Abstract: The role of committed minorities in shaping public opinion has been recently addressed with the help of multi-agent models. However, previous studies focused on homogeneous populations where zealots stand out only for their stubbornness. Here, we consider the more general case in which individuals are characterized by different propensities to communicate. In particular, we correlate commitment with a higher tendency to push an opinion, acknowledging the fact that individuals with unwavering dedication to a cause are also more active in their attempts to promote their message. We show that these activists are not only more efficient in spreading their message but that their efforts require an order of magnitude fewer individuals than a randomly selected committed minority to bring the population over to a new consensus. Finally, we address the role of communities, showing that partisan divisions in the society can make it harder for committed individuals to flip the status-quo social consensus.

Abstract: The utterance selection model (USM) for language change (Baxter et al. 2006) is a stochastic agent-based model developed to simulation language change. In this model, agents are vertices of a graph and interact along its edges by stochastically producing utterances and learning from them. The dynamics of such an agent-based model is defined at the agent level and it is usually difficult to deduce the average dynamics of the complete population. In the original paper, the authors derived a continuous time limit in the form of a Fokker-Planck equation. This limit is only valid for a restricted set of parameters. In this talk, I will derive a new continuous time limit of the USM, which has no constraints on parameters and use it to derive a coarse-grained population level approximation of the dynamics. This approximation is a stochastic version of the heterogeneous mean field approximation. Using this approximation, I will characterize the dynamics of the USM at the population level. In particular, I will show that the population dynamics of the system can mainly be captured by three parameters. The analysis also reveals a finite-size effect in the dynamics.

Abstract: Facial emotions are believed to be very expressive social stimuli; hence significant amount of efforts have been made within the past two decades to detect, study and analyze these emotions in a way to reveal specific human behaviors and characteristics, in this paper we aim to decode some of the detected facial emotions on political leaders in a way to find potential correlations between their facial expressions and impacts on voter preference decisions in the United States, we study facial emotions detected on images of selected Republican and Democratic presidential candidates during their most controversial campaign speeches and debates for the 2016 United States presidential election. Our facial detection application is based on the Face API recently developed by Microsoft Cognitive Services, which is designed to recognize eight universal groups of facial emotions including sadness, neutral, contempt, disgust, anger, surprise, fear and happiness. 180 images were collected and analyzed for Donald Trump, John Kasich and Ted Cruz; Republican party nominees, also Hillary Clinton and Bernie Sanders for the Democratic party nominees, the results of the detected groups of facial emotions are scored forming an eight-dimensional vector and then re-scaled to two-dimensional vectors using Principal Component Analysis technique, the findings are discussed in the context of direct correlation between some facial expressions and voters decisions for the primary presidential election taking place since February first 2016.

Abstract: A combined approach to identify emotionally colored texts, which reflect the excited state of its authors and also make the sentiment analysis of these texts is proposed. This approach is based on one side on use of psycholinguistic markers that are calculated on the basis of the morphological characteristics of the text and on other side on use of object oriented sentiment analyser based on SVM classification. A complex indicator reflecting emotiveness of texts on the basis of the core group of markers was presented. On an example of two thematic collections it was shown that on the basis of that complex indicator the most emotional topic could be automatically detected. In this article an integrated approach is presented which combines the assessment of the text using the sentiment analysis method and context-independent psycholinguistic markers based on morphological features. The proposed approach can be a useful extension of Social Mining methods in different languages and it can be applicable in developing methods in the fields of affective and personality computing.

Abstract: As Ireland emerges from recession, riding a wave of growth largely driven by the presence of large profitable foreign multi-nationals, recurring questions surrounding the long-term durability of the so-called 'industrialisation by invitation' approach to modernisation persist. If Ireland is to benefit from this strategy, there needs to be significant transfer of knowledge between foreign and domestic firms, thus enabling the latter to emerge as global competitors in their own right. In order to investigate patterns of knowledge diffusion within the Irish economy, here we build a network of labour transitions (job switches) between foreign-owned multi-nationals and domestic firms using a new dataset constructed from the Irish economic census of 2014. Using network techniques for community detection, we identify a highly modular network structure, as workers tend to switch to a narrow set of similar industries that share their own skill set. We find that while some sectors such as pharmaceuticals, with a high share of foreign firms, are largely disconnected from the wider economy, other sectors such as financial services, IT and food processing are more integrated with domestic economic activities. This analysis suggests that policies focusing on increasing labour mobility between certain sectors, and hence enabling workers to move more freely throughout the economy, could result in improved knowledge and expertise transfer from foreign to domestic firms (and vice versa).