Characterizing Reactions and Comments Associated with News on Facebook
Resumo
News consumption is increasingly done on social media websites. In this environment, all types of entities and people present themselves as news sources. These new outlets might focus on specific audiences, and some exhibit the news less objectively. Facebook is one of these platforms, which categorizes an extensive group of pages as a kind of news media. To analyze this phenomenon, it is crucial to characterize all pages that disseminate information in this ecosystem. Our main objective is to create an in-depth diagnostic of news stories and opinions, focusing on Brazilian Facebook. Our contributions are: (i) a new method to measure the political bias of Facebook pages on a given country, and (ii) a detailed characterization of a comprehensive sample of these pages.Referências
Araujo, R., Maciel, R., and Boscarioli, C. (2017). I grandsi-br: Grandes desafios de pesquisa em sistemas de informação no brasil (2016-2026). Relatório Técnico. Comissão Especial de Sistemas de Informação (CE-SI) da Sociedade Brasileira de Computação (SBC). 67p.
Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132.
Basile, A., Caselli, T., and Nissim, M. (2017). Predicting controversial news using facebook reactions. In Proceedings of the 4th Italian Conference on Computational Linguistics, pages 12–17.
Budak, C., Goel, S., and Rao, J. M. (2016). Fair and balanced? quantifying media bias through crowdsourced content analysis. Public Opinion Quarterly, 80(S1):250–271.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.
D’Alessio, D. and Allen, M. (2000). Media bias in presidential elections: A meta-analysis. Journal of communication, 50(4):133–156.
de Pelle, R. P. and Moreira, V. P. (2017). Offensive comments in the brazilian web: a dataset and baseline results. In Proceedings of the 6th Brazilian Workshop on Social Network Analysis and Mining, pages 510–519.
Druckman, J. N. and Parkin, M. (2005). The impact of media bias: How editorial slant affects voters. The Journal of Politics, 67(4):1030–1049.
Entman, R. M. (2007). Framing bias: Media in the distribution of power. Journal of communication, 57(1):163–173.
Floyd, R.W. (1962). Algorithm 97: shortest path. Communications of the ACM, 5(6):345.
Fortuna, P. and Nunes, S. (2018). A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):85.
Guimarães, S. S., Reis, J. C. S., Lima, L., Ribeiro, F. N., Vasconcelos, M., An, J., Kwak, H., and Benevenuto, F. (2020a). Identifying and characterizing alternative news media on facebook. In Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 448–452.
Guimarães, S. S., Reis, J. C. S., Ribeiro, F. N., and Benevenuto, F. (2020b). Characterizing toxicity on facebook comments in brazil. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 253–260.
Haynes, B. (2018). Facebook retira do ar rede ligada ao mbl antes das eleições. https://br.reuters.com/article/idBRKBN1KF1MI-OBRDN. Accessed September 30th, 2020.
Hille, S. and Bakker, P. (2014). Engaging the social news user: Comments on news sites and facebook. Journalism Practice, 8(5):563–572.
Holt, K. (2018). Alternative media and the notion of anti-systemness: Towards an analytical framework. Media and Communication, 6(4):49–57.
Holt, K., Ustad Figenschou, T., and Frischlich, L. (2019). Key dimensions of alternative news media. Digital Journalism, 7(7):860–869.
Jigsaw (2017). Perspective api. https://www.perspectiveapi.com/. Accessed May 24th, 2019.
Joachims, T. (2003). Transductive learning via spectral graph partitioning. In Proceedings of the 20th International Conference on Machine Learning, pages 290–297.
Kelkar, S. (2019). Post-truth and the search for objectivity: political polarization and the remaking of knowledge production. Engaging Science, Technology, and Society, 5:86–106.
Khan, S. A. and Chang, H.-T. (2019). Comparative analysis on facebook post interaction using dnn, elm and lstm. PloS ONE, 14(11):1–26.
Kolhatkar, V. and Taboada, M. (2017). Constructive language in news comments. In Proceedings of the First Workshop on Abusive Language Online, pages 11–17.
Mitchell, A. (2014). Political polarization & media habits. Pew Research Center.
Moers, T., Krebs, F., and Spanakis, G. (2018). Semtec: social emotion mining techniques for analysis and prediction of facebook post reactions. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence, pages 361–382.
Moretto, M. and Ortellado, P. (2018). Quanto mais velhos, mais polarizados: Perfil dos usuários que interagem com páginas de notícias no facebook. Technical Report 1, Monitor do Debate Político no Meio Digital.
Mullainathan, S. and Shleifer, A. (2002). Media bias. Technical report, National Bureau of Economic Research.
Newman, N. (2011). Mainstream media and the distribution of news in the age of social media. RISJ Report 2011, Reuters Institute for the Study of Journalism.
Newman, N., Fletcher, R., Kalogeropoulos, A., and Nielsen, R. (2019). Reuters Institute digital news report 2019. Reuters Institute for the Study of Journalism.
