of the voter model in complex settings, and advocate for further empirical evaluations of opinion dynamics models at the microscopical level.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau6wh49-e40864aaf536f09b3e3bfe190ad2f0dc'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-4' data-fake-id='#toggle-id-4' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-4' data-slide-speed=\"200\" data-title=\"Inference of multi-dimensional political positions of online users and web domains: methodology and validation on large-scale French Twitter data, Antoine Vendeville et al.\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Inference of multi-dimensional political positions of online users and web domains: methodology and validation on large-scale French Twitter data, Antoine Vendeville et al.\" data-aria_expanded=\"Click to collapse: Inference of multi-dimensional political positions of online users and web domains: methodology and validation on large-scale French Twitter data, Antoine Vendeville et al.\">Inference of multi-dimensional political positions of online users and web domains: methodology and validation on large-scale French Twitter data, Antoine Vendeville et al.<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-4' aria-labelledby='toggle-toggle-id-4' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>The study of phenomena related to public opinion online and especially political polarization garners significant interest in Computational social sciences. The undertaking of several studies of political phenomena in social media mandates the operationalization of the notion of political stance of users and contents involved. Relevant examples include the study of segregation and polarization online, or the study of political diversity in content diets in social media. While many research designs rely on operationalizations best suited for the US setting, few allow addressing more general design, in which users and content might take stances on multiple ideology and issue dimensions, going beyond traditional Liberal-Conservative or Left-Right scales. To advance the study of more general online ecosystems, we present a methodology for the computation of multidimensional political positions of social media users and web domains. We perform a case study on a large-scale X\/Twitter population of users in the French political Twittersphere and web domains, embedded in a political space spanned by dimensions measuring attitudes towards immigration, the EU, liberal values, elites and institutions, nationalism and the environment. We provide several benchmarks validating the positions of these entities (based on both LLM and human annotations), as well as a discussion of the case studies in which they can be used, including, e.g., AI explainability, political polarization and segregation, and media diets. To encourage reproducibility and further studies on the topic, we publicly release our anonymized data.<\/p>\n<p>Antoine Vendeville, Jimena Royo-Letelier, Duncan Cassells, Jean-Philippe Cointet, Maxime Cr\u00e9pel, Tim Faveron, Th\u00e9ophile Lenoir, B\u00e9atrice Mazoyer, Benjamin Ooghe-Tabanou, Armin Pournaki, Hiroki Yamashita and Pedro Ramaciotti<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau6xgfb-65f721451ea0e27000fdb9c08e2c4592'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-5' data-fake-id='#toggle-id-5' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-5' data-slide-speed=\"200\" data-title=\"Uncovering the structure and dynamics of information flow on the Telegram network, Thomas Louf, Aurora Vindimian and Riccardo Gallotti\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Uncovering the structure and dynamics of information flow on the Telegram network, Thomas Louf, Aurora Vindimian and Riccardo Gallotti\" data-aria_expanded=\"Click to collapse: Uncovering the structure and dynamics of information flow on the Telegram network, Thomas Louf, Aurora Vindimian and Riccardo Gallotti\">Uncovering the structure and dynamics of information flow on the Telegram network, Thomas Louf, Aurora Vindimian and Riccardo Gallotti<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-5' aria-labelledby='toggle-toggle-id-5' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>More than a messaging service, Telegram has emerged as a central online social network in recent years. The network has a particular organisation, as it is split into groups of users, or channels, in which either only a set of administrators can post content (broadcast channels), or any user who joins the group can participate in a discussion discussion channels). It is however not its particular ontology \u2013which in<br \/>\nitself would warrant scientific attention\u2013 that earned it the attention of researchers, but rather as it was pointed out as a haven for the spread of hate speech and disinformation [1, 2]. But while other social media that witness the diffusion of similar problematic content have been extensively studied, little is known about the structure and dynamics of information diffusion on Telegram.<\/p>\n<p>In this work, we aim to provide further understanding on the mechanisms at play behind the growth of the Telegram network. We first do so through an extensive characterisation, using the open Pushshift Telegram dataset [3] as a support for our study. It features 29,000 public channels in which more than 2 million users shared about 300 million messages between September 2015 and October 2019. From this dataset, we built a temporal network with more than 7.5 million edges, an edge from channel i to channel j appearing at any timestamp when j forwarded a message from i. This representation thus encodes the flow of information between channels that happens via message-forwarding.