871 research outputs found

    Bridgeness: A Local Index on Edge Significance in Maintaining Global Connectivity

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    Edges in a network can be divided into two kinds according to their different roles: some enhance the locality like the ones inside a cluster while others contribute to the global connectivity like the ones connecting two clusters. A recent study by Onnela et al uncovered the weak ties effects in mobile communication. In this article, we provide complementary results on document networks, that is, the edges connecting less similar nodes in content are more significant in maintaining the global connectivity. We propose an index named bridgeness to quantify the edge significance in maintaining connectivity, which only depends on local information of network topology. We compare the bridgeness with content similarity and some other structural indices according to an edge percolation process. Experimental results on document networks show that the bridgeness outperforms content similarity in characterizing the edge significance. Furthermore, extensive numerical results on disparate networks indicate that the bridgeness is also better than some well-known indices on edge significance, including the Jaccard coefficient, degree product and betweenness centrality.Comment: 10 pages, 4 figures, 1 tabl

    Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections

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    "This is an Accepted Manuscript of an article published by Taylor & Francis in The Communication Review on 2019, available online: https://www.tandfonline.com/doi/full/10.1080/10714421.2019.1599642"[EN] During election campaigns, candidates, parties, and media share their relevance on Twitter with a group of especially active users, aligned with a particular party. This paper introduces the profile of ¿party evangelists,¿ and explores the activity and effects these users had on the general political conversation during the 2015 Spanish general election. On that occasion, the electoral expectations were uncertain for the two major parties (PP and PSOE) because of the rise of two emerging parties that were disrupting the political status quo (Podemos and Ciudadanos). This was an ideal situation to assess the differences between the evangelists of established and emerging parties. The paper evaluates two aspects of the political conversation based on a corpus of 8.9 million tweets: the retweet- ing effectiveness, and the sentiment analysis of the overall conver- sation. We found that one of the emerging party¿s evangelists dominated message dissemination to a much greater extent.The present research was supported by the Ministerio de Economia y Competitividad [CSO2013-43960-R] [CSO2016-77331-C2-1-R]. The present research was supported by the Ministerio de Economia y Competitividad, Spain, under Grants CSO2013-43960-R ("2015-2016 Spanish political parties' online campaign strategies") and CSO2016-77331-C2-1-R ("Strategies, agendas and discourse in electoral cybercampaigns: media and citizens"). This work was possible thanks to help received from Emilio Giner in his task of extracting the corpus of tweets and from assistance provided by Mike Thelwall and David Vilares in the use of the SentiStrength application. We have benefited from valuable comments on drafts of this article from professors Joaquín Aldás, Amparo Baviera-Puig, Guillermo López-García, and especially Lidia Valera-Ordaz.Baviera, T.; Sampietro, A.; García-Ull, FJ. (2019). Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections. The Communication Review. 22(2):117-138. https://doi.org/10.1080/10714421.2019.1599642S117138222Alvarez, R., Garcia, D., Moreno, Y., & Schweitzer, F. (2015). Sentiment cascades in the 15M movement. EPJ Data Science, 4(1). doi:10.1140/epjds/s13688-015-0042-4Anduiza, E., Cristancho, C., & Sabucedo, J. M. (2013). Mobilization through online social networks: the political protest of theindignadosin Spain. Information, Communication & Society, 17(6), 750-764. doi:10.1080/1369118x.2013.808360Anstead, N., & O’Loughlin, B. (2011). The Emerging Viewertariat and BBC Question Time. The International Journal of Press/Politics, 16(4), 440-462. doi:10.1177/1940161211415519Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Barberá, P. (2015). Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis, 23(1), 76-91. doi:10.1093/pan/mpu011Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting From Left to Right. Psychological Science, 26(10), 1531-1542. doi:10.1177/0956797615594620Barberá, P., & Rivero, G. (2014). Understanding the Political Representativeness of Twitter Users. Social Science Computer Review, 33(6), 712-729. doi:10.1177/0894439314558836Berger, J., & Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192-205. doi:10.1509/jmr.10.0353Bigonha, C., Cardoso, T. N. C., Moro, M. M., Gonçalves, M. A., & Almeida, V. A. F. (2011). Sentiment-based influence detection on Twitter. Journal of the Brazilian Computer Society, 18(3), 169-183. doi:10.1007/s13173-011-0051-5Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008Bravo-Marquez, F., Mendoza, M., & Poblete, B. (2014). Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69, 86-99. doi:10.1016/j.knosys.2014.05.016Casero-Ripollés, A., Feenstra, R. A., & Tormey, S. (2016). Old and New Media Logics in an Electoral Campaign. The International Journal of Press/Politics, 21(3), 378-397. doi:10.1177/1940161216645340Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2013). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340-358. doi:10.1177/1461444813480466Meeyoung Cha, Benevenuto, F., Haddadi, H., & Gummadi, K. (2012). The World of Connections and Information Flow in Twitter. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(4), 991-998. doi:10.1109/tsmca.2012.2183359Chadwick, A. (2013). The Hybrid Media System. doi:10.1093/acprof:oso/9780199759477.001.0001Cogburn, D. L., & Espinoza-Vasquez, F. K. (2011). From Networked Nominee to Networked Nation: Examining the Impact of Web 2.0 and Social Media on Political Participation and Civic Engagement in the 2008 Obama Campaign. Journal of Political Marketing, 10(1-2), 189-213. doi:10.1080/15377857.2011.540224(2014). Journal of Communication, 64(2). doi:10.1111/jcom.2014.64.issue-2Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012). Partisan asymmetries in online political activity. EPJ Data Science, 1(1). doi:10.1140/epjds6Coviello, L., Sohn, Y., Kramer, A. D. I., Marlow, C., Franceschetti, M., Christakis, N. A., & Fowler, J. H. (2014). Detecting Emotional Contagion in Massive Social Networks. PLoS ONE, 9(3), e90315. doi:10.1371/journal.pone.0090315D’heer, E., & Verdegem, P. (2014). Conversations about the elections on Twitter: Towards a structural understanding of Twitter’s relation with the political and the media field. European Journal of Communication, 29(6), 720-734. doi:10.1177/0267323114544866Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). AN INVESTIGATION OF INFLUENTIALS AND THE ROLE OF SENTIMENT IN POLITICAL COMMUNICATION ON TWITTER DURING ELECTION PERIODS. Information, Communication & Society, 16(5), 795-825. doi:10.1080/1369118x.2013.783608Díaz-Parra, I., & Jover-Báez, J. (2016). Social movements in crisis? From the 15-M movement to the electoral shift in Spain. International Journal of Sociology and Social Policy, 36(9/10), 680-694. doi:10.1108/ijssp-09-2015-0101Dubois, E., & Gaffney, D. (2014). The Multiple Facets of Influence. American Behavioral Scientist, 58(10), 1260-1277. doi:10.1177/0002764214527088Enli, G. (2017). Twitter as arena for the authentic outsider: exploring the social media campaigns of Trump and Clinton in the 2016 US presidential election. European Journal of Communication, 32(1), 50-61. doi:10.1177/0267323116682802Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 205395171664582. doi:10.1177/2053951716645828Ferrara, E., & Yang, Z. (2015). Measuring Emotional Contagion in Social Media. PLOS ONE, 10(11), e0142390. doi:10.1371/journal.pone.0142390(2015). Journal of Communication, 65(5). doi:10.1111/jcom.2015.65.issue-5Guerrero-Solé, F. (2018). Interactive Behavior in Political Discussions on Twitter: Politicians, Media, and Citizens’ Patterns of Interaction in the 2015 and 2016 Electoral Campaigns in Spain. Social Media + Society, 4(4), 205630511880877. doi:10.1177/2056305118808776Guo, L., & Vargo, C. (2015). The Power of Message Networks: A Big-Data Analysis of the Network Agenda Setting Model and Issue Ownership. Mass Communication and Society, 18(5), 557-576. doi:10.1080/15205436.2015.1045300Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter. Journal of Computer-Mediated Communication, 18(2), 40-60. doi:10.1111/jcc4.12001Huckfeldt, R., Johnson, P. E., & Sprague, J. (2004). Political Disagreement. doi:10.1017/cbo9780511617102Brundidge, J. (2010). Encountering «Difference» in the Contemporary Public Sphere: The Contribution of the Internet to the Heterogeneity of Political Discussion Networks. Journal of Communication, 60(4), 680-700. doi:10.1111/j.1460-2466.2010.01509.xJungherr, A. (2015). Analyzing Political Communication with Digital Trace Data. Contributions to Political Science. doi:10.1007/978-3-319-20319-5Jungherr, A., Jürgens, P., & Schoen, H. (2011). Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. «Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment». Social Science Computer Review, 30(2), 229-234. doi:10.1177/0894439311404119Kaiser, H. F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20(1), 141-151. doi:10.1177/001316446002000116Klinger, U., & Svensson, J. (2014). The emergence of network media logic in political communication: A theoretical approach. New Media & Society, 17(8), 1241-1257. doi:10.1177/1461444814522952Lavezzolo, S., & Ramiro, L. (2017). Stealth democracy and the support for new and challenger parties. European Political Science Review, 10(2), 267-289. doi:10.1017/s1755773917000108McGregor, S. C., Mourão, R. R., & Molyneux, L. (2017). Twitter as a tool for and object of political and electoral activity: Considering electoral context and variance among actors. Journal of Information Technology & Politics, 14(2), 154-167. doi:10.1080/19331681.2017.1308289McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1), 415-444. doi:10.1146/annurev.soc.27.1.415Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. doi:10.1016/j.asej.2014.04.011Min, Y. (2004). News Coverage of Negative Political Campaigns. Harvard International Journal of Press/Politics, 9(4), 95-111. doi:10.1177/1081180x04271861Newman, M. (2010). Networks. doi:10.1093/acprof:oso/9780199206650.001.0001Orriols, L., & Cordero, G. (2016). The Breakdown of the Spanish Two-Party System: The Upsurge of Podemos and Ciudadanos in the 2015 General Election. South European Society and Politics, 21(4), 469-492. doi:10.1080/13608746.2016.1198454Park, C. S. (2013). Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29(4), 1641-1648. doi:10.1016/j.chb.2013.01.044Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management, 52(5), 949-975. doi:10.1016/j.ipm.2016.04.003Robinson, J. P. (1976). Interpersonal Influence in Election Campaigns: Two Step-Flow Hypotheses. Public Opinion Quarterly, 40(3), 304. doi:10.1086/268307Robles, J. M., Díez, R., R. Castromil, A., Rodríguez, A., & Cruz, M. (2015). El movimiento 15-M en los medios y en las redes. Un análisis de sus estrategias comunicativas. Empiria. Revista de metodología de ciencias sociales, 0(32), 37. doi:10.5944/empiria.32.2015.15308Recerca. Revista de pensament i anàlisi. (s. f.). doi:10.6035/recercaSunstein, C. R. (2017). #Republic. doi:10.1515/9781400884711Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Vaccari, C., Chadwick, A., & O’Loughlin, B. (2015). Dual Screening the Political: Media Events, Social Media, and Citizen Engagement. Journal of Communication, 65(6), 1041-1061. doi:10.1111/jcom.12187Vergeer, M., & Hermans, L. (2013). Campaigning on Twitter: Microblogging and Online Social Networking as Campaign Tools in the 2010 General Elections in the Netherlands. Journal of Computer-Mediated Communication, 18(4), 399-419. doi:10.1111/jcc4.12023Vilares, D., Thelwall, M., & Alonso, M. A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41(6), 799-813. doi:10.1177/0165551515598926Weimann, G. (1991). The Influentials: Back to the Concept of Opinion Leaders? Public Opinion Quarterly, 55(2), 267. doi:10.1086/269257Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Who says what to whom on twitter. Proceedings of the 20th international conference on World wide web - WWW ’11. doi:10.1145/1963405.1963504Xu, W. W., Sang, Y., Blasiola, S., & Park, H. W. (2014). Predicting Opinion Leaders in Twitter Activism Networks. American Behavioral Scientist, 58(10), 1278-1293. doi:10.1177/0002764214527091Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., … Quattrociocchi, W. (2015). Emotional Dynamics in the Age of Misinformation. PLOS ONE, 10(9), e0138740. doi:10.1371/journal.pone.013874

