Abstract
This document discusses the potential role of Machine Learning (ML) methods in social science research, in general, and specifically in studies of political behavior of users in social networks (SN). This paper explores challenges which occurred in a set of studies which we conducted regarding classification of comments to posts of politicians and suggests ways of addressing these challenges. These challenges apply to a larger set of online political behavior studies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boyd, D., Crawford, K.: Critical questions for big data. Inf. Commun. Soc. 15, 662–679 (2012)
Dalton, C.M., Taylor, L., Thatcher (alphabetical), J.: Critical data studies: a dialog on data and space. Big Data Soc. 3, (2016). https://doi.org/10.1177/2053951716648346
Nahon, K.: Where there is social media there is politics. In: Bruns, A., Enli, G., Skogerbo, E., Larsson, A.O., Christensen, C. (eds.) The Routledge Companion to Social Media and Politics. Routledge, New York (2016)
Mitchell, T.M.: The discipline of machine learning. Machine Learning Department, School of Computer Science, Carnegie Mellon University (2006)
Shah, D.V., Cappella, J.N., Neuman, W.R., Burscher, B., Vliegenthart, R., De Vreese, C.H.: Using supervised machine learning to code policy issues: can classifiers generalize across contexts? Ann. Am. Acad. Pol. Soc. Sci. 659, 122–131 (2015)
Hopkins, D.J., King, G.: A method of automated nonparametric content analysis for social science. Am. J. Polit. Sci. 54, 229–247 (2010)
Grimmer, J., Stewart, B.M.: Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Polit. Anal. 21, 267–297 (2013)
Stewart, B.M., Zhukov, Y.M.: Use of force and civil–military relations in Russia: an automated content analysis. Small Wars Insur. 20, 319–343 (2009)
Liebeskind, C., Nahon, K., Hacohen-Kerner, Y., Manor, Y.: Comparing sentiment analysis models to classify attitudes of political comments on Facebook. Polibits Res. J. Comput. Sci. Comput. Eng. Appl. (2017)
Driscoll, K., Walker, S.: Big data, big questions| working within a black box: transparency in the collection and production of big Twitter data. Int. J. Commun. 8, 20 (2014)
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)
Liebeskind, C., Liebeskind, S., HaCohen-Kerner, Y.: Comment relevance classification in Facebook. In: CICLING 2016, the Eighteen International Conference on Computational Linguistics and Intelligent Text Processing, Budapest, Hungary (2017)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11, 10–18 (2009)
Hall, M.A.: Correlation-based Feature Selection for Machine Learning (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Liebeskind, C., Nahon, K. (2018). Challenges in Applying Machine Learning Methods: Studying Political Interactions on Social Networks. In: Szymański, J., Velegrakis, Y. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2017. Lecture Notes in Computer Science(), vol 10546. Springer, Cham. https://doi.org/10.1007/978-3-319-74497-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-74497-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74496-4
Online ISBN: 978-3-319-74497-1
eBook Packages: Computer ScienceComputer Science (R0)