We conducted semi-structured interviews with 20 members who engaged in community-based effort to reverse overdoses using a smartphone-based app in an Eastern United States (U.S.) city. Drawing from feminist ethics of care, we identify how the caring practices of community members extend from administering a medical intervention to building trust and support between the care receivers and caregivers in the case of opioid overdose response. Contrary to the predominant patient-centered care paradigm, we emphasize community-centered care, which acknowledges the resistance of individuals and attends to reallocating caring responsibility and building relationships within the community. Our results highlight how trust intersects with social ecologies of care in the highly stigmatized context of opioid overdose and that trustful and less hierarchical relationships are critical sources of care for groups experiencing marginalization. We discuss applying harm reduction principles in designing health technologies for substance use disorders. We also discuss research and design opportunities for community-centered design for marginalized individuals and community caregivers.

The need for advanced automation and artificial intelligence (AI) in various fields, including text classification, has dramatically increased in the last decade, leaving us critically dependent on their performance and reliability. Yet, as we increasingly rely more on AI applications, their algorithms are becoming more nuanced, more complex, and less understandable precisely at a time we need to understand them better and trust them to perform as expected. Text classification in the medical and cybersecurity domains is a good example of a task where we may wish to keep the human in the loop. Human experts lack the capacity to deal with the high volume and velocity of data that needs to be classified, and ML techniques are often unexplainable and lack the ability to capture the required context needed to make the right decision and take action. We propose a new abstract configuration of Human-Machine Learning (HML) that focuses on reciprocal learning, where the human and the AI are collaborating partners. We employ design-science research (DSR) to learn and design an application of the HML configuration, which incorporates software to support combining human and artificial intelligences. We define the HML configuration by its conceptual components and their function. We then describe the development of a system called Fusion that supports human-machine reciprocal learning. Using two case studies of text classification from the cyber domain, we evaluate Fusion and the proposed HML approach, demonstrating benefits and challenges. Our results show a clear ability of domain experts to improve the ML classification performance over time, while both human and machine, collaboratively, develop their conceptualization, i.e., their knowledge of classification. We generalize our insights from the DSR process as actionable principles for researchers and designers of 'human in the learning loop' systems. We conclude the paper by discussing HML configurations and the challenge of capturing and representing knowledge gained jointly by human and machine, an area we feel has great potential.

Text mining have gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment mining, with much attention being given to sentiment analysis and opinion mining. An essential step in the process of comment mining is text pre-processing; a step in which each linguistic term is assigned with a weight that commonly increases with its appearance in the studied text, yet is offset by the frequency of the term in the domain of interest. A common practice is to use the well-known tf-idf formula to compute these weights. This paper reveals the bias introduced by between-participants' discourse to the study of comments in social media, and proposes an adjustment. We find that content extracted from discourse is often highly correlated, resulting in dependency structures between observations in the study, thus introducing a statistical bias. Ignoring this bias can manifest in a non-robust analysis at best and can lead to an entirely wrong conclusion at worst. We propose an adjustment to tf-idf that accounts for this bias. We illustrate the effects of both the bias and correction with with seven Facebook fan pages data, covering different domains, including news, finance, politics, sport, shopping, and entertainment.

Confidential information is all too easily leaked by naive users posting comments. In this paper we introduce DUIL, a system for Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material non-public information. DUIL is comprised of several artefacts, each designed to analyse a different aspect of this challenge: the information, the user(s) who posted the information, and the user(s) who may be involved in the dissemination of information. We present a design science analysis of DUIL as an information system artefact comprised of social, information, and technology artefacts. We demonstrate the performance of DUIL on real data crawled from several Facebook news pages spanning two years of news articles.

This study investigates the interplay between online news, reader comments, and social networks to detect and characterize comments leading to the revelation of censored information. Censorship of identity occurs in different contexts–for example, the military censors the identity of personnel and the judiciary censors the identity of minors and victims. We address three objectives: (a) assess the relevance of identity censorship in the presence of user‐generated comments, (b) understand the fashion of censorship circumvention (what people say and how), and (c) determine how comment analysis can aid in identifying decensorship and information leakage through comments. After examining 3,582 comments made on 48 articles containing obfuscated terms, we find that a systematic examination of comments can compromise identity censorship. We identify and categorize information leakage in comments indicative of knowledge of censored information that may result in information decensorship. We show that the majority of censored articles contained at least one comment leading to censorship circumvention.

This paper simulates Companies' ego networks on Twitter, meaning the companies' number and type of followers. Evident from our data, we show that followers' distribution, in our focus, is neither scale free nor random, thus common network simulations cannot be used to mimic observed data. We present novel rate equations model to capture the complex dynamics of these ego networks.

In this work we study information leakage through discussions in online social networks. In particular, we focus on articles published by news pages, in which a person's name is censored, and we examine whether the person is identifiable (decensored) by analyzing comments and social network graphs of commenters. As a case study for our proposed methodology, in this paper we considered 48 articles (Israeli, military related) with censored content, followed by a threaded discussion. We qualitatively study the set of comments and identify comments (in this case referred as "leakers") and the commenter and the censored person. We denote these commenters as "leakers". We found that such comments are present for some 75% of the articles we considered. Finally, leveraging the social network graphs of the leakers, and specifically the overlap among the graphs of the leakers, we are able to identify the censored person. We show the viability of our methodology through some illustrative use cases.

Purpose IP reputation systems, which filter e‐mail based on the sender's IP address, are located at the perimeter – before the messages reach the mail server's anti‐spam filters. To increase IP reputation system efficacy and overcome the shortcomings of individual IP‐based filtering, recent studies have suggested exploiting the properties of IP clusters, such as those of Autonomous Systems (AS). Cluster‐based techniques can enhance accuracy and reduce false negative rates. However, clusters generally contain enormous amounts of IP addresses, which hinder cluster‐based systems from reaching their full spam filtering potential. The purpose of this paper is exploitation of social network metrics to obtain a more granular, i.e. sub‐divided, view of cluster‐based reputation, and thus enhance spam filtering accuracy. Design/methodology/approach The authors examined the performance of various social network metrics, including nodal degree, betweenness centrality, closeness centrality and valued graphs, to find an optimal element that enhances IP reputation prediction in AS clusters. Findings It was found that all measures contributed to prediction, yet the best predictor of spam reputation was the out‐degree metric, which showed a strong positive correlation with spam reputation prediction. This implies that more granular information can increase the accuracy of IP reputation prediction in AS clusters. Practical implications Used in conjunction with other technologies, the granular cluster‐based reputation system can be a valuable addition to commercial and open‐source spam filtering systems, or to standalone DNS‐based blacklists. Originality/value The authors' approach can promote mitigation of larger spam volumes at the perimeter, save bandwidth, and conserve valuable system resources.

This article presents some of the findings from the editorial process of creating an Encyclopedia of Knowledge Management. The global view of knowledge management (KM) research made available by this process provides interesting insights into the state of knowledge management research today and raises some questions regarding future directions for knowledge management as a discipline. The popularity and interaction between the different foundations of KM research is discussed, and specific attention is given to the discipline of social epistemology as a frame of reference for knowledge management research.

Complex organizations are characterized by distributed decision making, and require a sharing of perspectives among distributed decision makers if they are to coordinate activity and adapt to changing circumstances. This paper explains the process of perspective taking and its roles in human communication, mutual trust, and organizational learning. SPIDER is a software environment for enriching communication among managers by improving their ability to represent and exchange understandings of the situations they face. Cognitive maps linked to underlying assumptions are used as a basis for sharing their perspectives and enabling coordination of distributed decision making.