<\/p>\n<p>We first aggregate these directed, temporal edges into static, weighted ones to perform a topological analysis of this aggregate network. We thus find features typical of more traditional social networks: scale-free distributions of the in- and out-strengths, high clustering relatively to randomised versions of the network, and node-feature assortativity, in particular in terms of channel\u2019s language, as also revealed through community detection [4] (see Fig. 1(a-b)). The temporal aspect of this information flow was also characterised. The time between two forwarding events in channels follows a piecewise power law distribution, with two distinct regimes for times inferior or superior to a day, as shown in Fig. 1(c). Remarkably, this distribution holds when considering channels within different activity ranges, and rescaling the times within each activity-group with its average activity. The distribution of burst train sizes E [5] featured in Fig. 1(d) also uncovers the bursty nature of the phenomenon, a trait commonly found in human communication.<\/p>\n<p>This extensive characterisation allowed us to uncover mechanisms which are central to the emergence of the structure and temporality of information flow in this Telegram network. We then propose a model of network growth by exploiting these insights, namely that the forwarding phenomenon is bursty, driven<br \/>\nmy a memory function, and that its structure is driven by focal and triadic closure. We adapt and combine existing topological [6] and temporal [5] models into a complete one which faithfully fits our observations, as we partially show in Fig. 1(c-d).<\/p>\n<p>This work may enable further works striving to understand the spread of information on such networks, and how different external interventions may actually impact this spread.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7j4p4-9d8e246e4a02d277ee970361e0054064'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-6' data-fake-id='#toggle-id-6' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-6' data-slide-speed=\"200\" data-title=\"A model for French voters, Antoine Vendeville\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: A model for French voters, Antoine Vendeville\" data-aria_expanded=\"Click to collapse: A model for French voters, Antoine Vendeville\">A model for French voters, Antoine Vendeville<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-6' aria-labelledby='toggle-toggle-id-6' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Models of opinion dynamics describe how opinions are shaped in various environments. While these models are able to replicate macroscopical opinion distributions observed in real-world scenarios, their capacity to align with data at the microscopical level remains mostly untested. We evaluate the capacity of the celebrated Voter Model to capture individual opinions in online social networks. We leverage a directed, weighted network of retweets between Twitter (now X) users, collected during the campaign of the 2017 French Presidential Elections. We uncover a strong correspondence between individual opinions in the equilibrium state of the model, and ground-truth party affiliations explicitly stated by the users in their publications and self-descriptions. Users are well separated along party lines in the opinion space of the model, and the model correctly identifies ground-truth party affiliations in 92.5% of cases. We also show that discord probabilities allow us to deduce with high accuracy whether or not two users support the same party. Neither the undirected or unweighted counterparts of the retweet network, nor the follow and mention networks produce comparable results. Our findings highlight the necessity for a fine-grained modelling approach, and contribute to the growing literature on the empirical validity of opinion dynamics models.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7jsny-8b3e1e4006139b15f6053bdbc8a80a65'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-7' data-fake-id='#toggle-id-7' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-7' data-slide-speed=\"200\" data-title=\"Modeling the Emergence of Shared-Issue Networks in the Era of Fragmentation Using Digital Log and Survey Data, Choi Sujin \" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Modeling the Emergence of Shared-Issue Networks in the Era of Fragmentation Using Digital Log and Survey Data, Choi Sujin \" data-aria_expanded=\"Click to collapse: Modeling the Emergence of Shared-Issue Networks in the Era of Fragmentation Using Digital Log and Survey Data, Choi Sujin \">Modeling the Emergence of Shared-Issue Networks in the Era of Fragmentation Using Digital Log and Survey Data, Choi Sujin <span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-7' aria-labelledby='toggle-toggle-id-7' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p class=\"paper_abstract\">As signified in the phrase, \u2018no issue, no public,\u2019 attention to shared issues brings strangers together. In today\u2019s increasingly fragmented issue landscape, establishing a common understanding of issues becomes particularly crucial for social cohesion. This study investigates what promotes issue overlap between individuals engaged in personalized news curation.<\/p>\n<p class=\"paper_abstract\">Through stochastic actor-oriented modeling (SAOM) with digital log data and survey data, we investigate underlying mechanisms leading to the formation of shared-issue networks during South Korea\u2019s presidential election. We also compare network dynamics between individuals with low and high involvement in politics, examining how the election\u2019s increased issue salience and meta-narrative catalyzed joint interest differently across involvement levels.<\/p>\n<p class=\"paper_abstract\">Our findings reveal that the likelihood of forming shared-issue relations increases over time, when individuals are less susceptible to political homophily, accumulate greater political knowledge, and practice manual filtering to increase exposure to diverse news genres. Notably, specialized issue interests tend to develop from general interests, rather than vice versa. Individuals became involved in specific issues based on their broader understanding of related contexts, highlighting the significance of cultivating genre-level news interests and ensuring diversified genre exposure in news consumption patterns.