Stakeholder engagement in water governance as social learning: lessons from practice

The OECD Principles on Water Governance set out various requirements for stakeholder engagement. Coupled with conceptualizations of social learning, this article asks how we define and enact stakeholder engagement and explores the actual practice of engagement of stakeholders in three fields of water governance. The results suggest that a key consideration is the purpose of the stakeholder engagement, requiring consideration of its ethics, process, roles and expected outcomes. While facilitators cannot be held accountable if stakeholder engagement ‘fails’ in terms of social learning, they are responsible for ensuring that the enabling conditions for social learning are met

Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

During the 2016 US elections Twitter experienced unprecedented levels of propaganda and fake news through the collaboration of bots and hired persons, the ramifications of which are still being debated. This work proposes an approach to identify the presence of organized behavior in tweets. The Random Forest, Support Vector Machine, and Logistic Regression algorithms are each used to train a model with a data set of 850 records consisting of 299 features extracted from tweets gathered during the 2016 US presidential election. The features represent user and temporal synchronization characteristics to capture coordinated behavior. These models are trained to classify tweet sets among the categories: organic vs organized, political vs non-political, and pro-Trump vs pro-Hillary vs neither. The random forest algorithm performs better with greater than 95% average accuracy and f-measure scores for each category. The most valuable features for classification are identified as user based features, with media use and marking tweets as favorite to be the most dominant.Comment: 51 pages, 5 figure

POISED: Spotting Twitter Spam Off the Beaten Paths

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks

Decentralized Learning Infrastructures for Community Knowledge Building

Learning in Communities of Practice (CoPs) makes up a significant portion of today's knowledge gain. However, only little technological support is tailored specifically towards CoPs and their particular strengths and challenges. Even worse, CoPs often do not possess the resources to host or develop a software ecosystem to support their activities. In this contribution, we describe a decentralized learning infrastructure for community knowledge building. It takes into account the constant change of these communities by providing a leightweight and scalable infrastructure, without the need for central coordination or facilitation. As a real use case, we implement a question-based dialog application for inquiry-based learning and ignorance modeling with our infrastructure. Additionally, we explore the possibility of using social bots to connect the services provided by the decentralized infrastructure to communication tools already present in most communities (e.g. chat platforms). Following a design science approach, we describe a multi-step evaluation of both the infrastructure and application, together with the improvements made to the resulting artifacts of each step. Our results indicate the relevance of our approach, that may serve as an example of how decentralized learning infrastructures for learning outside of formal settings can be applied by CoPs for knowledge building

Developing virtual heritage application with 3D collaborative virtual environments and mobile devices in a multi-cultural team: experiences and challenges

Until recently museums have been the sole repositories of an objective factual history. However, with the advent of online interactive media, there has been a shift to alternate forms of cultural exposition. This paper presents a project where 3D CVE is augmented with mobile devices in order to support a collaborative educational exploration of a famous historical site in Norway, where Battle of Stiklestad took place in 1030. This system can be used by both local and distant learning communities, working together towards a common goal. The paper presents a background for the project and describes the preliminary design. Finally, the paper discusses the challenges associated with developing educational augmented virtual heritage applications in a multicultural context

A Microservice Infrastructure for Distributed Communities of Practice

Non-formal learning in Communities of Practice (CoPs) makes up a significant portion of today’s knowledge gain. However, only little technological support is tailored specifically towards CoPs and their particular strengths and challenges. Even worse, CoPs often do not possess the resources to host or even develop a software ecosystem to support their activities. In this paper, we describe a distributed, microservice-based Web infrastructure for non-formal learning in CoPs. It mitigates the need for central infrastructures, coordination or facilitation and takes into account the constant change of these communities. As a real use case, we implement an inquiry-based learning application on-top of our infrastructure. Our evaluation results indicate the usefulness of this learning application, which shows promise for future work in the domain of community-hosted, microservice-based Web infrastructures for learning outside of formal settings