IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v5y2019i1d10.1057_s41599-019-0300-3.html
   My bibliography  Save this article

Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections

Author

Listed:
  • Carolina Becatti

    (IMT School for Advanced Studies)

  • Guido Caldarelli

    (IMT School for Advanced Studies
    Istituto dei Sistemi Complessi (CNR) UoS Università “Sapienza”
    Università di Venezia “Ca’ Foscari”
    Talent Garden Poste Italiane)

  • Renaud Lambiotte

    (University of Oxford)

  • Fabio Saracco

    (IMT School for Advanced Studies)

Abstract

According to the Eurobarometer report about EU media use of May 2018, the number of European citizens who consult on-line social networks for accessing information is considerably increasing. In this work we analyse approximately 106 tweets exchanged during the last Italian elections held on March 4, 2018. Using an entropy-based null model discounting the activity of the users, we first identify potential political alliances within the group of verified accounts: if two verified users are retweeted more than expected by the non-verified ones, they are likely to be related. Then, we derive the users’ affiliation to a coalition measuring the polarisation of unverified accounts. Finally, we study the bipartite directed representation of the tweets and retweets network, in which tweets and users are collected on the two layers. Users with the highest out-degree identify the most popular ones, whereas highest out-degree posts are the most “viral”. We identify significant content spreaders with a procedure that allows to statistically validate the connections that cannot be explained by users’ tweeting activity and posts’ virality, using an entropy-based null model as benchmark. The analysis of the directed network of validated retweets reveals signals of the alliances formed after the elections, highlighting commonalities of interests before the event of the national elections.

