In this experiment, we operated within the novel research area of Informal Green Spaces (often called green wastelands), exploring emotional well-being with the employment of portable electroencephalography (EEG) devices. The apparatus (commercial EEG Muse headband) provided an opportunity to analyze states of calmness and alertness in n = 20 participants as they visited selected Informal Green Spaces in Warsaw, Poland. The article aims to test the hypothesis that passive recreation in Informal Green Spaces (IGSs) has a positive impact on emotional well-being and that there is a connection between the intensity of states of calmness and alertness and 1. the type of green space (IGS/GS), 2. the type of scenery and 3. the type of IGS. The preliminary experiment showed that there might be no substantial distinction in the users’ levels of emotional states when considering existing typologies. On the other hand, data-driven analysis suggests that there might be a connection between the state of alertness and some characteristics of specific areas. After carrying out the multivariate analyses of variance in the repeated measurement scheme and finding significant differences between oscillations in different areas, we conclude that there might be three possible sources of lower alertness and increased calmness in some areas. These are 1. the presence of “desirable” human intervention such as paths and urban furniture, 2. a lack of “undesirable” users and signs of their presence and 3. the presence of other “desirable” users.

Echo chambers, a recent phenomenon in the realm of social networks, have garnered significant attention from researchers due to their profound implications. Their role in propagating information, reinforcing beliefs and opinions, and potentially fostering inequality within networks and societies underscores the critical need for comprehensive understanding. Despite the lack of a clear definition, existing research has primarily concentrated on five aspects of echo chambers: their attributes, underlying mechanisms, modeling, detection, and mitigation strategies. The main objectives of this systematic review are to identify terminology, examine the effects of echo chambers, analyze approaches to echo chamber mechanisms, assess modeling and detection techniques, and evaluate metrics used to specify echo chambers in online social networks. By doing so, this article aims to illuminate the strengths and weaknesses of current approaches. To conduct this study, a systematic review was conducted of studies published from 2013 to October 2022, peer-reviewed in five prestigious publishers, including ACM Digital Library, IEEE Xplore, Science Direct, Springer, and Nature. The methodology of this systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Ultimately, 28 studies were selected for the final review. The findings of this study highlight several main limitations. Firstly, there is a lack of an accurate definition for echo chambers. Secondly, there is a lack of a solid approach to address the components of echo chambers. Thirdly, there is a controversial issue regarding the effect of echo chambers. Lastly, the measures used mostly did not adequately specify echo chambers.

Sentiment analysis has become a focal point of interdisciplinary research, prompting the use of diverse methodologies and the continual emergence of programming language packages. Notably, Python and R have introduced comprehensive packages in this realm. In this study, we analyze established packages in these languages, focusing on accuracy while also considering time complexity. Across experiments conducted on seven distinct datasets, a crucial revelation surfaces: the accuracy of these packages significantly varies depending on the dataset used. Among these, the ’sentimentr’ package consistently performs well across diverse datasets. Generally, Python libraries showcase superior processing speed. However, it’s essential to note that while these packages adeptly classify sentences as positive or negative, capturing sentiment intensity proves challenging. Our findings highlight a prevalent trend of overfitting, where these packages excel on familiar datasets but struggle when faced with unfamiliar ones.