Machine learning, artificial neural networks and social research
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Machine learning, artificial neural networks and social research Giovanni Di Franco1 · Michele Santurro1 Accepted: 29 August 2020 © The Author(s) 2020
Abstract Machine learning (ML), and particularly algorithms based on artificial neural networks (ANNs), constitute a field of research lying at the intersection of different disciplines such as mathematics, statistics, computer science and neuroscience. This approach is characterized by the use of algorithms to extract knowledge from large and heterogeneous data sets. In addition to offering a brief introduction to ANN algorithms-based ML, in this paper we will focus our attention on its possible applications in the social sciences and, in particular, on its potential in the data analysis procedures. In this regard, we will provide three examples of applications on sociological data to assess the impact of ML in the study of relationships between variables. Finally, we will compare the potential of ML with traditional data analysis models. Keywords Machine learning · Deep learning Artificial neural network · Supervised learning · Linear models · Nonlinear models
1 Introduction ML is an automatic learning process that takes place through the processing of usually very large data sets. The procedures of the past, defined with the “symbolic artificial intelligence” label, operated on algorithms constituted by a logical set of instructions by which a given output (usually called target) was encoded for all possible inputs. Contrarily, the new ML systems “learn” directly from data and estimate mathematical functions that discover representations of some input, or learn to link one or more inputs to one or more outputs to be able to formulate predictions on new data (Jordan and Mitchell 2015).
The paper is the result of the collaboration of the two authors. The drawing up of the text is attributed as follows: Sects. 3 and 4 to Giovanni Di Franco; Sects. 1, 2 and 5 to Michele Santurro. * Giovanni Di Franco [email protected] Michele Santurro [email protected] 1
Department of Social and Economic Sciences, Sapienza University of Rome, Rome, Italy
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In recent years in various human sciences: economics (Varian 2014; Blumenstock et al. 2015; Athey and Imbens 2017; Mullainathan and Spiess 2017), political science (Baldassarri and Goldberg 2014; Bonikowski and DiMaggio 2016), sociology (Barocas and Selbst 2016; Evans and Aceves 2016; Baldassarri and Abascal 2017), communication science (Hopkins and King 2010; Grimmer and Stewart 2013; Bail 2014), etc., ML has started to be applied both in academic research and in areas related to the management of services provided by the public administration (Athey 2017; Berk et al. 2018) or by private companies. Overall, many different approaches and tools are included under the ML label (Kleinberg et al. 2015). Here we will only consider ANNs that use supervised ML algorithms. In the supervised ML the algorithm observes an output for each input. This output giv
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