Trusting code in the wild: A social network-based centrality rating for developers in the Rust ecosystem

As modern software extensively uses open source packages, developers regularly pull in new upstream code through frequent updates. While a manual review of all upstream changes may not be practical, developers may rely on the authors' and reviewers' identities, among other factors, to decide what level of review the new code may require. The goal of this study is to help downstream project developers prioritize review efforts for upstream code by providing a social network-based centrality rating for the authors and reviewers of that code. To that end, we build a social network of 6,949 developers across the collaboration activity from 1,644 Rust packages. Further, we survey the developers in the network to evaluate if code coming from a developer with a higher centrality rating is likely to be accepted with lesser scrutiny by the downstream projects and, therefore, is perceived to be more trusted. Our results show that 97.7\% of the developers from the studied packages are interconnected via collaboration, with each developer separated from another via only four other developers in the network. The interconnection among developers from different Rust packages establishes the ground for identifying the central developers in the ecosystem. Our survey responses ($N=206$) show that the respondents are more likely to not differentiate between developers in deciding how to review upstream changes (60.2\% of the time). However, when they do differentiate, our statistical analysis showed a significant correlation between developers' centrality ratings and the level of scrutiny their code might face from the downstream projects, as indicated by the respondents

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Hashtag Analysis of Indonesian COVID-19 Tweets Using Social Network Analysis

Social media has become more critical for people to communicate about the pandemic of COVID-19. In social media, hashtags are social annotations which often used to denote message content. It serves as an intuitive and flexible tool for making huge collections of posts searchable on Twitter. Through practices of hashtagging, user representations of a given post also become connected. This study aimed to analyze the hashtag of Indonesian COVID-19 Tweets using Social Network Analysis (SNA). We used SNA techniques to visualize network models and measure some centrality to find the most influential hashtag in the network. We collected and analyzed 500.000 public tweets from Twitter based on COVID-19 keywords. Based on the centrality measurement result, the hashtag #corona is a hashtag with the most connection with other hashtags. The hashtag #COVID19 is the hashtag that is most closely related to all other hashtags. The hashtag #corona is the hashtag that most acts as a bridge that can control the flow of information related to COVID-19. The hashtag #coronavirus is the most important of hashtags based on their link. Our study also found that the hashtag #covid19 and #wabah have a substantial relationship with religious-related hashtags based on network visualization

Manipulation of Online Reviews: Analysis of Negative Reviews for Healthcare Providers

There is a growing reliance on online reviews in today’s digital world. As the influence of online reviews amplified in the competitive marketplace, so did the manipulation of reviews and evolution of fake reviews on these platforms. Like other consumer-oriented businesses, the healthcare industry has also succumbed to this phenomenon. However, health issues are much more personal, sensitive, complicated in nature requiring knowledge of medical terminologies and often coupled with myriad of interdependencies. In this study, we collated the literature on manipulation of online reviews, identified the gaps and proposed an approach, including validation of negative reviews of the 500 doctors from three different states: New York and Arizona in USA and New South Wales in Australia from the RateMDs website. The reviews of doctors was collected, which includes both numerical star ratings (1-low to 5-high) and textual feedback/comments. Compared to other existing research, this study will analyse the textual feedback which corresponds to the clinical quality of doctors (helpfulness and knowledge criteria) rather than process quality experiences. Our study will explore pathways to validate the negative reviews for platform provider and rank the doctors accordingly to minimise the risks in healthcare

Three Essays on Trust Mining in Online Social Networks

This dissertation research consists of three essays on studying trust in online social networks. Trust plays a critical role in online social relationships, because of the high levels of risk and uncertainty involved. Guided by relevant social science and computational graph theories, I develop conceptual and predictive models to gain insights into trusting behaviors in online social relationships. In the first essay, I propose a conceptual model of trust formation in online social networks. This is the first study that integrates the existing graph-based view of trust formation in social networks with socio-psychological theories of trust to provide a richer understanding of trusting behaviors in online social networks. I introduce new behavioral antecedents of trusting behaviors and redefine and integrate existing graph-based concepts to develop the proposed conceptual model. The empirical findings indicate that both socio-psychological and graph-based trust-related factors should be considered in studying trust formation in online social networks. In the second essay, I propose a theory-based predictive model to predict trust and distrust links in online social networks. Previous trust prediction models used limited network structural data to predict future trust/distrust relationships, ignoring the underlying behavioral trust-inducing factors. I identify a comprehensive set of behavioral and structural predictors of trust/distrust links based on related theories, and then build multiple supervised classification models to predict trust/distrust links in online social networks. The empirical results confirm the superior fit and predictive performance of the proposed model over the baselines. In the third essay, I propose a lexicon-based text mining model to mine trust related user-generated content (UGC). This is the first theory-based text mining model to examine important factors in online trusting decisions from UGC. I build domain-specific trustworthiness lexicons for online social networks based on related behavioral foundations and text mining techniques. Next, I propose a lexicon-based text mining model that automatically extracts and classifies trustworthiness characteristics from trust reviews. The empirical evaluations show the superior performance of the proposed text mining system over the baselines

