ScriptingRT is a new open source tool to collect response latencies in online studies of human cognition. ScriptingRT studies run as Flash applets in enabled browsers. ScriptingRT provides the building blocks of response latency studies, which are then combined with generic Apache Flex programming. Six studies evaluate the performance of ScriptingRT empirically. Studies 1–3 use specialized hardware to measure variance of response time measurement and stimulus presentation timing. Studies 4–6 implement a Stroop paradigm and run it both online and in the laboratory, comparing ScriptingRT to other response latency software. Altogether, the studies show that Flash programs developed in ScriptingRT show a small lag and an increased variance in response latencies. However, this did not significantly influence measured effects: The Stroop effect was reliably replicated in all studies, and the found effects did not depend on the software used. We conclude that ScriptingRT can be used to test response latency effects online.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively.

Looking at how social values are represented in fairy tales can give insights about the variations in communication of values across cultures. We study how values are communicated in fairy tales from Portugal, Italy and Germany using a technique called word embedding with a compass to quantify vocabulary differences and commonalities. We study how these three national traditions differ in their explicit references to values. To do this, we specify a list of value-charged tokens, consider their word stems and analyse the distance between these in a bespoke pre-trained Word2Vec model. We triangulate and critically discuss the validity of the resulting hypotheses emerging from this quantitative model. Our claim is that this is a reusable and reproducible method for the study of the values explicitly referenced in historical corpora. Finally, our preliminary findings hint at a shared cultural understanding and the expression of values such as Benevolence, Conformity, and Universalism across the studied cultures, suggesting the potential existence of a pan-European cultural memory.

The study of social influence in social media settings has gained significant importance in the last years due to the advent of the internet and the increase of social media users and followers of social media influencers (SMI). This chapter focuses on the followers' psychological experience by reviewing how SMIs are perceived by followers and what moves the users to follow SMIs. Users follow SMI in an attempt to obtain information, inspiration, and entertainment, therefore brands have the chance to promote their products and services based on an appealing communication that can be provided by the SMI. However, the SMIs' perceived credibility, trustworthiness, authenticity, and expertise can impact not just the interaction with followers but also the brand image. It is recommended that brands conduct a continuous and careful assessment of SMIs' communication style and followers' perceptions to improve followers' attitudes towards the brands and their purchase intentions.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.