Modeling, analysis and synthesis of behaviour are the subject of major efforts in computing science, especially when it comes to technologies that make sense of humanhuman and human-machine interactions. This article outlines some of the most important issues that still need to be addressed to ensure substantial progress in the field, namely 1) development and adoption of virtuous data collection and sharing practices, 2) shift of the focus of interest from individuals to dyads and groups, 3) endowment of artificial agents with internal representations of users and context, 4) modeling of cognitive and semantic processes underlying social behaviour, and 5) identification of application domains and strategies for moving from laboratory to the realworld products.

Gas chromatographic-mass spectrometric analyses of whole body extracts of Oribotritia berlesei, a largesized soil-dwelling oribatid mite, revealed a consistent chemical pattern of ten components, probably originating from the well-developed opisthonotal glands. The three major components of the extract were the iridoid monoterpene, (3S,8S)-chrysomelidial (about 45% of the extract), the unsaturated hydrocarbon 6,9-heptadecadiene, and the diterpene β-springene (the latter two, each about 20-25% of the extract). The remaining minor components (together about 10% of the extract) included a series of hydrocarbons (tridecene, tridecane, pentadecene, pentadecane, 8-heptadecene, and heptadecane) and the tentatively identified 9,17-octadecadienal. In contrast, analysis of juveniles showed only two compounds, namely a 2:1 mixture of (3S,8S)-chrysomelidial and its epimer, epi-chrysomelidial (3S,8R-chrysomelidial). Unexpectedly, neither adult nor juvenile secretions contained the so-called astigmatid compounds, which are considered characteristic of secretions of oribatids above moderately derived Mixonomata. The chrysomelidials, as well as β-springene and octadecadienal, are newly identified compounds in the opisthonotal glands of oribatid mites and have chemotaxonomic potential for this group. This is the first instance of finding chrysomelidials outside the Coleoptera.

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Anticipating repliers in online conversations is a fundamental challenge for computer mediated communication systems which aim to make textual, audio and/or video communication as natural as face to face communication. The massive amounts of data that social media generates has facilitated the study of online conversations on a scale unimaginable a few years ago. In this work we use data from Twitter to explore the predictability of repliers, and investigate the factors which influence who will reply to a message. Our results suggest that social factors, which describe the strength of relations between users, are more useful than topical factors. This indicates that Twitter users' reply behavior is more impacted by social relations than by topics. Finally, we show that a binary classification model, which differentiates between users who will and users who will not reply to a certain message, may achieve an F1-score of 0.74 when using social features.