    Big Line Bundles over Arithmetic Varieties

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    We prove a Hilbert-Samuel type result of arithmetic big line bundles in Arakelov geometry, which is an analogue of a classical theorem of Siu. An application of this result gives equidistribution of small points over algebraic dynamical systems, following the work of Szpiro-Ullmo-Zhang. We also generalize Chambert-Loir's non-archimedean equidistribution

    On the classification of OADP varieties

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    The main purpose of this paper is to show that OADP varieties stand at an important crossroad of various main streets in different disciplines like projective geometry, birational geometry and algebra. This is a good reason for studying and classifying them. Main specific results are: (a) the classification of all OADP surfaces (regardless to their smoothness); (b) the classification of a relevant class of normal OADP varieties of any dimension, which includes interesting examples like lagrangian grassmannians. Following [PR], the equivalence of the classification in (b) with the one of quadro-quadric Cremona transformations and of complex, unitary, cubic Jordan algebras are explained.Comment: 13 pages. Dedicated to Fabrizio Catanese on the occasion of his 60th birthday. To appear in a special issue of Science in China Series A: Mathematic

    The media effect in Axelrod's model explained

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    We revisit the problem of introducing an external global field -- the mass media -- in Axelrod's model of social dynamics, where in addition to their nearest neighbors, the agents can interact with a virtual neighbor whose cultural features are fixed from the outset. The finding that this apparently homogenizing field actually increases the cultural diversity has been considered a puzzle since the phenomenon was first reported more than a decade ago. Here we offer a simple explanation for it, which is based on the pedestrian observation that Axelrod's model exhibits more cultural diversity, i.e., more distinct cultural domains, when the agents are allowed to interact solely with the media field than when they can interact with their neighbors as well. In this perspective, it is the local homogenizing interactions that work towards making the absorbing configurations less fragmented as compared with the extreme situation in which the agents interact with the media only

    Triangulations and Severi varieties

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    We consider the problem of constructing triangulations of projective planes over Hurwitz algebras with minimal numbers of vertices. We observe that the numbers of faces of each dimension must be equal to the dimensions of certain representations of the automorphism groups of the corresponding Severi varieties. We construct a complex involving these representations, which should be considered as a geometric version of the (putative) triangulations

    Human group formation in online guilds and offline gangs driven by common team dynamic

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    Quantifying human group dynamics represents a unique challenge. Unlike animals and other biological systems, humans form groups in both real (offline) and virtual (online) spaces -- from potentially dangerous street gangs populated mostly by disaffected male youths, through to the massive global guilds in online role-playing games for which membership currently exceeds tens of millions of people from all possible backgrounds, age-groups and genders. We have compiled and analyzed data for these two seemingly unrelated offline and online human activities, and have uncovered an unexpected quantitative link between them. Although their overall dynamics differ visibly, we find that a common team-based model can accurately reproduce the quantitative features of each simply by adjusting the average tolerance level and attribute range for each population. By contrast, we find no evidence to support a version of the model based on like-seeking-like (i.e. kinship or `homophily')

    Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

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    BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research

    On the canonical map of surfaces with q>=6

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    We carry out an analysis of the canonical system of a minimal complex surface of general type with irregularity q>0. Using this analysis we are able to sharpen in the case q>0 the well known Castelnuovo inequality K^2>=3p_g+q-7. Then we turn to the study of surfaces with p_g=2q-3 and no fibration onto a curve of genus >1. We prove that for q>=6 the canonical map is birational. Combining this result with the analysis of the canonical system, we also prove the inequality: K^2>=7\chi+2. This improves an earlier result of the first and second author [M.Mendes Lopes and R.Pardini, On surfaces with p_g=2q-3, Adv. in Geom. 10 (3) (2010), 549-555].Comment: Dedicated to Fabrizio Catanese on the occasion of his 60th birthday. To appear in the special issue of Science of China Ser.A: Mathematics dedicated to him. V2:some typos have been correcte

    Longitudinal Peer Network Data in Higher Education

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    This chapter employs a longitudinal social network approach to research small group teaching in higher education. Longitudinal social network analyses can provide in-depth understanding of the social dynamics in small groups. Specifically, it is possible to investigate and disentangle the processes by which students make or break social connections with peers and are influenced by them, as well as how those processes relate to group compositions and personal attributes, such as achievement level. With advanced methods for modelling longitudinal social networks, researchers can identify social processes affecting small group teaching and learning
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