From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page

Towards Effective Peripherial Chatbot Communication with Adjustable Intensity of Content Changes

The art of designing user interfaces requires the efficient management of all the elements with which they cooperate. Problems appear with the effective delivery of messages to users dynamically switching web pages. This happens, for example, with chatbots appearing in peripheral areas. They are difficult to notice and are often missed during website exploration. This study shows the impact of the intensity of content changes in the chatbot area on the user response. The study was performed for changes in the peripheral area for different reaction times. The results showed that the intensity of changes within the chatbot area mainly impacts reactions within 400 ms; for a longer response, the difference between substantial and small changes is lower. Chatbot dynamics can be adjusted to user behavior to increase awareness and the performance of message delivery

Eye-Tracker Study of Influence of Affective Disruptive Content on User’s Visual Attention and Emotional State

When reading interesting content or searching for information on a website, the appearance of a pop-up advertisement in the middle of the screen is perceived as irritating by a recipient. Interrupted cognitive processes are considered unwanted by the user but desired by advertising providers. Diverting visual attention away from the main content is intended to focus the user on the appeared disruptive content. Is the attempt to reach the user by any means justified? In this study, we examined the impact of pop-up emotional content on user reactions. For this purpose, a cognitive experiment was designed where a text-reading task was interrupted by two types of affective pictures: positive and negative ones. To measure the changes in user reactions, an eye-tracker (for analysis of eye movements and changes in gaze points) and an iMotion Platform (for analysis of face muscles’ movements) were used. The results confirm the impact of the type of emotional content on users’ reactions during cognitive process interruptions and indicate that the negative impact of cognitive process interruptions on the user can be reduced. The negative content evoked lower cognitive load, narrower visual attention, and lower irritation compared to positive content. These results offer insight on how to provide more efficient Internet advertising

Modeling the impact of the habituation effect on information spreading processes with repeated contacts under an SI model.

People are exposed to information from different sources whether or not such exposure is desired. Due to a limited ability to process information, only parts of the messages may be absorbed, and other parts may be ignored. Repeated stimuli lead to lower responses due to the learning process and the habituation effect. While this effect has been intensively studied, mainly in relation to visual stimulus, it is also incorporated in information spreading processes. Information spreading models often assume the possibility of repeated contact, but no habituation effect, which lowers the level of responsiveness of nodes in the network, has been implemented. Here, we study the impact of the habituation effect on information spreading with a susceptible-infected (SI) model, which is often the basis for other models. We assume that a decrease in habituation has an impact on propagation processes. Analysis of the results shows that the course of these propagation processes behave differently, significantly worsening their results. These processes are very sensitive, even to small changes in the level of habituation

Example illustrating the impact of repeated ineffective/unwanted contacts on node responsiveness.

Example illustrating the impact of repeated ineffective/unwanted contacts on node responsiveness.</p

Duration for spreading processes with and without habituation.

Duration for spreading processes with and without habituation.</p

Duration increase for spreading processes with the habituation effect.

Duration increase for spreading processes with the habituation effect.</p

Coverage for spreading processes with and without habituation.

Coverage for spreading processes with and without habituation.</p

Fig 4 -

(A1) Coverage for spreading processes with and without habituation. (A2) Coverage decrease in processes with the habituation effect, compared to processes without habituation, sorted by the decrease in coverage. (A3--A5) Distances between coverage in simulations with and without the habituation effect, with results sorted by coverage without habituation and assigned corresponding results from processes with the habituation effect. (B1) Coverage for each network for spreading processes with and without habituation. (B2) Decrease in coverage for each network in relation to a process without habituation. (C1) Coverage for each alpha value for spreading processes with habituation compared to a process without habituation. (C2) Decrease in coverage for each alpha in relation to a process without habituation. (D1) Coverage for propagation probabilities for spreading processes with and without habituation. (D2) Decrease in coverage for each PP value in relation to a process without habituation. (E1) Coverage for each seeding percentage for spreading processes with and without habituation. (E2) Decrease in coverage for each SP in relation to a process without habituation. (F1) Coverage for each tau value for spreading processes with habituation in comparison with processes without habituation. (F2) Decrease in coverage for each tau in relation to a process without habituation.</p

Parameters used for diffusion in the simulations.

People are exposed to information from different sources whether or not such exposure is desired. Due to a limited ability to process information, only parts of the messages may be absorbed, and other parts may be ignored. Repeated stimuli lead to lower responses due to the learning process and the habituation effect. While this effect has been intensively studied, mainly in relation to visual stimulus, it is also incorporated in information spreading processes. Information spreading models often assume the possibility of repeated contact, but no habituation effect, which lowers the level of responsiveness of nodes in the network, has been implemented. Here, we study the impact of the habituation effect on information spreading with a susceptible–infected (SI) model, which is often the basis for other models. We assume that a decrease in habituation has an impact on propagation processes. Analysis of the results shows that the course of these propagation processes behave differently, significantly worsening their results. These processes are very sensitive, even to small changes in the level of habituation.</div