Italian Twitter semantic network during the Covid-19 epidemic
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Guido Caldarelli | Fabio Saracco | Tiziano Squartini | Mattia Mattei | G. Caldarelli | T. Squartini | F. Saracco | Mattia Mattei | Tiziano Squartini
[1] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[2] M E J Newman,et al. Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[3] Andrea Gabrielli,et al. Randomizing bipartite networks: the case of the World Trade Web , 2015, Scientific Reports.
[4] Fabio Saracco,et al. Networked partisanship and framing: A socio-semantic network analysis of the Italian debate on migration , 2021, PloS one.
[5] Giulio Cimini,et al. Unfolding the innovation system for the development of countries: coevolution of Science, Technology and Production , 2017, Scientific Reports.
[6] Thomas Blaschke,et al. Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems - An Overview , 2011, Remote. Sens..
[7] V. Loreto,et al. Hamiltonian modelling of macro-economic urban dynamics , 2020, Royal Society Open Science.
[8] Jesse M. Shapiro,et al. Ideological Segregation Online and Offline , 2010 .
[9] Bruno Lepri,et al. Segregated interactions in urban and online space , 2020, EPJ Data Science.
[10] Réka Albert,et al. Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[11] Diego Garlaschelli,et al. Unbiased sampling of network ensembles , 2014, ArXiv.
[12] Guido Caldarelli,et al. Entropy-based randomisation of rating networks , 2018, Physical review. E.
[13] Guido Caldarelli,et al. Debunking in a world of tribes , 2015, PloS one.
[14] Diego Garlaschelli,et al. Maximum-Entropy Networks: Pattern Detection, Network Reconstruction and Graph Combinatorics , 2017 .
[15] D. Garlaschelli,et al. Reconstruction methods for networks: The case of economic and financial systems , 2018, Physics Reports.
[16] Guido Caldarelli,et al. Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections , 2019, Palgrave Communications.
[17] János Kertész,et al. Inequality is rising where social network segregation interacts with urban topology , 2019, Nature communications.
[18] Diego Garlaschelli,et al. Breaking of Ensemble Equivalence in Networks. , 2015, Physical review letters.
[19] Giorgio Fagiolo,et al. Enhanced reconstruction of weighted networks from strengths and degrees , 2013, 1307.2104.
[20] Fabio Saracco,et al. Analysing Twitter semantic networks: the case of 2018 Italian elections , 2020, Scientific Reports.
[21] Guido Caldarelli,et al. The role of bot squads in the political propaganda on Twitter , 2019, Communications Physics.
[22] Giulio Cimini,et al. Statistically validated network of portfolio overlaps and systemic risk , 2016, Scientific Reports.
[23] Jacob Ratkiewicz,et al. Political Polarization on Twitter , 2011, ICWSM.
[24] G. Caldarelli,et al. Flow of online misinformation during the peak of the COVID-19 pandemic in Italy , 2020, EPJ Data Science.
[25] Claudio J. Tessone,et al. The ambiguity of nestedness under soft and hard constraints , 2020, Scientific reports.
[26] Angelo Spognardi,et al. Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection , 2019, WebSci.
[27] A. Baronchelli,et al. The geographic embedding of online echo chambers: Evidence from the Brexit campaign , 2018, PloS one.
[28] Thierry Mora,et al. Local equilibrium in bird flocks , 2015, Nature Physics.
[29] A. Maritan,et al. Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns , 2006, Proceedings of the National Academy of Sciences.
[30] E. Jaynes. Information Theory and Statistical Mechanics , 1957 .
[31] Hernán A. Makse,et al. CUNY Academic Works , 2022 .
[32] Yili Hong,et al. On computing the distribution function for the Poisson binomial distribution , 2013, Comput. Stat. Data Anal..
[33] C. J. Carstens. Proof of uniform sampling of binary matrices with fixed row sums and column sums for the fast Curveball algorithm. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[34] Ideological Segregation Online and Offline , 2010 .
[35] A. L. Schmidt,et al. The COVID-19 social media infodemic , 2020, Scientific Reports.
