Heattweet: Exploring the link between weather and aggression on social media

Recently, meteorological conditions (e.g., temperature) have been linked to expressed sentiment on social media (Baylis et al., 2018). In this project we focus on the influence of meteorological conditions on expressions of interpersonal and intergroup aggression in social media messages, and on a possible explanatory mechanism, i.e. strength of future orientation. Given the importance of social media in interpersonal and intergroup communication nowadays, expressions of aggression in social media messages may threaten societies’ interconnectedness and inclusiveness.
According to the model for CLimate, Aggression, and Self-control (CLASH; Van Lange, Rinderu, & Bushman, 2017), higher temperatures may increase aggression because they result in a weaker future orientation, which is linked to lower levels of self-control (e.g., Baumeister et al., 1994). However, some psychological experiments suggest that higher temperatures may actually inhibit aggression and promote prosocial behavior by enhancing relational mindsets (e.g., IJzerman & Semin, 2009) and affiliative motivation (e.g., Fay & Maner, 2012). To make things even more complicated, other resarch suggests a curvilinear relationship between temperature and aggression (Van de Vliert et al., 1999).
In the current project, we will explore the link between the daily temperature and other meteorological conditions in the Netherlands (data obtained from KNMI), and expressions of interpersonal and intergroup aggression extracted from social media data (provided by Coosto). Proxies for aggression include terms of abuse (i.e., swear words), and words specifically related to, e.g., racist discourse (e.g., Tulkens, 2016), hate speech, and cyberbullying (e.g., Del Vigna et al., 2017). In addition to existing word lists, dictionaries will be composed semi-automatically, using wordnet propagation, corpus comparison, and pattern extraction (Baccianella et al., 2010, Maks et al., 2014). Degree of future orientation will be assessed by detecting use of temporal references (e.g.,
tomorrow, next week; see Basic et al., 2018), and subsequently tested as explanatory mechanism.

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