Suggested Citation

  • Carolina Becatti & Guido Caldarelli & Renaud Lambiotte & Fabio Saracco, 2019. "Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-16, December.
    • Handle: RePEc:pal:palcom:v:5:y:2019:i:1:d:10.1057_s41599-019-0300-3
    DOI: 10.1057/s41599-019-0300-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-019-0300-3
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-019-0300-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Di Gangi, Domenico & Lillo, Fabrizio & Pirino, Davide, 2018. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Journal of Economic Dynamics and Control, Elsevier, vol. 94(C), pages 117-141.
    2. Stanislao Gualdi & Giulio Cimini & Kevin Primicerio & Riccardo Di Clemente & Damien Challet, 2016. "Statistically validated network of portfolio overlaps and systemic risk," Papers 1603.05914, arXiv.org, revised Sep 2016.
    3. Domenico Di Gangi & Fabrizio Lillo & Davide Pirino, 2015. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Papers 1509.00607, arXiv.org, revised Jul 2018.
    4. Tiziano Squartini & Diego Garlaschelli, 2011. "Exact maximum-likelihood method to detect patterns in real networks," LEM Papers Series 2011/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Fabio Saracco & Mika J. Straka & Riccardo Di Clemente & Andrea Gabrielli & Guido Caldarelli & Tiziano Squartini, 2016. "Inferring monopartite projections of bipartite networks: an entropy-based approach," Papers 1607.02481, arXiv.org, revised May 2017.
    6. Fabio Saracco & Riccardo Di Clemente & Andrea Gabrielli & Tiziano Squartini, 2015. "Randomizing bipartite networks: the case of the World Trade Web," Papers 1503.05098, arXiv.org, revised Jun 2015.
    7. Tiziano Squartini & Iman van Lelyveld & Diego Garlaschelli, 2013. "Early-warning signals of topological collapse in interbank networks," Papers 1302.2063, arXiv.org, revised Nov 2013.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ioan Batrancea & Mehmet Ali Balcı & Larissa M. Batrancea & Ömer Akgüller & Horia Tulai & Mircea-Iosif Rus & Ema Speranta Masca & Ioan Dan Morar, 2024. "Topic Analysis of Social Media Posts during the COVID-19 Pandemic: Evidence from Tweets in Turkish," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 12361-12391, September.
    2. Côme Billard & Anna Creti & Antoine Mandel, 2020. "How Environmental Policies Spread? A Network Approach to Diffusion in the U.S," Working Papers 2020.12, FAERE - French Association of Environmental and Resource Economists.
    3. Meng-Jie Wang & Kumar Yogeeswaran & Sivanand Sivaram & Kyle Nash, 2021. "Examining spread of emotional political content among Democratic and Republican candidates during the 2018 US mid-term elections," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    4. Mary Sanford & Jamie Lorimer, 2022. "Veganuary and the vegan sausage (t)rolls: conflict and commercial engagement in online climate-diet discourse," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    5. Maulana, Ardian & Situngkir, Hokky, 2020. "Measuring Media Partisanship during Election: The Case of 2019 Indonesia Election," MPRA Paper 101950, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
    2. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2020. "Reconstructing and stress testing credit networks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    3. Fessina, Massimiliano & Zaccaria, Andrea & Cimini, Giulio & Squartini, Tiziano, 2024. "Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Barucca, Paolo & Mahmood, Tahir & Silvestri, Laura, 2021. "Common asset holdings and systemic vulnerability across multiple types of financial institution," Journal of Financial Stability, Elsevier, vol. 52(C).
    5. Mika J. Straka & Guido Caldarelli & Tiziano Squartini & Fabio Saracco, 2017. "From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networks," Papers 1710.10143, arXiv.org.
    6. Lin, Li & Guo, Xin-Yu, 2019. "Identifying fragility for the stock market: Perspective from the portfolio overlaps network," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 132-151.
    7. Tiziano Squartini & Guido Caldarelli & Giulio Cimini & Andrea Gabrielli & Diego Garlaschelli, 2018. "Reconstruction methods for networks: the case of economic and financial systems," Papers 1806.06941, arXiv.org.
    8. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2019. "Reconstructing and stress testing credit networks," LSE Research Online Documents on Economics 118938, London School of Economics and Political Science, LSE Library.
    9. Carattini, Stefano & Fankhauser, Sam & Gao, Jianjian & Gennaioli, Caterina & Panzarasa, Pietro, 2023. "What does network analysis teach us about international environmental cooperation?," Ecological Economics, Elsevier, vol. 205(C).
    10. Marc van Kralingen & Diego Garlaschelli & Karolina Scholtus & Iman van Lelyveld, 2020. "Crowded trades, market clustering, and price instability," Papers 2002.03319, arXiv.org.
    11. Matteo Bruno & Dario Mazzilli & Aurelio Patelli & Tiziano Squartini & Fabio Saracco, 2023. "Inferring comparative advantage via entropy maximization," Papers 2304.12245, arXiv.org.
    12. Jeroen van Lidth de Jeude & Riccardo Di Clemente & Guido Caldarelli & Fabio Saracco & Tiziano Squartini, 2019. "Reconstructing Mesoscale Network Structures," Complexity, Hindawi, vol. 2019, pages 1-13, January.
    13. Macchiati, Valentina & Mazzarisi, Piero & Garlaschelli, Diego, 2024. "Interbank network reconstruction enforcing density and reciprocity," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    14. Andrea Flori & Fabrizio Lillo & Fabio Pammolli & Alessandro Spelta, 2021. "Better to stay apart: asset commonality, bipartite network centrality, and investment strategies," Annals of Operations Research, Springer, vol. 299(1), pages 177-213, April.
    15. Alessandro Ferracci & Giulio Cimini, 2021. "Systemic risk in interbank networks: disentangling balance sheets and network effects," Papers 2109.14360, arXiv.org, revised Sep 2022.
    16. Andreas Muhlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Papers 1803.00261, arXiv.org.
    17. Domenico Di Gangi & Fabrizio Lillo & Davide Pirino, 2015. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Papers 1509.00607, arXiv.org, revised Jul 2018.
    18. Fabio Saracco & Riccardo Di Clemente & Andrea Gabrielli & Tiziano Squartini, 2015. "Detecting early signs of the 2007-2008 crisis in the world trade," Papers 1508.03533, arXiv.org, revised Jul 2016.
    19. Ramadiah, Amanah & Fricke, Daniel & Caccioli, Fabio, 2022. "Backtesting macroprudential stress tests," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    20. Michel Alexandre & Thiago Christiano Silva & Colm Connaughton & Francisco A. Rodrigues, 2021. "The Role of (non-)Topological Features as Drivers of Systemic Risk: a machine learning approach," Working Papers Series 556, Central Bank of Brazil, Research Department.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:5:y:2019:i:1:d:10.1057_s41599-019-0300-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.