Open source software communities thrive on global collaboration and contributions from diverse participants. This study explores the Rust programming language ecosystem to understand its contributors’ demographic composition and interaction patterns. Our objective is to investigate the phenomenon of participation inequality in key Rust projects and the presence of diversity among them. We studied GitHub pull request data from the year leading up to the release of the latest completed Rust community annual survey in 2023. Specifically, we extracted information from three leading repositories: Rust, Rust Analyzer, and Cargo, and used social network graphs to visualize the interactions and identify central contributors and subcommunities. Social network analysis has shown concerning disparities in gender and geographic representation among contributors who play pivotal roles in collaboration networks and the presence of varying diversity levels in the subcommunities formed. These results suggest that while the Rust community is globally active, the contributor base does not fully reflect the diversity of the wider user community. We conclude that there is a need for more inclusive practices to encourage broader participation and ensure that the contributor base aligns more closely with the diverse global community that utilizes Rust.

Mentorship in Open Source Software (OSS) projects is crucial for reducing barriers to entry for newcomers and for fostering the technical and social integration of new contributors. While mentorship in OSS has been recognized as essential for sustainable project growth, quantitative research supporting qualitative findings is not common. To address this gap, we present OpenMent, a comprehensive dataset comprising over 500,000 issue comments, pull request comments, and commit messages from GitHub projects participating in the Google Summer of Code (GSoC) program. OpenMent is curated to capture role-specific interactions and communication patterns between mentors and mentees, providing information on the challenges and dynamics of OSS mentoring. This dataset is designed to be a reusable resource for the Software Engineering community, enabling researchers and practitioners to explore mentorship dynamics and investigate the impact of mentoring on contributor retention. By making OpenMent openly available, we aim to facilitate future research in OSS mentorship, fostering a deeper understanding of mentorship challenges, strategies, and contributions to the growth and inclusivity of OSS ecosystems.

The rapid spread of misinformation on social media platforms poses significant societal challenges, underscoring the need for effective methods to detect fake news spreaders. While many studies combine textual and user profile features for this task, the individual impact of these attributes remains unclear, raising questions about how each feature independently influences model performance. This study evaluates the effectiveness of textual data and user profile information in detecting fake news spreaders. By utilizing the Bidirectional Long Short-Term Memory (BiLSTM) model on the TruthSeeker dataset, this study assesses the individual contribution of each feature and analyzes the effect of combining them on model accuracy. Additionally, the role of optimization techniques, such as L2 regularization and early stopping, in improving detection performance was investigated. The results indicate that textual data alone is highly effective (achieving an accuracy of 97.78%) in distinguishing between real and fake news, while the inclusion of user profile data (97.61%) does not significantly enhance model performance. Furthermore, models using only user profile data exhibited accuracy between 55% and 64%, highlighting the limited predictive power of this feature set alone.

Persuasive technologies, which are intended to change users' attitudes or behaviors and encourage specific actions, are widely applied across various domains.However, the fine line between persuasion and coercion raises significant ethical concerns, which current literature only superficially addresses.This paper aims to deepen the understanding of factors influencing the ethical perception of persuasive technologies through a systematic literature review of 17 journal articles.The selected studies were analyzed using content analysis to identify key ethical factors.The findings indicate that factors such as autonomy, consent, data privacy, transparency, and addictive design strategies significantly influence users' ethical perceptions across multiple application domains.Generative artificial intelligence (AI) technologies or AI agents, particularly applications like argumentative chatbots and storytelling robots, exhibit the highest number of ethical considerations.The study also notes thematic overlaps among many ethical factors, with the context and use case impacting ethical perceptions.Based on these results, this paper offers design recommendations and suggestions for the design of ethical persuasive technology applications.

The rapid rise of persuasive technologies (PT) in healthcare, education, and e-commerce has raised ethical concerns.This study explores ethical PT design through focus groups with ten HCI experts, revealing two main themes: visual information presentation and clarity in communication.Participants emphasized that effective visuals, such as checklist bullet points and short videos, enhance consent comprehension and trust, while clear messaging boosts perceived trustworthiness.Transparent communication about persuasive tactics, balanced with user autonomy, is essential.These insights support ethical, user-centered PT design that fosters trust and minimizes manipulation.

People typically eat what they shop for; if consumers shop for healthy foods, they will likely eat healthy foods. In order to influence healthier eating habits among consumers, it is important to identify the factors that influence them to shop for healthy foods. To contribute to ongoing research in this area, we explore the influence of commonly used e-commerce strategies: personality, persuasive strategies, social support, relative price, and perceived product quality on healthy shopping habits among e-commerce shoppers. Research has shown that personalizing these strategies makes them more effective in achieving the desired behavior change among users. Age and gender have been identified as factors that can be used for group-based personalization. We thus investigate the moderating effect of age and gender on the factors that influence healthy shopping habits in e-commerce shoppers. To achieve this, we carried out an online study of 244 e-commerce shoppers. Using partial least squares structural equation modeling (PLS-SEM), we developed a path model using the commonly used e-commerce factors: personality, persuasive strategies, social support, relative price, and perceived product quality. The result of our analysis suggests that social support, relative price and perceived product quality significantly influence healthy shopping habits in e-commerce shoppers. In addition, females are more influenced by social support to adopt healthy shopping habits compared to male e-shoppers. Furthermore, older shoppers are more influenced by social support to adopt healthy shopping habits, while the younger shoppers are more influenced by the relative price of products.