Reis, J., Benevenuto, F., Vaz de Melo, P., Prates, R., Kwak, H., and An, J. (2015). Breaking the news: First impressions matter on online news. In Proceedings of the 9th International AAAI Conference on Web-Blogs and Social Media, pages 357–366.
Ribeiro, F. N., Henrique, L., Benevenuto, F., Chakraborty, A., Kulshrestha, J., Babaei, M., and Gummadi, K. P. (2018). Media bias monitor: Quantifying biases of social media news outlets at large-scale. In Proceedings of the 12th International AAAI Conference on Web and Social Media, pages 290—-299.
Ross, B., Rist, M., Carbonell, G., Cabrera, B., Kurowsky, N., and Wojatzki, M. (2016). Measuring the reliability of hate speech annotations: The case of the european refugee crisis. In Bochumer linguistische Arbeitsberichte 17, 3rd Workshop on Natural Language Processing for Computer-Mediated Communication, pages 6–9.
Russell, A. (2011). The arab spring— extra-national information flows, social media and the 2011 egyptian uprising. International Journal of Communication, 5:10.
Shearer, E. and Matsa, K. E. (2018). News use across social media platforms 2018. https://www.journalism.org/2018/09/10/news-use-across-social-mediaplatforms-2018/. Accessed December 15th, 2019.
Shi, Y., Mast, K., Weber, I., Kellum, A., and Macy, M. (2017). Cultural fault lines and political polarization. In Proceedings of the 9th ACM on Web Science Conference, pages 213–217.
Stroud, N. J., Van Duyn, E., and Peacock, C. (2016). Survey of commenters and comment readers. https://mediaengagement.org/research/survey-of-commenters-and-commentreaders/. Accessed February 21th, 2020.
Su, L. Y.-F., Xenos, M. A., Rose, K. M., Wirz, C., Scheufele, D. A., and Brossard, D. (2018). Uncivil and personal? comparing patterns of incivility in comments on the facebook pages of news outlets. New Media & Society, 20(10):3678–3699.
Tian, Y., Galery, T., Dulcinati, G., Molimpakis, E., and Sun, C. (2017). Facebook sentiment: Reactions and emojis. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pages 11–16.
Weeks, B. E. and Holbert, R. L. (2013). Predicting dissemination of news content in social media: A focus on reception, friending, and partisanship. Journalism & Mass Communication Quarterly, 90(2):212–232.
Wells, C., Zhang, Y., Lukito, J., and Pevehouse, J. C. W. (2020). Modeling the formation of attentive publics in social media: The case of donald trump. Mass Communication and Society, 23(2):181–205.
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Sch¨olkopf, B. (2003). Learning with local and global consistency. In Proceedings of the 16th International Conference on Neural Information Processing Systems, pages 321–328.
Zhu, X., Ghahramani, Z., and Lafferty, J. D. (2003). Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning, pages 912–919.
Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348(6239):1130–1132.
Basile, A., Caselli, T., and Nissim, M. (2017). Predicting controversial news using facebook reactions. In Proceedings of the 4th Italian Conference on Computational Linguistics, pages 12–17.
Budak, C., Goel, S., and Rao, J. M. (2016). Fair and balanced? quantifying media bias through crowdsourced content analysis. Public Opinion Quarterly, 80(S1):250–271.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.
D’Alessio, D. and Allen, M. (2000). Media bias in presidential elections: A meta-analysis. Journal of communication, 50(4):133–156.
de Pelle, R. P. and Moreira, V. P. (2017). Offensive comments in the brazilian web: a dataset and baseline results. In Proceedings of the 6th Brazilian Workshop on Social Network Analysis and Mining, pages 510–519.
Druckman, J. N. and Parkin, M. (2005). The impact of media bias: How editorial slant affects voters. The Journal of Politics, 67(4):1030–1049.
Entman, R. M. (2007). Framing bias: Media in the distribution of power. Journal of communication, 57(1):163–173.
Floyd, R.W. (1962). Algorithm 97: shortest path. Communications of the ACM, 5(6):345.
Fortuna, P. and Nunes, S. (2018). A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4):85.
Guimarães, S. S., Reis, J. C. S., Lima, L., Ribeiro, F. N., Vasconcelos, M., An, J., Kwak, H., and Benevenuto, F. (2020a). Identifying and characterizing alternative news media on facebook. In Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 448–452.
Guimarães, S. S., Reis, J. C. S., Ribeiro, F. N., and Benevenuto, F. (2020b). Characterizing toxicity on facebook comments in brazil. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 253–260.
Haynes, B. (2018). Facebook retira do ar rede ligada ao mbl antes das eleições. https://br.reuters.com/article/idBRKBN1KF1MI-OBRDN. Accessed September 30th, 2020.
Hille, S. and Bakker, P. (2014). Engaging the social news user: Comments on news sites and facebook. Journalism Practice, 8(5):563–572.
Holt, K. (2018). Alternative media and the notion of anti-systemness: Towards an analytical framework. Media and Communication, 6(4):49–57.