<\/p>\n<p class=\"paper_abstract\">This research extends scholarly discourse beyond personalized news consumption to issue sharing mechanisms by shifting the focus from the individual level to the network level\u2014an approach rarely taken in public opinion formation literature. It offers insights into the evolving public discourse landscape shaped by both low-and-high involvement citizens. Our findings also contribute to a deeper understanding of the current information dynamics.<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7kd22-1c5f7a0ec986c2ec6d81f16750027eab'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-8' data-fake-id='#toggle-id-8' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-8' data-slide-speed=\"200\" data-title=\"Coevolutionary Axelrod Model with Weighted Overlap and Features Competition, Chiara Giaquinta\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Coevolutionary Axelrod Model with Weighted Overlap and Features Competition, Chiara Giaquinta\" data-aria_expanded=\"Click to collapse: Coevolutionary Axelrod Model with Weighted Overlap and Features Competition, Chiara Giaquinta\">Coevolutionary Axelrod Model with Weighted Overlap and Features Competition, Chiara Giaquinta<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-8' aria-labelledby='toggle-toggle-id-8' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p class=\"paper_abstract\">As it is well known [1], the influence of media on social opinion does not come from the fact that they succeed in telling people what to think of a given subject but from their success in imposing what people should think about; a situation known as the Agenda Setting Problem. In this way, topics discussed in the public arena are in competition to attract limited people\u2019s attention. In order to model this problem one needs to study two coupled dynamical processes that have comparable time-scales: the evolution of the opinion of the actors, and that of the attention got by the different topics under discussion. Here we propose a multi-dimensional opinion dynamics model inspired by the Axelrod model [2], where each dimension corresponds to a given topic under discussion. Unlike the original model, the contribution of the topics to the overlap that rules social influence among the agents, is neither uniform nor constant. Instead, their relative importance is dynamical, modulated by the attention they attract. The overlap is weighted based on topic popularity, therefore coupled to a process where topics gain or lose attention over time.<\/p>\n<p class=\"paper_abstract\">We tested the model on stylized networks (Barab\u00e1si-Albert and Erd\u0151s-R\u00e9nyi) and also on real-world retweet networks of comparable sizes, for various values of the number of features F (here representing the number of topics under discussion), and the number of traits for each feature q (the number of different options the agents can choose for each topic). Preliminary results reveal that the size of the largest opinion cluster and convergence times heavily depend on the choice of the parameters F and q, with lower q and higher F promoting consensus, aligning with previous findings [3].<\/p>\n<p class=\"paper_abstract\">Competition among topics intensifies with increasing F , making dominance less likely. Moreover, consensus often forms on key features while persistent disagreements on others slow the dynamics. Finally we observe that the network structure significantly impacts the dynamics, leading to distinct outcomes in stylized random and community-structured networks. This work constitutes a new step towards the possibility of comparing theoretical models with empirical studies where the evolution of the attention given to different topics has been measured [4,5].<\/p>\n<p>Chiara; Hernandez, Laura; Chavalarias, David<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7yyi0-ecbc5929b77c756033f42c8468dd9dcd'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-9' data-fake-id='#toggle-id-9' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-9' data-slide-speed=\"200\" data-title=\"When the heterogeneous Hegselmann\u2013Krause model meets community structure, Lucas Andr\u00e9s Sobehart\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: When the heterogeneous Hegselmann\u2013Krause model meets community structure, Lucas Andr\u00e9s Sobehart\" data-aria_expanded=\"Click to collapse: When the heterogeneous Hegselmann\u2013Krause model meets community structure, Lucas Andr\u00e9s Sobehart\">When the heterogeneous Hegselmann\u2013Krause model meets community structure, Lucas Andr\u00e9s Sobehart<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-9' aria-labelledby='toggle-toggle-id-9' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Sobehart, Lucas A.; Hernandez, Laura; Moreno, Yamir Sobehart,<\/p>\n<blockquote>\n<p>Since the first appearance of the Hegselmann-Krause bounded confidence model, many efforts have been made to include properties usually present in real-world systems into it. In particular, the inclusion of heterogeneous agents on systems with a subjacent network structure have shown prominent advances in recent years. In this work we use these studies as a preliminary background to understand the effect of networks with community structure on the steady state of the heterogenous Hegselmann-Krause opinion model. To this extent, we propose a novel benchmark of random networks composed of sparsely interconnected communities that can be used to generate a uniform ensemble of networks with the desired properties to study real-world social systems. Using this ensemble to create networks with high clustering, power-law degree distributions and small world behavior, we show that, when agents are divided into communities, having a large amount of individuals with high confidence bounds will make the opinion on each community converge to a weak consensus. Nevertheless, we also show that given that communities are sparsely connected, each community will have a different mean opinion, preventing the system to reach consensus as a whole. Finally, we observed that the system can also reach a state of polarization where each community becomes polarized between two different opinions.