Behavior modeling has become a very important behavior change technique employed in most fitness apps. However, its effect as a persuasive strategy on users has not been well investigated. Consequently, we conducted an empirical study among 669 participants to uncover: (1) how the perceived persuasiveness of behavior model design influences three social cognitive theory (SCT) determinants of behavior: self-efficacy, self-regulation and outcome expectation; and (2) the moderating effect of gender-based personalization. We based our study on user evaluation of prototypes of behavior models performing push-up and squat exercise behaviors as a case study. Our results show that, overall, the perceived persuasiveness of behavior models significantly influences all of the three SCT factors. The effect of persuasiveness on self-regulation (β = 0.42, p < 0.001) and outcome expectation (β = 0.41, p < 0.001) is stronger than on self-efficacy (β = 0.13, p < 0.05). Moreover, the behavior model design has a stronger effect on females' self-efficacy and males' outcome expectation if personalized to their gender. We discuss the implication of our findings.

Personalizing persuasive technologies (PTs) is one of the hallmarks of a successful PT intervention. However, there is a lack of understanding of how Africans and North Americans differ or are similar in the susceptibility to persuasive strategies. To bridge this gap, we conducted a cross-cultural study among 284 subjects to investigate the moderating effect of culture on the susceptibility of users to Cialdini's principles of persuasion. Specifically, using Nigeria and Canada as a case study, we investigated how both groups vary in their levels of susceptibility to Authority, Commitment, Consensus, Liking, Reciprocity and Scarcity. The results of our analysis show that Nigerians are more susceptible to Authority and Scarcity than Canadians, while Canadians are more susceptible to Reciprocity, Liking and Consensus than Nigerians. However, both groups do not differ with respect to Commitment (the most persuasive strategy). Finally, we discussed our findings and mapped the most persuasive Cialdini's principles in each group to implementable persuasive strategies in the PT domain.

With the increase in the number of e-commerce companies over the last decade, there is stiffer competition for e-businesses to win and retain customers. Companies have to give clients reasons to shop with them and become return customers. The use of personalization strategies and persuasive technology have been identified as means through which e-businesses can engage their clients and give them a unique shopping experience. To con-tribute to ongoing research in personalization and persuasive technology in e-commerce, my thesis proposes a framework that can create a personalized shopping experience for clients using the consumers' personality and shopping type. This paper presents the results of the first stage of my research which is a user study carried out on 324 e-commerce shoppers to identify the persuasive strategies and its implementation in an e-commerce site, Amazon, and to evaluate the persuasiveness of these strategies to consumers. The result of this thesis can contribute to ongoing research in development of personalization and persuasive strategies that work in e-commerce especially for new companies.

Active participation of users in Q&A social networks like Stack Overflow is key to the sustenance of the network. One way to encourage participation is to allow collaboration or cooperation between users in order to improve question and answer posts, and allow users to learn from one another. In order to implement strategies that encourage cooperation, it is important to understand what influences the users in the network to cooperate. In this extended abstract, we investigate the social support principles that influence cooperation in Stack Overflow. Using a sample size of 282 Stack Overflow users, we develop and test a global research model using partial least squares structural equation modelling (PLS-SEM). We further investigate any possible differences in the effect of these strategies between cultures, by testing two cultural subgroups; collectivist and individualist cultures. Our results show that social learning significantly influences cooperation in Stack Overflow at the global level. However, at the cultural subgroup level, recognition influences cooperation among collectivists, while social facilitation influences individualists to cooperate. These findings suggest possible design guidelines in the development of successful personalized Q&A social networking sites that encourage participation through cooperation.

Research has shown that in Q&A social networks, collaboration between respondents results in quality answers. Since good answers are required to keep any Q&A social network active, it is important to understand the characteristics of these collaborations and the collaborators. In this paper, we investigate how Stack Overflow promotes collaboration by allowing users to edit existing questions and answers in order to improve them. Using over 40,000 answer posts, our study reveals that collaboration in answer posts is not a function of achievement earned in terms of badges, as most edits associated with "best answer" rewards were posted by users who have not earned any answer badge. Our study further shows that posts that earned the "best answer" reward have more comments than those that did not. This study though, work in progress, can aid developers in implementing collaboration strategies in social networks that work.

In Q&A social networks, the few respondents that answer most of the questions are an asset to that network. Being able to predict the churn of these expert respondents will enable the owners of such network put things in place in order to keep them. In this paper, we predicted the churn of expert respondents in Stack Overflow. We identified experts based on the InDegree of the respondents and the value of the incentives earned by these experts from the questions they have answered in the past. Using four data mining techniques: logistic regression, neural networks, support vector machines and random forests, we predicted user churn and evaluated our results with four evaluation metrics: percentage correctly classified, area under receiver operating characteristic curve, precision and recall. Of the four data mining algorithms, random forests performed best with PCC of 76%, ROC area of 0.82, precision of 0.76 and recall of 0.77.