Emojis are a quickly spreading and rather unknown communication phenomenon which occasionally receives attention in the mainstream press, but lacks the scientific exploration it deserves. This paper is a first attempt at investigating the global distribution of emojis. We perform our analysis of the spatial distribution of emojis on a dataset of ∼17 million (and growing) geo-encoded tweets containing emojis by running a cluster analysis over countries represented as emoji distributions and performing correlation analysis of emoji distributions and World Development Indicators. We show that emoji usage tends to draw quite a realistic picture of the living conditions in various parts of our world.

In this paper we present the legal framework, dataset and annotation schema of socially unacceptable discourse practices on social networking platforms in Slovenia. On this basis we aim to train an automatic identification and classification system with which we wish contribute towards an improved methodology, understanding and treatment of such practices in the contemporary, increasingly multicultural information society.

0000−0001−7169−9152] , Darja Fišer 2,1[0000−0002−9956−1689] , and Tomaž Erjavec 1[0000−0002−1560−4099]Abstract. In this paper we present datasets of Facebook comment threads to mainstream media posts in Slovene and English developed inside the Slovene national project FRENK 3 which cover two topics, migrants and LGBT, and are manually annotated for different types of socially unacceptable discourse (SUD). The main advantages of these datasets compared to the existing ones are identical sampling procedures, producing comparable data across languages and an annotation schema that takes into account six types of SUD and five targets at which SUD is directed. We describe the sampling and annotation procedures, and analyze the annotation distributions and inter-annotator agreements. We consider this dataset to be an important milestone in understanding and combating SUD for both languages.

This paper presents two large newly constructed datasets of moderated news comments from two highly popular online news portals in the respective countries: the Slovene RTV MCC and the Croatian 24sata. The datasets are analyzed by performing manual annotation of the types of the content which have been deleted by moderators and by investigating deletion trends among users and threads. Next, initial experiments on automatically detecting the deleted content in the datasets are presented. Both datasets are published in encrypted form, to enable others to perform experiments on detecting content to be deleted without revealing potentially inappropriate content. Finally, the baseline classification models trained on the non-encrypted datasets are disseminated as well to enable real-world use.

The notions of concreteness and imageability, traditionally important in psycholinguistics, are gaining significance in semantic-oriented natural language processing tasks. In this paper we investigate the predictability of these two concepts via supervised learning, using word embeddings as explanatory variables. We perform predictions both within and across languages by exploiting collections of cross-lingual embeddings aligned to a single vector space. We show that the notions of concreteness and imageability are highly predictable both within and across languages, with a moderate loss of up to 20% in correlation when predicting across languages. We further show that the crosslingual transfer via word embeddings is more efficient than the simple transfer via bilingual dictionaries.