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7zype-72083029e5559ef275fad82bb384ea36'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-10' data-fake-id='#toggle-id-10' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-10' data-slide-speed=\"200\" data-title=\"TIDEM: Measuring Political Distance and Polarization through Retweet Networks in Spanish Regional Elections, Raul Broto\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: TIDEM: Measuring Political Distance and Polarization through Retweet Networks in Spanish Regional Elections, Raul Broto\" data-aria_expanded=\"Click to collapse: TIDEM: Measuring Political Distance and Polarization through Retweet Networks in Spanish Regional Elections, Raul Broto\">TIDEM: Measuring Political Distance and Polarization through Retweet Networks in Spanish Regional Elections, Raul Broto<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-10' aria-labelledby='toggle-toggle-id-10' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>This study introduces TIDEM (Twitter Ideological Distance Estimation Method) a novel methodology for measuring ideological distances and evaluating political polarization using Twitter retweet networks. By using network-based analysis and spatial proximity within ForceAtlas2 layouts, the method captures ideological dynamics and provides a complementary perspective to traditional approaches such as self-placement surveys, the Chapel Hill Expert Survey (CHES), and Manifesto analysis. The methodology is applied to three Spanish regional elections (Catalonia and Madrid 2021, and Andalusia 2022) revealing consistent results through the three cases. A cross-election comparative demonstrates consistency in the relative positioning of left-right ideological blocks and the two major national parties (PP-PSOE). When evaluating TIDEM against traditional methods, the results indicate a strong correlation with self-ideological surveys across all three elections, except for the positioning of Cs in Madrid. However, comparisons with CHES and Manifesto data show mixed outcomes. Additionally, the analysis highlights the importance of regional context in shaping party positions, particularly in multi-dimensional ideological scenarios like Catalonia. Key findings indicate that TIDEM shows higher levels of polarization, likely due to the clustering effects inherent in retweet interactions. While traditional methods tend to position parties more centrally and show reduced distances between ideological blocks, our approach underscores the fluidity of public sentiment and the amplifying effects of online discourse. These results accentuate the potential of social media data as a valuable, scalable, and cost-effective source. TIDEM provides a relevant methodology for studying ideological distances and polarization. While it cannot replace traditional methods, it serves as a powerful complement.<\/p>\n<p>Broto Cervera, Raul; Batlle, Albert; P\u00e9rez-Sol\u00e0, Cristina<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau80ncx-85f62993b51221992de8473b007297d9'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-11' data-fake-id='#toggle-id-11' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-11' data-slide-speed=\"200\" data-title=\"Ideological bias and information cascades on Twitter: evidence from French politicians, Shaden Shabayek\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Ideological bias and information cascades on Twitter: evidence from French politicians, Shaden Shabayek\" data-aria_expanded=\"Click to collapse: Ideological bias and information cascades on Twitter: evidence from French politicians, Shaden Shabayek\">Ideological bias and information cascades on Twitter: evidence from French politicians, Shaden Shabayek<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-11' aria-labelledby='toggle-toggle-id-11' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Shabayek, Shaden; Comola, Margherita Shabayek,<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau81foz-a4f64a03089df1605dad92b9f1dce7bd'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-12' data-fake-id='#toggle-id-12' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-12' data-slide-speed=\"200\" data-title=\"A social media analysis of the political interactions during the French 2022 presidential election, Ixandra Achitouv\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: A social media analysis of the political interactions during the French 2022 presidential election, Ixandra Achitouv\" data-aria_expanded=\"Click to collapse: A social media analysis of the political interactions during the French 2022 presidential election, Ixandra Achitouv\">A social media analysis of the political interactions during the French 2022 presidential election, Ixandra Achitouv<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-12' aria-labelledby='toggle-toggle-id-12' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Ixandra Achitouv and David Chavalarias<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau81vhb-fd1375089a4f95c7197da16a19d99829'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-13' data-fake-id='#toggle-id-13' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-13' data-slide-speed=\"200\" data-title=\"Beyond the Ideological Echo Chambers: Exploring the Dynamics of Diversity, and Demography in Digital Information Ecosystem, Burak Ozturan\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Beyond the Ideological Echo Chambers: Exploring the Dynamics of Diversity, and Demography in Digital Information Ecosystem, Burak Ozturan\" data-aria_expanded=\"Click to collapse: Beyond the Ideological Echo Chambers: Exploring the Dynamics of Diversity, and Demography in Digital Information Ecosystem, Burak Ozturan\">Beyond the Ideological Echo Chambers: Exploring the Dynamics of Diversity, and Demography in Digital Information Ecosystem, Burak Ozturan<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-13' aria-labelledby='toggle-toggle-id-13' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Toggle Content goes here<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau83cwm-33969ea922bcff481636de7a4e2afa15'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-14' data-fake-id='#toggle-id-14' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-14' data-slide-speed=\"200\" data-title=\"Network pragmatic arenas: Analyzing a vaccine controversy on YouTube, Alexandre