Holt, K., Ustad Figenschou, T., and Frischlich, L. (2019). Key dimensions of alternative news media. Digital Journalism, 7(7):860–869.
Jigsaw (2017). Perspective api. https://www.perspectiveapi.com/. Accessed May 24th, 2019.
Joachims, T. (2003). Transductive learning via spectral graph partitioning. In Proceedings of the 20th International Conference on Machine Learning, pages 290–297.
Kelkar, S. (2019). Post-truth and the search for objectivity: political polarization and the remaking of knowledge production. Engaging Science, Technology, and Society, 5:86–106.
Khan, S. A. and Chang, H.-T. (2019). Comparative analysis on facebook post interaction using dnn, elm and lstm. PloS ONE, 14(11):1–26.
Kolhatkar, V. and Taboada, M. (2017). Constructive language in news comments. In Proceedings of the First Workshop on Abusive Language Online, pages 11–17.
Mitchell, A. (2014). Political polarization & media habits. Pew Research Center.
Moers, T., Krebs, F., and Spanakis, G. (2018). Semtec: social emotion mining techniques for analysis and prediction of facebook post reactions. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence, pages 361–382.
Moretto, M. and Ortellado, P. (2018). Quanto mais velhos, mais polarizados: Perfil dos usuários que interagem com páginas de notícias no facebook. Technical Report 1, Monitor do Debate Político no Meio Digital.
Mullainathan, S. and Shleifer, A. (2002). Media bias. Technical report, National Bureau of Economic Research.
Newman, N. (2011). Mainstream media and the distribution of news in the age of social media. RISJ Report 2011, Reuters Institute for the Study of Journalism.
Newman, N., Fletcher, R., Kalogeropoulos, A., and Nielsen, R. (2019). Reuters Institute digital news report 2019. Reuters Institute for the Study of Journalism.
Reis, J., Benevenuto, F., Vaz de Melo, P., Prates, R., Kwak, H., and An, J. (2015). Breaking the news: First impressions matter on online news. In Proceedings of the 9th International AAAI Conference on Web-Blogs and Social Media, pages 357–366.
Ribeiro, F. N., Henrique, L., Benevenuto, F., Chakraborty, A., Kulshrestha, J., Babaei, M., and Gummadi, K. P. (2018). Media bias monitor: Quantifying biases of social media news outlets at large-scale. In Proceedings of the 12th International AAAI Conference on Web and Social Media, pages 290—-299.
Ross, B., Rist, M., Carbonell, G., Cabrera, B., Kurowsky, N., and Wojatzki, M. (2016). Measuring the reliability of hate speech annotations: The case of the european refugee crisis. In Bochumer linguistische Arbeitsberichte 17, 3rd Workshop on Natural Language Processing for Computer-Mediated Communication, pages 6–9.
Russell, A. (2011). The arab spring— extra-national information flows, social media and the 2011 egyptian uprising. International Journal of Communication, 5:10.
Shearer, E. and Matsa, K. E. (2018). News use across social media platforms 2018. https://www.journalism.org/2018/09/10/news-use-across-social-mediaplatforms-2018/. Accessed December 15th, 2019.
Shi, Y., Mast, K., Weber, I., Kellum, A., and Macy, M. (2017). Cultural fault lines and political polarization. In Proceedings of the 9th ACM on Web Science Conference, pages 213–217.
Stroud, N. J., Van Duyn, E., and Peacock, C. (2016). Survey of commenters and comment readers. https://mediaengagement.org/research/survey-of-commenters-and-commentreaders/. Accessed February 21th, 2020.
Su, L. Y.-F., Xenos, M. A., Rose, K. M., Wirz, C., Scheufele, D. A., and Brossard, D. (2018). Uncivil and personal? comparing patterns of incivility in comments on the facebook pages of news outlets. New Media & Society, 20(10):3678–3699.
Tian, Y., Galery, T., Dulcinati, G., Molimpakis, E., and Sun, C. (2017). Facebook sentiment: Reactions and emojis. In Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pages 11–16.
Weeks, B. E. and Holbert, R. L. (2013). Predicting dissemination of news content in social media: A focus on reception, friending, and partisanship. Journalism & Mass Communication Quarterly, 90(2):212–232.
Wells, C., Zhang, Y., Lukito, J., and Pevehouse, J. C. W. (2020). Modeling the formation of attentive publics in social media: The case of donald trump. Mass Communication and Society, 23(2):181–205.
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Sch¨olkopf, B. (2003). Learning with local and global consistency. In Proceedings of the 16th International Conference on Neural Information Processing Systems, pages 321–328.
Zhu, X., Ghahramani, Z., and Lafferty, J. D. (2003). Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning, pages 912–919.
Publicado
07/06/2021
Como Citar
GUIMARÃES, Samuel; BENEVENUTO, Fabrício.
Characterizing Reactions and Comments Associated with News on Facebook. In: CONCURSO DE TESES E DISSERTAÇÕES EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 17. , 2021, On-line.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 157-171.
DOI: https://doi.org/10.5753/sbsi.2021.15370.