Coworker Mistreatment in a Singaporean Chinese Firm: The Roles of Third-Party Embeddedness and Network Closure

This study integrates research in social networks and interpersonal counterproductive behaviors to examine the role of third-party relationships in predicting an individual’s susceptibility to coworker mistreatment, and in moderating the relationship between coworker mistreatment and job performance. Third-party embeddedness and network closure are examined in the formal workflow network and the informal liking network. Results obtained from employees in a family-owned Chinese business in Singapore indicate that an individual is more likely to be mistreated by a coworker when both parties are strongly embedded in mutual third-party relationships in the workflow network, and that the individual is less likely to be mistreated when both parties are strongly embedded in the liking network. At the individual network level, network closure (i.e., the extent to which an individual’s contacts are themselves connected to one another) in the workflow network increases the likelihood that the individual will be mistreated by a coworker, but closure in the liking network weakens the negative relationship between mistreatment and performance. The findings offer a network-based perspective to understanding interpersonal mistreatment and counterproductive work behaviors, particularly in the context of Confucian Asian firms, and provide practical implications for organizations and individuals to reduce counterproductive behaviors at work

Coworker Mistreatment in a Singaporean Chinese Firm: The Roles of Third-Party Embeddedness and Network Closure

This study integrates research in social networks and interpersonal counterproductive behaviors to examine the role of third-party relationships in predicting an individual’s susceptibility to coworker mistreatment, and in moderating the relationship between coworker mistreatment and job performance. Third-party embeddedness and network closure are examined in the formal workflow network and the informal liking network. Results obtained from employees in a family-owned Chinese business in Singapore indicate that an individual is more likely to be mistreated by a coworker when both parties are strongly embedded in mutual third-party relationships in the workflow network, and that the individual is less likely to be mistreated when both parties are strongly embedded in the liking network. At the individual network level, network closure (i.e., the extent to which an individual’s contacts are themselves connected to one another) in the workflow network increases the likelihood that the individual will be mistreated by a coworker, but closure in the liking network weakens the negative relationship between mistreatment and performance. The findings offer a network-based perspective to understanding interpersonal mistreatment and counterproductive work behaviors, particularly in the context of Confucian Asian firms, and provide practical implications for organizations and individuals to reduce counterproductive behaviors at work

Dealings on the Dark Web: An Examination of the Trust, Consumer Satisfaction, and the Efficacy of Interventions Against a Dark Web Cryptomarket

Abstract Objective. The overarching goal of this thesis is to better understand not only the network dynamics which undergird the function and operation of cryptomarkets but the nature of consumer satisfaction and trust on these platforms. More specifically, I endeavour to push the cryptomarket literature beyond its current theoretical and methodological limits by documenting the network structure of a cryptomarket, the factors which predicts for vendor trust, the efficacy of targeted strategies on the transactional network of a cryptomarket, and the dynamics which facilitate consumer satisfaction despite information asymmetry. Moreover, we also aim to test the generalizability of findings made in prior cryptomarket studies (Duxbury and Haynie, 2017; 2020; Norbutas, 2018). Methods. I realize the aims of this research by using a buyer-seller dataset from the Abraxas cryptomarket (Branwen et al., 2015). Given the differences between the topics and the research questions featured, this thesis employs a variety of methodological techniques. Chapter two uses a combination of descriptive network analysis, community detection analysis, statistical modelling, and trajectory modelling. Chapter three utilizes three text analytic strategies: descriptive text analysis, sentiment analysis, and textual feature extraction. Finally, chapter four employs sequential node deletion pursuant to six law enforcement strategies: lead k (degree centrality), eccentricity, unique items bought/sold, cumulative reputation score, total purchase price, and random targeting. Results. Social network analysis of the Abraxas cryptomarket revealed a large and diffuse network where the majority of buyers purchased from a small cohort of vendors. This theme of preferential selection of vendors on the part of buyers is repeated in other findings within this study. More generally, the Abraxas transactional network can then be viewed as set of transactional islands as opposed to a large, densely connected conglomeration of vendors and buyers. With regard buyer feedback, buyers are generally pleased with their transactions on Abraxas as long as the product arrives on time and is as advertised. In general, vendors have a relatively low bar to achieve when it comes to satisfying their customers. Based on the results of the sequential node deletion, random targeting was found to be ineffective across the five outcome measures, producing minimal and a slow disruptive effect. Finally, these strategies are based on a power law where a small percentage of deleted nodes is responsible for an outsized proportion of the disruptive impact. Conclusion. As with all applied research examining emergent phenomena, this thesis lends itself to a more refined understanding of dark web cryptomarkets. While the results and conclusions drawn from these results are not perfectly generalizable to all cryptomarkets, they should serve to inform law enforcement on the dynamics which undergird these markets. To this extent, a sombre consideration of trust, consumer satisfaction, and tactical effectiveness of interventions is a necessary step towards the development of more effective countermeasures against these illicit online marketplaces. For law enforcement to be more effective against cryptomarkets, it is advised that an evidence-based approach be taken