Dor\u00e9\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Network pragmatic arenas: Analyzing a vaccine controversy on YouTube, Alexandre Dor\u00e9\" data-aria_expanded=\"Click to collapse: Network pragmatic arenas: Analyzing a vaccine controversy on YouTube, Alexandre Dor\u00e9\">Network pragmatic arenas: Analyzing a vaccine controversy on YouTube, Alexandre Dor\u00e9<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-14' aria-labelledby='toggle-toggle-id-14' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div><\/p>\n<div  class='flex_column av-f3l5f-674bb93939059f9159674a28742b3835 av_one_third  avia-builder-el-6  el_after_av_one_third  el_before_av_one_third  flex_column_div  column-top-margin'     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mau72u48-05d901fe0d0e4f6affa07ac4c20ae0c8\">\n#top .av-special-heading.av-mau72u48-05d901fe0d0e4f6affa07ac4c20ae0c8{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-mau72u48-05d901fe0d0e4f6affa07ac4c20ae0c8 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-mau72u48-05d901fe0d0e4f6affa07ac4c20ae0c8 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-mau72u48-05d901fe0d0e4f6affa07ac4c20ae0c8 av-special-heading-h3  avia-builder-el-7  el_before_av_toggle_container  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Beyond detection: disinformation and the amplification of toxic content in the age of social media<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<div  class='togglecontainer av-mau7fd9j-ab7aeffd690fd9b14084781babbc485e  avia-builder-el-8  el_after_av_heading  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-mau73hkk-f6106c335b564489711278912beb6bdd'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-15' data-fake-id='#toggle-id-15' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-15' data-slide-speed=\"200\" data-title=\"A Data-Driven Adaptive Approach to Supporting Fact-Checking and Mitigating Mis\/Disinformation Through Domain Quality Evaluation, Kadkhoda Kaveh\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: A Data-Driven Adaptive Approach to Supporting Fact-Checking and Mitigating Mis\/Disinformation Through Domain Quality Evaluation, Kadkhoda Kaveh\" data-aria_expanded=\"Click to collapse: A Data-Driven Adaptive Approach to Supporting Fact-Checking and Mitigating Mis\/Disinformation Through Domain Quality Evaluation, Kadkhoda Kaveh\">A Data-Driven Adaptive Approach to Supporting Fact-Checking and Mitigating Mis\/Disinformation Through Domain Quality Evaluation, Kadkhoda Kaveh<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-15' aria-labelledby='toggle-toggle-id-15' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Kadkhoda, Kaveh; Bertani, Anna; Louf, Thomas; Gallotti, Riccardo Kadkhoda,<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7auoh-ef18fee085edf6006181350c55015aa7'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-16' data-fake-id='#toggle-id-16' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-16' data-slide-speed=\"200\" data-title=\"The COVID-19 Infodemic on Twitter: Exploring Patterns and Dynamics across Countries, Anna Bertani\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The COVID-19 Infodemic on Twitter: Exploring Patterns and Dynamics across Countries, Anna Bertani\" data-aria_expanded=\"Click to collapse: The COVID-19 Infodemic on Twitter: Exploring Patterns and Dynamics across Countries, Anna Bertani\">The COVID-19 Infodemic on Twitter: Exploring Patterns and Dynamics across Countries, Anna Bertani<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-16' aria-labelledby='toggle-toggle-id-16' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Bertani, Anna; Cortese, Alessandro; Pilati, Federico; Sacco, Pierluigi; Gallotti, Riccardo<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7d0ns-488ea8c01986d1d8c8c20b217d5747bd'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-17' data-fake-id='#toggle-id-17' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-17' data-slide-speed=\"200\" data-title=\"A temporal-network perspective on the longitudinal analysis of online coordinated behaviour, Magnani Matteo \" data-title-open=\"\" data-aria_collapsed=\"Click to expand: A temporal-network perspective on the longitudinal analysis of online coordinated behaviour, Magnani Matteo \" data-aria_expanded=\"Click to collapse: A temporal-network perspective on the longitudinal analysis of online coordinated behaviour, Magnani Matteo \">A temporal-network perspective on the longitudinal analysis of online coordinated behaviour, Magnani Matteo <span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-17' aria-labelledby='toggle-toggle-id-17' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Rossi, Luca; Magnani, Matteo Magnani<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau735yz-768e73af3e668b8a1488a85b82388fb5'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-18' data-fake-id='#toggle-id-18' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-18' data-slide-speed=\"200\" data-title=\"Amplifying Extremism: Network Dynamics of Conspiratorial and Toxic Content in the Canadian Freedom Convoy Movement, Abul-Fottouh, Abul-Fottouh Deena\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Amplifying Extremism: Network Dynamics of Conspiratorial and Toxic Content in the Canadian Freedom Convoy Movement, Abul-Fottouh, Abul-Fottouh Deena\" data-aria_expanded=\"Click to collapse: Amplifying Extremism: Network Dynamics of Conspiratorial and Toxic Content in the Canadian Freedom Convoy Movement, Abul-Fottouh, Abul-Fottouh Deena\">Amplifying Extremism: Network Dynamics of Conspiratorial and Toxic Content in the Canadian Freedom Convoy Movement, Abul-Fottouh, Abul-Fottouh Deena<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-18' aria-labelledby='toggle-toggle-id-18' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Abul-Fottouh, Deena; Eckardt, Jan; McLay, Rachel; Turgeon, Mathieu Abul-Fottouh<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7a66n-feea4264d15c763d9e3d49bcbd6332ef'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-19' data-fake-id='#toggle-id-19' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-19' data-slide-speed=\"200\" data-title=\"The Rise of the Right