[36] Manlio De Domenico,et al. Assessing the risks of 'infodemics' in response to COVID-19 epidemics. , 2020, Nature human behaviour.
[37] Filippo Menczer,et al. The rise of social bots , 2014, Commun. ACM.
[38] M. Newman,et al. Statistical mechanics of networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[39] T. Squartini,et al. Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints , 2021, Scientific Reports.
[40] Lada A. Adamic,et al. Exposure to ideologically diverse news and opinion on Facebook , 2015, Science.
[41] Filippo Menczer,et al. Arming the public with AI to counter social bots , 2019, ArXiv.
[42] Guido Caldarelli,et al. Users Polarization on Facebook and Youtube , 2016, PloS one.
[43] Chao Yang,et al. Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers , 2013, IEEE Trans. Inf. Forensics Secur..
[44] Andrea Gabrielli,et al. Inferring monopartite projections of bipartite networks: an entropy-based approach , 2016 .
[45] Diego Garlaschelli,et al. Irreducible network backbones: unbiased graph filtering via maximum entropy , 2017, ArXiv.
[46] Fast and scalable likelihood maximization for Exponential Random Graph Models , 2021 .
[47] G. Caldarelli,et al. Firms’ challenges and social responsibilities during Covid-19: A Twitter analysis , 2021, PloS one.
[48] F. Chung,et al. Connected Components in Random Graphs with Given Expected Degree Sequences , 2002 .
[49] A. Bhagavathula,et al. COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study , 2020, JMIR public health and surveillance.
[50] Matteo Cinelli,et al. The echo chamber effect on social media , 2021, Proceedings of the National Academy of Sciences.
[51] Giulio Cimini,et al. The statistical physics of real-world networks , 2018, Nature Reviews Physics.
[52] Riccardo Di Clemente,et al. bmotif: a package for motif analyses of bipartite networks , 2018, bioRxiv.
[53] D. Garlaschelli,et al. Early-warning signals of topological collapse in interbank networks , 2013, Scientific Reports.
[54] W. Bialek,et al. Statistical mechanics for natural flocks of birds , 2011, Proceedings of the National Academy of Sciences.
[55] Guido Caldarelli,et al. Echo Chambers: Emotional Contagion and Group Polarization on Facebook , 2016, Scientific Reports.
[56] Fabio Saracco,et al. Detecting early signs of the 2007–2008 crisis in the world trade , 2015, Scientific Reports.
[57] M. De Domenico,et al. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics , 2020, Nature Human Behaviour.
[58] Francesco Pierri,et al. Information disorders on Italian Facebook during COVID-19 infodemic , 2020, ArXiv.
[59] W. Bialek,et al. Maximum entropy models for antibody diversity , 2009, Proceedings of the National Academy of Sciences.
[60] Filippo Menczer,et al. How algorithmic popularity bias hinders or promotes quality , 2017, Scientific Reports.
[61] D. Garlaschelli,et al. Maximum likelihood: extracting unbiased information from complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[62] Lada A. Adamic,et al. The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.
[63] Chao Yang,et al. Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers , 2011, IEEE Transactions on Information Forensics and Security.
[64] Roberto Di Pietro,et al. Fame for sale: Efficient detection of fake Twitter followers , 2015, Decis. Support Syst..
[65] Diego Garlaschelli,et al. Analytical maximum-likelihood method to detect patterns in real networks , 2011, 1103.0701.
[66] Giovanni Strona,et al. A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals , 2014, Nature Communications.
[67] Steven E Wheeler,et al. Local nature of substituent effects in stacking interactions. , 2011, Journal of the American Chemical Society.
[68] Manlio De Domenico,et al. Influence of augmented humans in online interactions during voting events , 2018, PloS one.
[69] Mark Newman,et al. Networks: An Introduction , 2010 .
[70] Yamir Moreno,et al. Broadcasters and Hidden Influentials in Online Protest Diffusion , 2012, ArXiv.
[71] Jean-Loup Guillaume,et al. Fast unfolding of communities in large networks , 2008, 0803.0476.