Authors:
(1) Anees Baqir, Ca’ Foscari University of Venice, Italy;
(2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy;
(3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Table of Links Abstract and Intro
Materials and Methods
Results and Discussion
Conclusions and References
Supplementary Information 4. Conclusions In this study, we have delved into the evolving dynamics of news production and consumption within the European context. We examined the consumption of Twitter content produced by news outlets in France, Germany, Italy, and the United Kingdom, providing a cross-country and cross-topic comparison of the online public discourse. We identified topics debated across all four countries and highlighted differences and similarities in consumption patterns. Additionally, we constructed networks based on the similarities among news outlets’ audiences, revealing the presence of groups of users engaging with sources of different reliability. Our findings indicated that reliable sources dominate the information landscape, but users consuming content mainly or exclusively from questionable news outlets were often present. However, the size and importance of such groups vary based on the topic and the country under consideration. Furthermore, our cross-country comparison has revealed variations in the structure of news sources’ similarity networks. While some countries exhibited a clearer separation between clusters of questionable sources and reliable sources, others showed a more heterogeneous situation with less detectable differences in cluster composition. However, the connectedness of the networks and users’ behavior analysis indicated the presence of a small fraction of users with a mixed news diet in all countries. Our results emphasized the differences and similarities in news consumption patterns across countries in relation to globally significant subjects. Understanding the dynamic of news consumption and its dependence on factors such as the topic or country can provide valuable insights into the development of effective countermeasures to mitigate the spread of misinformation and disinformation. Monitoring the information landscape at both national and European levels is indeed crucial to understanding the state of public discourse on contentious topics and developing tailored cohesive strategies to improve the health of information ecosystems. References European commission, the digital services act package. accessed on 23-10-2023. Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Identifying influencers on twitter. In Fourth ACM International Conference on Web Seach and Data Mining (WSDM), volume 2. Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132. Bessi, A. and Ferrara, E. (2016). Social bots distort the 2016 us presidential election online discussion. First monday, 21(11-7). Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008. Bovet, A. and Makse, H. A. (2019). Influence of fake news in twitter during the 2016 us presidential election. Nature communications, 10(1):7. Broniatowski, D. A., Simons, J. R., Gu, J., Jamison, A. M., and Abroms, L. C. (2023). The efficacy of facebook’s vaccine misinformation policies and architecture during the covid-19 pandemic. Science Advances, 9(37):eadh2132. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., and Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9):e2023301118. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., and Scala, A. (2020). The covid-19 social media infodemic. Scientific reports, 10(1):1–10. Cota, W., Ferreira, S. C., Pastor-Satorras, R., and Starnini, M. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1):35. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., and Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the national academy of Sciences, 113(3):554–559. Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., and Quattrociocchi, W. (2017). Mapping social dynamics on facebook: The brexit debate. Social Networks, 50:6–16. Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al. (2022). Growing polarization around climate change on social media. Nature Climate Change, pages 1–8. Ferrara, E. (2017). Disinformation and social bot operations in the run up to the 2017 french presidential election. arXiv preprint arXiv:1707.00086. Ferrara, E., Cresci, S., and Luceri, L. (2020). Misinformation, manipulation, and abuse on social media in the era of covid-19. Journal of Computational Social Science, 3:271–277. Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., and Szymanski, B. K. (2023). Political polarization of news media and influencers on twitter in the 2016 and 2020 us presidential elections. Nature Human Behaviour, pages 1–13. Flaxman, S., Goel, S., and Rao, J. M. (2013). Ideological segregation and the effects of social media on news consumption. Available at SSRN, 2363701. Garimella, K., Smith, T., Weiss, R., and West, R. (2021). Political polarization in online news consumption. In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 152–162. Gonz´alez-Bail´on, S., Lazer, D., Barber´a, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A. M., et al. (2023). Asymmetric ideological segregation in exposure to political news on facebook. Science, 381(6656):392–398. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., and Lazer, D. (2019). Fake news on twitter during the 2016 us presidential election. Science, 363(6425):374–378. Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794. Karimi, F. and Oliveira, M. (2022). On the inadequacy of nominal assortativity for assessing homophily in networks. arXiv preprint arXiv:2211.10245. Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018). The science of fake news. Science, 359(6380):1094–1096. McInnes, L., Healy, J., and Astels, S. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2(11):205. McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426. Nyhan, B., Settle, J., Thorson, E., Wojcieszak, M., Barber´a, P., Chen, A. Y., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., et al. (2023). Like-minded sources on facebook are prevalent but not polarizing. Nature, 620(7972):137–144. Ruths, D. (2019). The misinformation machine. Science, 363(6425):348–348. Sammut, C. and Webb, G. I. (2011). Encyclopedia of machine learning. Springer Science & Business Media. Santoro, A., Galeazzi, A., Scantamburlo, T., Baronchelli, A., Quattrociocchi, W., and Zollo, F. (2023). Analyzing the changing landscape of the covid-19 vaccine debate on twitter. Social Network Analysis and Mining, 13(1):115. Schmidt, A. L., Zollo, F., Scala, A., Betsch, C., and Quattrociocchi, W. (2018). Polarization of the vaccination debate on facebook. Vaccine, 36(25):3606–3612. Stella, M., Ferrara, E., and De Domenico, M. (2018). Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49):12435–12440. Zannettou, S., Bradlyn, B., De Cristofaro, E., Kwak, H., Sirivianos, M., Stringini, G., and Blackburn, J. (2018). What is gab: A bastion of free speech or an alt-right echo chamber. In Companion Proceedings of the The Web Conference 2018, pages 1007–1014. Zannettou, S., Caulfield, T., De Cristofaro, E., Sirivianos, M., Stringhini, G., and Blackburn, J. (2019). Disinformation warfare: Understanding state-sponsored trolls on twitter and their influence on the web. In Companion proceedings of the 2019 world wide web conference, pages 218–226. This paper is available on arxiv under CC 4.0 license. Authors: (1) Anees Baqir, Ca’ Foscari University of Venice, Italy; (2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy; (3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Authors: Authors: (1) Anees Baqir, Ca’ Foscari University of Venice, Italy; (2) Alessandro Galeazzi, Ca’ Foscari University of Venice, Italy; (3) Fabiana Zollo, Ca’ Foscari University of Venice, Italy and The New Institute Centre for Environmental Humanities, Italy. Table of Links Abstract and Intro Materials and Methods Results and Discussion Conclusions and References Supplementary Information Abstract and Intro Abstract and Intro Materials and Methods Materials and Methods Results and Discussion Results and Discussion Conclusions and References Conclusions and References Supplementary Information Supplementary Information 4. Conclusions In this study, we have delved into the evolving dynamics of news production and consumption within the European context. We examined the consumption of Twitter content produced by news outlets in France, Germany, Italy, and the United Kingdom, providing a cross-country and cross-topic comparison of the online public discourse. We identified topics debated across all four countries and highlighted differences and similarities in consumption patterns. Additionally, we constructed networks based on the similarities among news outlets’ audiences, revealing the presence of groups of users engaging with sources of different reliability. Our findings indicated that reliable sources dominate the information landscape, but users consuming content mainly or exclusively from questionable news outlets were often present. However, the size and importance of such groups vary based on the topic and the country under consideration. Furthermore, our cross-country comparison has revealed variations in the structure of news sources’ similarity networks. While some countries exhibited a clearer separation between clusters of questionable sources and reliable sources, others showed a more heterogeneous situation with less detectable differences in cluster composition. However, the connectedness of the networks and users’ behavior analysis indicated the presence of a small fraction of users with a mixed news diet in all countries. Our results emphasized the differences and similarities in news consumption patterns across countries in relation to globally significant subjects. Understanding the dynamic of news consumption and its dependence on factors such as the topic or country can provide valuable insights into the development of effective countermeasures to mitigate the spread of misinformation and disinformation. Monitoring the information landscape at both national and European levels is indeed crucial to understanding the state of public discourse on contentious topics and developing tailored cohesive strategies to improve the health of information ecosystems. References European commission, the digital services act package. accessed on 23-10-2023. Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Identifying influencers on twitter. In Fourth ACM International Conference on Web Seach and Data Mining (WSDM), volume 2. Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132. Bessi, A. and Ferrara, E. (2016). Social bots distort the 2016 us presidential election online discussion. First monday, 21(11-7). Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008. Bovet, A. and Makse, H. A. (2019). Influence of fake news in twitter during the 2016 us presidential election. Nature communications, 10(1):7. Broniatowski, D. A., Simons, J. R., Gu, J., Jamison, A. M., and Abroms, L. C. (2023). The efficacy of facebook’s vaccine misinformation policies and architecture during the covid-19 pandemic. Science Advances, 9(37):eadh2132. Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., and Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9):e2023301118. Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., and Scala, A. (2020). The covid-19 social media infodemic. Scientific reports, 10(1):1–10. Cota, W., Ferreira, S. C., Pastor-Satorras, R., and Starnini, M. (2019). Quantifying echo chamber effects in information spreading over political communication networks. EPJ Data Science, 8(1):35. Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., and Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the national academy of Sciences, 113(3):554–559. Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., and Quattrociocchi, W. (2017). Mapping social dynamics on facebook: The brexit debate. Social Networks, 50:6–16. Falkenberg, M., Galeazzi, A., Torricelli, M., Di Marco, N., Larosa, F., Sas, M., Mekacher, A., Pearce, W., Zollo, F., Quattrociocchi, W., et al. (2022). Growing polarization around climate change on social media. Nature Climate Change, pages 1–8. Ferrara, E. (2017). Disinformation and social bot operations in the run up to the 2017 french presidential election. arXiv preprint arXiv:1707.00086. Ferrara, E., Cresci, S., and Luceri, L. (2020). Misinformation, manipulation, and abuse on social media in the era of covid-19. Journal of Computational Social Science, 3:271–277. Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., and Szymanski, B. K. (2023). Political polarization of news media and influencers on twitter in the 2016 and 2020 us presidential elections. Nature Human Behaviour, pages 1–13. Flaxman, S., Goel, S., and Rao, J. M. (2013). Ideological segregation and the effects of social media on news consumption. Available at SSRN, 2363701. Garimella, K., Smith, T., Weiss, R., and West, R. (2021). Political polarization in online news consumption. In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 152–162. Gonz´alez-Bail´on, S., Lazer, D., Barber´a, P., Zhang, M., Allcott, H., Brown, T., Crespo-Tenorio, A., Freelon, D., Gentzkow, M., Guess, A. M., et al. (2023). Asymmetric ideological segregation in exposure to political news on facebook. Science, 381(6656):392–398. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., and Lazer, D. (2019). Fake news on twitter during the 2016 us presidential election. Science, 363(6425):374–378. Grootendorst, M. (2022). Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794. Karimi, F. and Oliveira, M. (2022). On the inadequacy of nominal assortativity for assessing homophily in networks. arXiv preprint arXiv:2211.10245. Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018). The science of fake news. Science, 359(6380):1094–1096. McInnes, L., Healy, J., and Astels, S. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2(11):205. McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426. Nyhan, B., Settle, J., Thorson, E., Wojcieszak, M., Barber´a, P., Chen, A. Y., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., et al. (2023). Like-minded sources on facebook are prevalent but not polarizing. Nature, 620(7972):137–144. Ruths, D. (2019). The misinformation machine. Science, 363(6425):348–348. Sammut, C. and Webb, G. I. (2011). Encyclopedia of machine learning. Springer Science & Business Media. Santoro, A., Galeazzi, A., Scantamburlo, T., Baronchelli, A., Quattrociocchi, W., and Zollo, F. (2023). Analyzing the changing landscape of the covid-19 vaccine debate on twitter. Social Network Analysis and Mining, 13(1):115. Schmidt, A. L., Zollo, F., Scala, A., Betsch, C., and Quattrociocchi, W. (2018). Polarization of the vaccination debate on facebook. Vaccine, 36(25):3606–3612. Stella, M., Ferrara, E., and De Domenico, M. (2018). Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49):12435–12440. Zannettou, S., Bradlyn, B., De Cristofaro, E., Kwak, H., Sirivianos, M., Stringini, G., and Blackburn, J. (2018). What is gab: A bastion of free speech or an alt-right echo chamber. In Companion Proceedings of the The Web Conference 2018, pages 1007–1014. Zannettou, S., Caulfield, T., De Cristofaro, E., Sirivianos, M., Stringhini, G., and Blackburn, J. (2019). Disinformation warfare: Understanding state-sponsored trolls on twitter and their influence on the web. In Companion proceedings of the 2019 world wide web conference, pages 218–226. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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