in the UK from Brexit to Tommy Robinson, Andrew Mackie\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The Rise of the Right in the UK from Brexit to Tommy Robinson, Andrew Mackie\" data-aria_expanded=\"Click to collapse: The Rise of the Right in the UK from Brexit to Tommy Robinson, Andrew Mackie\">The Rise of the Right in the UK from Brexit to Tommy Robinson, Andrew Mackie<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-19' aria-labelledby='toggle-toggle-id-19' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau79p1q-661f0d026cf3c99ddd70f5e2cd0b0285'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-20' data-fake-id='#toggle-id-20' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-20' data-slide-speed=\"200\" data-title=\"Cognitive Warfare on Social Networks, Andr\u00e9 Carvalho\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Cognitive Warfare on Social Networks, Andr\u00e9 Carvalho\" data-aria_expanded=\"Click to collapse: Cognitive Warfare on Social Networks, Andr\u00e9 Carvalho\">Cognitive Warfare on Social Networks, Andr\u00e9 Carvalho<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-20' aria-labelledby='toggle-toggle-id-20' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Carvalho, Andr\u00e9; Mourad, Aim\u00e3n; Conejero, Maria Carvalho<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau73vba-a68da6d0e8399045f2ffcbb9441333b8'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-21' data-fake-id='#toggle-id-21' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-21' data-slide-speed=\"200\" data-title=\"Streamwork Makes the Dream Work! Cross-Platform Collaboration and Community-Building Among Far-Right and Conspiracy-Ideologist Actors on Telegram and YouTube,  Harald Sick\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Streamwork Makes the Dream Work! Cross-Platform Collaboration and Community-Building Among Far-Right and Conspiracy-Ideologist Actors on Telegram and YouTube,  Harald Sick\" data-aria_expanded=\"Click to collapse: Streamwork Makes the Dream Work! Cross-Platform Collaboration and Community-Building Among Far-Right and Conspiracy-Ideologist Actors on Telegram and YouTube,  Harald Sick\">Streamwork Makes the Dream Work! Cross-Platform Collaboration and Community-Building Among Far-Right and Conspiracy-Ideologist Actors on Telegram and YouTube,  Harald Sick<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-21' aria-labelledby='toggle-toggle-id-21' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Sick, Harald; Jost, Pablo; Schmidt, Michael; Donner, Christian<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau750qg-54da454c6b4d7ee7c58cdc9cfe6628f5'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-22' data-fake-id='#toggle-id-22' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-22' data-slide-speed=\"200\" data-title=\"The resilience of conspiracy theory networks on social media: from COVID-19 to the Russian invasion of Ukraine, Gronow, Antti; Malkam\u00e4ki, Arttu; Mullo, Pamela\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The resilience of conspiracy theory networks on social media: from COVID-19 to the Russian invasion of Ukraine, Gronow, Antti; Malkam\u00e4ki, Arttu; Mullo, Pamela\" data-aria_expanded=\"Click to collapse: The resilience of conspiracy theory networks on social media: from COVID-19 to the Russian invasion of Ukraine, Gronow, Antti; Malkam\u00e4ki, Arttu; Mullo, Pamela\">The resilience of conspiracy theory networks on social media: from COVID-19 to the Russian invasion of Ukraine, Gronow, Antti; Malkam\u00e4ki, Arttu; Mullo, Pamela<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-22' aria-labelledby='toggle-toggle-id-22' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau74hl5-58970d6c89e1a15f5d1089d688cb4c94'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-23' data-fake-id='#toggle-id-23' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-23' data-slide-speed=\"200\" data-title=\"Studying information segregation on YouTube: Structural differences in the recommendation graph, Marijn A.; Erhard, Lukas; Kharazian, Zarine; Lamba, Manika\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Studying information segregation on YouTube: Structural differences in the recommendation graph, Marijn A.; Erhard, Lukas; Kharazian, Zarine; Lamba, Manika\" data-aria_expanded=\"Click to collapse: Studying information segregation on YouTube: Structural differences in the recommendation graph, Marijn A.; Erhard, Lukas; Kharazian, Zarine; Lamba, Manika\">Studying information segregation on YouTube: Structural differences in the recommendation graph, Marijn A.; Erhard, Lukas; Kharazian, Zarine; Lamba, Manika<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-23' aria-labelledby='toggle-toggle-id-23' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7c8gn-a57961489bdf62292f5f4053f81f4338'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-24' data-fake-id='#toggle-id-24' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-24' data-slide-speed=\"200\" data-title=\"The role of moral values in the social media debate, Pietro Gravino (Sony CSL)\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The role of moral values in the social media debate, Pietro Gravino (Sony CSL)\" data-aria_expanded=\"Click to collapse: The role of moral values in the social media debate, Pietro Gravino (Sony CSL)\">The role of moral values in the social media debate, Pietro Gravino (Sony CSL)<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-24' aria-labelledby='toggle-toggle-id-24' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Brugnoli, Emanuele; Gravino, Pietro; Lo Sardo, D. Ruggiero; Loreto, Vittorio<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7fct3-e30bc17dcf7dde012a4b1a8877cf5ae0'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-25' data-fake-id='#toggle-id-25' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-25' data-slide-speed=\"200\" data-title=\"\tThe Diffusion of Propaganda on Social Media: Analyzing Russian and Chinese Influence on X (Twitter) during Xi Jinping\u2019s visit to Moscow in 2023, Luliia Alieva\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: \tThe Diffusion of Propaganda on Social Media: Analyzing