The Potential of Big Data for the Cross-National Study of Political Behavior

Dalton, Russell J.

doi:10.1080/00207659.2016.1130410

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…The representative opinion survey has long been the pinnacle of empirical research in political science [ 1 , 2 ]. The recent immense growth in digital platforms has provided researchers with the possibility of studying human behaviour on a whole new scale from traces left behind by our digital interactions [ 3 ]. From being limited to surveys with a couple of thousand respondents, political studies covering millions of people have emerged within the field of computational social science, generating important new knowledge about our digital and analogue lives.…”

Section: Introductionmentioning

confidence: 99%

Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems

et al. 2017

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This study shows how liking politicians’ public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60–70%).The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems.

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Section: Introductionmentioning

confidence: 99%

Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems

et al. 2017

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“…(1) Big data analytics on social media has been applied in developing countries' political arena to enhance campaign strategies. For example, the Obama campaign put up a model of classifying individual voters that led their campaign strategy to an efficacious result [126] . However, it is not assured that these models can be pass-on to elections and other political activities and behaviour in underdeveloped and developing countries globally.…”

Section: Prospects For Future Studiesmentioning

confidence: 99%

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

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et al. 2022

Big Data Min. Anal.

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The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA.The related applications fields, challenges, and most importantly the openings for future research, are detailed.

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“…Of course, we must keep in mind that online data is a proxy for the real behavior under study, and thus, drawing causal inference and generalizing results from this type of data must be performed with caution [38,39]. For example, what you like on Facebook is not necessarily what you like in real-life and thus true behavior can only be studied indirectly [39]. More specifically, two potential hidden biases need to be considered.…”

Section: Predictive Modeling With Facebook Likesmentioning

confidence: 99%

“…[42]. 4 Yet, because of the enormous amount of information available in behavioral data, there is much to learn from it when following a rigorous research approach [38,39].…”

Section: Predictive Modeling With Facebook Likesmentioning

confidence: 99%

Predictive modeling to study lifestyle politics with Facebook likes

et al. 2021

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Abstract“Lifestyle politics” suggests that political and ideological opinions are strongly connected to our consumption choices, music and food taste, cultural preferences, and other aspects of our daily lives. With the growing political polarization this idea has become all the more relevant to a wide range of social scientists. Empirical research in this domain, however, is confronted with an impractical challenge; this type of detailed information on people’s lifestyle is very difficult to operationalize, and extremely time consuming and costly to query in a survey. A potential valuable alternative data source to capture these values and lifestyle choices is social media data. In this study, we explore the value of Facebook “like” data to complement traditional survey data to study lifestyle politics. We collect a unique dataset of Facebook likes and survey data of more than 6500 participants in Belgium, a fragmented multi-party system. Based on both types of data, we infer the political and ideological preference of our respondents. The results indicate that non-political Facebook likes are indicative of political preference and are useful to describe voters in terms of common interests, cultural preferences, and lifestyle features. This shows that social media data can be a valuable complement to traditional survey data to study lifestyle politics.

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The Potential of Big Data for the Cross-National Study of Political Behavior

Cited by 7 publications

References 21 publications

Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems

Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Predictive modeling to study lifestyle politics with Facebook likes

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