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This article presents a transnational study of the classification and evaluation of social media content. We conducted a large-scale survey (N = 4770) in five countries (Germany, Italy, Japan, South Korea, and the United States) with open-ended questions about the types of content people like and dislike. Through iterative and inductive coding, we identified 29 topics, or broad areas of interest, and 213 recurrent genres, or narrower categories that share elements of form and content. We compared the results according to country, gender, age, and education level, identifying patterns of cultural difference and commonality. While we found significant differences in the prominence and preferentiality of content, these distictions were less pronounced for disliked topics around which social media users tended to converge. Finally, we discuss genre imaginaries as normative maps that reflect ideas about morality in general and the purpose of social media in particular.

This paper introduces a systematic way of analyzing the semantics of causative linguistic expressions, and of how causal relations are expressed in natural languages. The starting point for this broad agenda is to provide an explanation for the asymmetrical inferential relationship between two causative constructions: change-of-state (CoS) verbs and the verb cause, commonly ascribed to the former having an additional prerequisite of direct causation. The direct causation hypothesis, however, is fraught with empirical and theoretical challenges. At the theoretical level, capturing the felicity conditions specific to CoS verbs and the notion of direct causation requires a means of modelling complex causal structures. This is on no account a trivial task, as it necessitates, inter alia, modelling causation in a way that is germane to the linguistic expressions designating such relations. Hence, the main objective of this paper is to develop a framework for modelling the  semantics of causal statements. For this purpose, this paper makes use of the framework
of Structural Equation Modelling (SEM), and it demonstrates how this approach provides tools for a rigorous model-theoretic treatment of the differential semantics of causal  expressions. This paper introduces formal logical definitions of different types of conditions  using SEM networks, and show how this proposal and the formal tools it employs allow us to make sense of the asymmetric entailment relationship between the two constructions. In our proposal, CoS verbs do not require contiguity between cause and effect at all, but instead they require that its subject is set by default to a participant in completion event, the event which “completes” a sufficient set of conditions, such that following this event (but not before) the values of the set of conditions in the sufficient set entail that the effect occurs. According to this, the intuition of direct causation arises (epiphenomenally) from contrasting CoS verbs with overt cause sentences: the stronger selection pattern of the former - which requires a completion event - may exclude more temporally distant conditions, while the latter admits any necessary condition. 

Fluorescence emitted by light-harvesting pigments responds to the physiological state of phytoplankton. We developed a time resolved system to monitor fluorescence from single cells. It captures multiple spectral channels and fluorescence lifetimes, eliminating ensemble averaging of bulk experiments. Tracking the diurnal cycles of three phytoplankton species, we uncover each species segregation into multiple distinct cell states, a feature previously hidden in bulk measurements. We also tested their response to light-intensity perturbations, and determined each species unique acclimation strategy involving transition between distinct cell subpopulations. This approach will be useful for characterizing natural phytoplankton population responses to environmental conditions, aiming for a better understanding of their acclimation strategies and the effects of global climate changes.