Russian and Chinese Influence on X (Twitter) during Xi Jinping\u2019s visit to Moscow in 2023, Luliia Alieva\" data-aria_expanded=\"Click to collapse: \tThe Diffusion of Propaganda on Social Media: Analyzing Russian and Chinese Influence on X (Twitter) during Xi Jinping\u2019s visit to Moscow in 2023, Luliia Alieva\">\tThe Diffusion of Propaganda on Social Media: Analyzing Russian and Chinese Influence on X (Twitter) during Xi Jinping\u2019s visit to Moscow in 2023, Luliia Alieva<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-25' aria-labelledby='toggle-toggle-id-25' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7dw0x-7022da63811afdf76da9f41ce766da3c'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-26' data-fake-id='#toggle-id-26' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-26' data-slide-speed=\"200\" data-title=\"How algorithms recommend political content on social network, Tim Faverjon\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: How algorithms recommend political content on social network, Tim Faverjon\" data-aria_expanded=\"Click to collapse: How algorithms recommend political content on social network, Tim Faverjon\">How algorithms recommend political content on social network, Tim Faverjon<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-26' aria-labelledby='toggle-toggle-id-26' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Faverjon, Tim; Ramaciotti, Pedro<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau7eobf-d39da2e798d86f91aa8829bd63ae6578'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-27' data-fake-id='#toggle-id-27' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-27' data-slide-speed=\"200\" data-title=\"Unveiling emerging moderation dynamics in Mastodon\u2019s federated instance network, Beatriz Arregui Garcia\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Unveiling emerging moderation dynamics in Mastodon\u2019s federated instance network, Beatriz Arregui Garcia\" data-aria_expanded=\"Click to collapse: Unveiling emerging moderation dynamics in Mastodon\u2019s federated instance network, Beatriz Arregui Garcia\">Unveiling emerging moderation dynamics in Mastodon\u2019s federated instance network, Beatriz Arregui Garcia<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-27' aria-labelledby='toggle-toggle-id-27' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Arregui Garcia, Beatriz; La Cava, Lucio; Baqir, Anees; Gallotti, Riccardo; Meloni, Sandro<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mb6eassv-b59a6309db3c55bdf10d610e897c76b3'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-28' data-fake-id='#toggle-id-28' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-28' data-slide-speed=\"200\" data-title=\"Sampled datasets risk substantial bias in the identification of political polarization on social media, Gabriele Di Bona et al.\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Sampled datasets risk substantial bias in the identification of political polarization on social media, Gabriele Di Bona et al.\" data-aria_expanded=\"Click to collapse: Sampled datasets risk substantial bias in the identification of political polarization on social media, Gabriele Di Bona et al.\">Sampled datasets risk substantial bias in the identification of political polarization on social media, Gabriele Di Bona et al.<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-28' aria-labelledby='toggle-toggle-id-28' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Gabriele Di Bona, Emma Fraxanet, Bj\u00f6rn Komander, Andrea Lo Sasso, Virginia Morini, Antoine Vendeville, Max Falkenberg and Alessandro Galeazzi<\/p>\n<blockquote>\n<p>The study of phenomena related to public opinion online and especially political polarization garners significant interest in Computational social sciences. The undertaking of several studies of political phenomena in social media mandates the operationalization of the notion of political stance of users and contents involved. Relevant examples include the study of segregation and polarization online, or the study of political diversity in content diets in social media. While many research designs rely on operationalizations best suited for the US setting, few allow addressing more general design, in which users and content might take stances on multiple ideology and issue dimensions, going beyond traditional Liberal-Conservative or Left-Right scales. To advance the study of more general online ecosystems, we present a methodology for the computation of multidimensional political positions of social media users and web domains. We perform a case study on a large-scale X\/Twitter population of users in the French political Twittersphere and web domains, embedded in a political space spanned by dimensions measuring attitudes towards immigration, the EU, liberal values, elites and institutions, nationalism and the environment. We provide several benchmarks validating the positions of these entities (based on both LLM and human annotations), as well as a discussion of the case studies in which they can be used, including, e.g., AI explainability, political polarization and segregation, and media diets. To encourage reproducibility and further studies on the topic, we publicly release our anonymized data.<\/p>\n<\/blockquote>\n<\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div>\n<div  class='flex_column av-8hcrf-517279ccdb9eb9605bb6eb9333a79ea5 av_one_third  avia-builder-el-9  el_after_av_one_third  el_before_av_one_full  flex_column_div  column-top-margin'     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mau6ybx1-767ca5d5a93caed70dba34a2f9f1bfe0\">\n#top .av-special-heading.av-mau6ybx1-767ca5d5a93caed70dba34a2f9f1bfe0{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-mau6ybx1-767ca5d5a93caed70dba34a2f9f1bfe0 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-mau6ybx1-767ca5d5a93caed70dba34a2f9f1bfe0 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-mau6ybx1-767ca5d5a93caed70dba34a2f9f1bfe0 av-special-heading-h3  avia-builder-el-10  el_before_av_toggle_container  avia-builder-el-first '><h3 class='av-special-heading-tag '  itemprop=\"headline\"  >Science dynamics : from reconstruction to social processes<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<div  class='togglecontainer av-mau71myh-3006b9bd2700749600125a2560615826  avia-builder-el-11  el_after_av_heading  avia-builder-el-last  toggle_close_all' >\n<section class='av_toggle_section av-mau6zecw-5f859e01270c8fa930e48da2055dafa6'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-29' data-fake-id='#toggle-id-29' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-29' data-slide-speed=\"200\" data-title=\"The robust-fragile duality of the ATLAS collaboration network, Rodr\u00edguez-Casa\u00f1, Rub\u00e9n et al.