Introductory texts on big data analysis of political communication

• Nickerson, D. W., & Rogers, T. (2014). Political campaigns and big data. Journal of Economic Perspectives, 28(2), 51-74.
• Callegaro, M., & Yang, Y. (2018). The Role of Surveys in the Era of “Big Data”. In The Palgrave Handbook of Survey Research (pp. 175-192). Palgrave Macmillan, Cham.
• Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.
• Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. Grimmer, J. (2015). We are all social scientists now: how big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80-83.
• Lyon, D. (2014). Surveillance, Snowden, and big data: Capacities, consequences, critique. Big Data & Society, 1(2).
• Russell Neuman, W., Guggenheim, L., Mo Jang, S., & Bae, S. Y. (2014). The dynamics of public attention: Agenda‐setting theory meets big data. Journal of Communication, 64(2), 193-214.
• Tufekci, Z. (2014). Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls. ICWSM, 14, 505-514.
• Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7).
• Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197.

Social Network Analysis in political communication and political action

• Recuero, R., Zago, G., & Soares, F. (2019). Using social network analysis and social capital to identify user roles on polarized political conversations on Twitter. Social media+ Society, 5(2), 2056305119848745.
• Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317-332.
• Boutyline, A., & Willer, R. (2017). The social structure of political echo chambers: Variation in ideological homophily in online networks. Political Psychology, 38(3), 551-569.
• Lazer, D. (2011). Networks in political science: Back to the future. PS: Political Science & Politics, 44(1), 61-68.
• Perliger, A., & Pedahzur, A. (2011). Social network analysis in the study of terrorism and political violence. PS: Political Science & Politics, 44(1), 45-50.

Semantic Network Analysis on political texts

Εισαγωγικά

• Leifeld, P. (2017). Discourse Network Analysis: Policy Debates as Dynamic Networks. In Jennifer N. Victor, Mark N. Lubell and Alexander H. Montgomery (eds), The Oxford Handbook of Political Networks. Oxford: Oxford University Press.
• Yang, S., & González-Bailón, S. (2017). Semantic networks and applications in public opinion research. The Oxford handbook of political networks, 327-353.
• Paranyushkin, D. (2019). Identifying the pathways for meaning circulation using text network analysis. In WWW '19: The World Wide Web Conference (pp. 3584–3589). https://doi.org/10.1145/3308558.3314123
• Xiong, Y., Cho, M., & Boatwright, B. (2019). Hashtag activism and message frames among social movement organizations: Semantic network analysis and thematic analysis of Twitter during the# MeToo movement. Public relations review, 45(1), 10-23. https://doi.org/10.1016/j.pubrev.2018.10.014

Εφαρμογές

• Shim, J., Park, C., & Wilding, M. (2015). Identifying policy frames through semantic network analysis: an examination of nuclear energy policy across six countries. Policy Sciences, 48(1), 51-83.
• Eddington, S. M. (2018). The communicative constitution of hate organizations online: A semantic network analysis of “Make America great again”. Social Media+ Society, 4(3), 2056305118790763.
• Kwon, K. H., Bang, C. C., Egnoto, M., & Raghav Rao, H. (2016). Social media rumors as improvised public opinion: semantic network analyses of twitter discourses during Korean saber rattling 2013. Asian Journal of Communication, 26(3), 201-222.
• Jiang, K., Barnett, G. A., & Taylor, L. D. (2016). Dynamics of culture frames in international news coverage: A semantic network analysis. International Journal of Communication, 10, 27.
• Guo, 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.

• Xiong, Y., Cho, M., & Boatwright, B. (2019). Hashtag activism and message frames among social movement organizations: Semantic network analysis and thematic analysis of Twitter during the# MeToo movement. Public Relations Review, 45(1), 10-23.

Text analysis in political communication

• Grimmer, J., and Stewart, Β.Μ. (2013). Text as Data: the Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21(3): 267–97.
• Denny, M. J., & Spirling, A. (2018). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political Analysis, 26(2), 168-189.
• Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008). Fightin'words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis, 16(4), 372-403.
• Wilkerson, J., & Casas, A. (2017). Large-scale computerized text analysis in political science: Opportunities and challenges. Annual Review of Political Science, 20, 529-544.
• Rudkowsky, E., Haselmayer, M., Wastian, M., Jenny, M., Emrich, Š., & Sedlmair, M. (2018). More than bags of words: Sentiment analysis with word embeddings. Communication Methods and Measures, 12(2-3), 140-157.
• Barberá, P., Casas, A., Nagler, J., Egan, P. J., Bonneau, R., Jost, J. T., & Tucker, J. A. (2019). Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data. American Political Science Review, 113(4), 883-901.
• Rodman, E. (2020). A Timely Intervention: Tracking the Changing Meanings of Political Concepts with Word Vectors. Political Analysis, 28(1), 87-111.
• Benoit, K. (2019). Text as data: An overview. In SAGE Handbook of Research Methods in Political Science and International Relations, ed. Luigi Curini and Robert Franzese. London: SAGE Publishing.
• Guo, L., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling. Journalism & Mass Communication Quarterly, 93(2), 332-359.
• Terman, R. (2017). Islamophobia and media portrayals of Muslim women: A computational text analysis of US news coverage. International Studies Quarterly, 61(3), 489-502.
• Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Emotion measurement (pp. 201-237). Woodhead Publishing.
• Yadollahi, A., Shahraki, A. G., & Zaiane, O. R. (2017). Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys (CSUR), 50(2), 1-33.

Machine learning approaches in the analysis of political communication

• Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1.

• Domingos, P. 2012. A Few Useful Things to Know about Machine Learning. Communications ACM 55(10): 78-87.
• Phillips, L., Dowling, C., Shaffer, K., Hodas, N., & Volkova, S. (2017). Using social media to predict the future: a systematic literature review. arXiv preprint arXiv:1706.06134.
• Nguyen, V. A. (2015). Guided probabilistic topic models for agenda-setting and framing. Doctoral dissertation. University of Maryland.
• Odijk, D., Burscher, B., Vliegenthart, R., & De Rijke, M. (2013, November). Automatic thematic content analysis: Finding frames in news. In International Conference on Social Informatics (pp. 333-345). Springer, Cham.
• Pessach, D., & Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Surveys (CSUR), 55(3), 1-44.
• Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1-35. https://arxiv.org/pdf/1908.09635.pdf

Hate speech detection

• Gitari, N. D., Zuping, Z., Damien, H., & Long, J. (2015). A lexicon-based approach for hate speech detection. International Journal of Multimedia and Ubiquitous Engineering, 10(4), 215-230.
• MONDAL, Mainack; SILVA, Leandro Araújo; BENEVENUTO, Fabrício. A Measurement Study of Hate Speech in Social Media. In: Proceedings of the 28th ACM Conference on Hypertext and Social Media. ACM, 2017. p. 85-94.
• Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. arXiv preprint arXiv:1703.04009.