No abstract

Mainstream media sources have recently heightened public awareness to a phenomenon known as Russian troll farms. This research thematically analyzes “Kremlin troll” use and its variations found in user comments on a leading Lithuanian news portal. The main findings of this study indicate that “Kremlin troll” was used in two oppositional themes. The first one reveals accusations of paid commentators as “Kremlin trolls.” The second, in contrast, counter-argues “Kremlin troll” accusations through rebuttal. Sarcasm and humor, e.g., by emergence of self-identification as a “Kremlin troll” furthermore downplays the “Kremlin troll” accusations and reclaims uncertainty of who is the real troll.Even if the offensive and defensive tactics might seem rather similar to overall Internet troll tactics found in the previous online research, the unique side of “Kremlin troll” use was the emergence of ideological trolling, charged with accusations of some commentators being paid by a foreign government, thus referring to “Kremlin trolling” as a form of astroturfing. We conclude that “Kremlin troll” in this study exemplifies politically charged ideological trolling, rather than the mere subcultural phenomenon that is prevalent in English-language contexts.

Networks are widely used to model a variety of complex, often multi-disciplinary, systems in which the relationships between their sub-parts play a significant role. In particular, there is extensive research on the topological properties associated with their structure as this allows the analysis of the overall behaviour of such networks. However, extracting networks from structured and unstructured data sets raises several challenges, including addressing any inconsistency present in the data, as well as the difficulty in investigating their properties especially when the topological structure is not fully determined or not explicitly defined. In this paper, we propose a novel method to address the automated identification, assessment and ranking of the most likely structure associated with networks extracted from a variety of data sets. More specifically, our approach allows to mine data to assess whether their associated networks exhibit properties comparable to well-known structures, namely scalefree, small world and random networks. The main motivation is to provide a toolbox to classify and analyse real-world networks otherwise difficult to fully assess due to their potential lack of structure. This can be used to investigate their dynamical and statistical behaviour which would potentially lead to a better understanding and prediction of the properties of the system(s) they model. Our initial validation shows the potential of our method providing relevant and accurate results.

This visionary paper presents the Internet of Things paradigm in terms of interdependent dynamic dimensions of objects and their properties. Given that in its current state Internet of Things (IoT) has been viewed as a paradigm based on hierarchical distribution of objects, evaluation of the dynamic nature of the hierarchical structures faces challenges in its evaluation and analysis. Within this in mind, our focus is on the area of complex social networks and the dynamic social network construction within the context of IoT. This is by highlighting and addressing the tagging issues of the objects to the real-world domain such as in disaster management; these are in relation to their hierarchies and interrelation within the context of social network analysis. Specifically, we suggest to investigate and deepen the understanding of the IoT paradigm through the application of social network analysis as a method for interlinking objectsand thus, propose ways in which IoT could be subsequently interlinked and analyzed through social network analysis approach -which provides possibilities for linking of the objects, while extends it into real-world domain. With this in mind, we present few applications and key characteristics of disaster management and the social networking analysis approach, as well as, foreseen benefits of its application in the IoT domain.

In this content analysis, we examined violence in Web‐based entertainment. YouTube videos (N = 2,520) were collected in 3 different categories: most viewed, top rated, and random, with additional comparisons between amateur and professional content. Frequencies of violent acts and the context of violence (e.g., characteristics of perpetrator and victim, justification, consequences) were compared both between these categories of YouTube videos and with existing research on television violence. The results showed far less violence as a percentage of programming on YouTube than there is on television. Moreover, the violence that was present showed more realistic consequences and more negative context than television violence. Post hoc comparisons illustrated several differences in the presentation of violence between make and category of video.