\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The robust-fragile duality of the ATLAS collaboration network, Rodr\u00edguez-Casa\u00f1, Rub\u00e9n et al.\" data-aria_expanded=\"Click to collapse: The robust-fragile duality of the ATLAS collaboration network, Rodr\u00edguez-Casa\u00f1, Rub\u00e9n et al.\">The robust-fragile duality of the ATLAS collaboration network, Rodr\u00edguez-Casa\u00f1, Rub\u00e9n et al.<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-29' aria-labelledby='toggle-toggle-id-29' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><p>Rodr\u00edguez-Casa\u00f1, Rub\u00e9n; Palazzi, Mar\u00eda; Sol\u00e9-Ribalta, Albert; Canals, Agust\u00ed; Borge-Holthoefer, Javier Borge-Holthoefer, Dr. Javier<\/p>\n<\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau6zsh8-5416a644bb4e758bd8c98637489f26d2'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-30' data-fake-id='#toggle-id-30' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-30' data-slide-speed=\"200\" data-title=\"Do states make scientific fields? McMahan, Peter; L\u00e9vesque, Gabriel L\u00e9vesque, Gabriel\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Do states make scientific fields? McMahan, Peter; L\u00e9vesque, Gabriel L\u00e9vesque, Gabriel\" data-aria_expanded=\"Click to collapse: Do states make scientific fields? McMahan, Peter; L\u00e9vesque, Gabriel L\u00e9vesque, Gabriel\">Do states make scientific fields? McMahan, Peter; L\u00e9vesque, Gabriel L\u00e9vesque, Gabriel<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-30' aria-labelledby='toggle-toggle-id-30' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau707sh-4baf12dec3e7967c84c5c9faa5c0e291'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-31' data-fake-id='#toggle-id-31' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-31' data-slide-speed=\"200\" data-title=\"Gender differences in scientific recognition: authorship and acknowledgment, Yukie; Kusumegi, Keigo; Acuna, Daniel E. Sano, Dr. Yukie\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Gender differences in scientific recognition: authorship and acknowledgment, Yukie; Kusumegi, Keigo; Acuna, Daniel E. Sano, Dr. Yukie\" data-aria_expanded=\"Click to collapse: Gender differences in scientific recognition: authorship and acknowledgment, Yukie; Kusumegi, Keigo; Acuna, Daniel E. Sano, Dr. Yukie\">Gender differences in scientific recognition: authorship and acknowledgment, Yukie; Kusumegi, Keigo; Acuna, Daniel E. Sano, Dr. Yukie<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-31' aria-labelledby='toggle-toggle-id-31' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau70omy-4a60498f3467fddd86b813399e3c8bee'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-32' data-fake-id='#toggle-id-32' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-32' data-slide-speed=\"200\" data-title=\"The stagnation of a science, Gillespie, Ryder Gillespie, Ryder\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: The stagnation of a science, Gillespie, Ryder Gillespie, Ryder\" data-aria_expanded=\"Click to collapse: The stagnation of a science, Gillespie, Ryder Gillespie, Ryder\">The stagnation of a science, Gillespie, Ryder Gillespie, Ryder<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-32' aria-labelledby='toggle-toggle-id-32' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<section class='av_toggle_section av-mau71it0-6dd90b3455be77678a595c7f7a6af995'  itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div role=\"tablist\" class=\"single_toggle\" data-tags=\"{All} \"  ><p id='toggle-toggle-id-33' data-fake-id='#toggle-id-33' class='toggler  av-title-above '  itemprop=\"headline\"  role='tab' tabindex='0' aria-controls='toggle-id-33' data-slide-speed=\"200\" data-title=\"Project ARCH: Optimizing the Design of Virtual Scientific Ecosystems for Team Formation and Innovation, Zajdela, Emma Rosa; Mojeed, Sodiq Abiodun; Kim, Joan Zajdela\" data-title-open=\"\" data-aria_collapsed=\"Click to expand: Project ARCH: Optimizing the Design of Virtual Scientific Ecosystems for Team Formation and Innovation, Zajdela, Emma Rosa; Mojeed, Sodiq Abiodun; Kim, Joan Zajdela\" data-aria_expanded=\"Click to collapse: Project ARCH: Optimizing the Design of Virtual Scientific Ecosystems for Team Formation and Innovation, Zajdela, Emma Rosa; Mojeed, Sodiq Abiodun; Kim, Joan Zajdela\">Project ARCH: Optimizing the Design of Virtual Scientific Ecosystems for Team Formation and Innovation, Zajdela, Emma Rosa; Mojeed, Sodiq Abiodun; Kim, Joan Zajdela<span class=\"toggle_icon\"><span class=\"vert_icon\"><\/span><span class=\"hor_icon\"><\/span><\/span><\/p><div id='toggle-id-33' aria-labelledby='toggle-toggle-id-33' role='region' class='toggle_wrap  av-title-above'  ><div class='toggle_content invers-color '  itemprop=\"text\" ><\/div><\/div><\/div><\/section>\n<\/div><\/p><\/div>\n<div  class='flex_column av-9qatf-ad4fc9c5dca904388dbbc5d09af601a6 av_one_full  avia-builder-el-12  el_after_av_one_third  avia-builder-el-last  first flex_column_div  column-top-margin'     ><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-619","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/619","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=619"}],"version-history":[{"count":10,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/619\/revisions"}],"predecessor-version":[{"id":796,"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=\/wp\/v2\/pages\/619\/revisions\/796"}],"wp:attachment":[{"href":"https:\/\/conferences.css-fr.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=619"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}