Predictive modeling to study lifestyle politics with Facebook likes

Abstract

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.

Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records

“…These digital footprints are increasingly available for academics, businesses and governments [1] and have been shown to provide highly intimate insights into people's lives as well as the ways in which they think, feel and behave. For example, digital footprints can be used to predict personality traits [2,3], mental health [4], sexual and political orientation [2,5] or intelligence [2]. The process of translating digital footprints into meaningful psychological profiles with the help of machine learning has been termed 'psychological profiling', and drives applications in a variety of areas ranging from marketing to employment to mental health (see Figure 1 for a conceptual overview).…”

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“…3 indicate an increase in positive reactions and the decline in negative comments within the period of a year. Founded on various studies, [10,11] this may be attributed to a blunting-or desensitization effect. More realistically, it may be attributed to a more general concern for physical and mental well-being [24], and therefore an increased awareness of healthy ways of living.…”

“…A pivotal note, is that the amount of "Likes at Posting" (and even "Total Positive") is consistently higher than the amount of "Shares" and one would assume that if someone "liked" a post, they would want to disseminate that information. Keeping in mind instances of "echo-chambers" [11] and "emotional blunting [10], Fig. 2 indicates Facebook reactions responding to the peaks and valleys of the infection rates, except in one instance in Aug 2021.…”

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