Web2Touch 2019: Semantic Technologies for Smart Information Sharing and Web Collaboration

This foreword introduces a summary of themes and papers of the Web2Touch (W2T) 2019 Track at the 28th IEEE WETICE Conference held in Capri, June 2019. W2T 2019 includes ten full papers and one short paper. They all address relevant issues in the field of information sharing for collaboration, including, big data analytics, knowledge engineering, linked open data, applications of smart Web technologies, and smart care. The papers are a portfolio of hot issues in research and applications of semantics, smart technologies (e.g., IoT, sensors, devices for tele-monitoring, and smart contents management) with crucial topics, such as big data analysis, knowledge representation, smart enterprise management, among the others. This track shows how cooperative technologies based on knowledge representation, intelligent tools, and enhanced Web engineering can enhance collaborative work through smart service design and delivery, so it contributes to radically change the role of the semantic Web and applications

On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

What happened on social media during the recent pandemic? Who was the opinion leader of the conversations? Who influenced whom? Were they medical doctors, ordinary people, scientific experts? Did health institutions play an important role in informing and updating citizens? Identifying opinion leaders within social platforms is of particular importance and, in this paper, we introduce the idea of a time sensitive interaction graph to identify opinion leaders within Twitter conversations. To evaluate our proposal, we focused on all the tweets posted on Twitter in the period 2020-21 and we considered just the ones that were Italian-written and were related to COVID-19. After mapping these tweets into the graph, we applied the PageRank algorithm to extract the opinion leaders of these conversations. Results show that our approach is effective in identifying opinion leaders and therefore it might be used to monitor the role that specific accounts (i.e., health authorities, politicians, city administrators) have within specific conversations

Employee attitudes and (Digital) collaboration data: a preliminary analysis in the HRM field

The digital transformation of organizations is making workplace collaboration more and more powerful and work always "observable"; however, the informational and managerial potential of the generated data is still largely unutilized in Human Resource Management (HRM). Our research, conducted in collaboration with business engineers and economists, aims at exploring the relationship between digital work behaviors and employee attitudes. This paper is a work-in-progress contribution that presents a preliminary phase of data analysis we performed on a collection of Enterprise Collaboration Software (ECS) data. In the exploratory data analysis step, we analyze data in their original table format and elaborate it according to the user who performed the action and the performed action. Then, we move to a graph representation in order to make explicit the interaction between users and the objects of their actions. Finally, we introduce the concept of employee-attitude-oriented pattern as a mean to derive significant views over the overall graph and discuss Social Network Analysis (SNA) approaches that can be exploited for our purposes

Data-driven vs knowledge-driven inference of health outcomes in the ageing population: A case study

Preventive, Predictive, Personalised and Participative (P4) medicine has the potential to not only vastly improve people's quality of life, but also to significantly reduce healthcare costs and improve its efficiency. Our research focuses on age-related diseases and explores the opportunities offered by a data-driven approach to predict wellness states of ageing individuals, in contrast to the commonly adopted knowledge-driven approach that relies on easy-to-interpret metrics manually introduced by clinical experts. This is done by means of machine learning models applied on the My Smart Age with HIV (MySAwH) dataset, which is collected through a relatively new approach especially for older HIV patient cohorts. This includes Patient Related Outcomes values from mobile smartphone apps and activity traces from commercial-grade activity loggers. Our results show better predictive performance for the data-driven approach. We also show that a post hoc interpretation method applied to the predictive models can provide intelligible explanations that enable new forms of personalised and preventive medicine

Invited Speech: Data Analytics and (Interpretable) Machine Learning for Social Good

In recent years, in all contexts of our lives, we have seen a real explosion of data. From a research standpoint, data processing needs have increasingly become common in an ever growing number of applications, with potential benefits not only in our work but also in our lives: the need not just to acquire, store and perform modest operational tasks but also to analyze and properly interpret data. In this talk, we consider some of the hottest and most demanding scenarios in our daily lives, which include: medical analytics to improve the quality of life of the elderly and reduce health care expenses; social network analytics for enhancing cultural heritage dissemination; exploration of work datafication potential in improving the management of human resources (HRM); game analytics to foster Computational Thinking in education. We describe the recent findings we have obtained in our research in these contexts using the latest technology for data analytics, including interpretable machine learning, and discuss the consequences and directions for the future

Web2Touch 2020–21: Semantic Technologies for Smart Information Sharing and Web Collaboration

This foreword introduces a summary of themes and papers of the Web2Touch (W2T) 2020–21 Track at the 29th IEEE WETICE Conference held as a virtual Conference, in October 2020. W2T 2020–21 includes six full papers and four short papers. They all address relevant issues in the field of information sharing for collaboration, including, big data analytics, knowledge engineering, linked open data, applications of smart Web technologies, and smart care. The papers address a portfolio of hot issues in research and applications of semantics, smart technologies (e.g., IoT, sensors, devices for tele-monitoring, and smart contents management) with crucial topics, such as big data analysis, knowledge representation, smart enterprise management, among the others. This track shows how cooperative technologies based on knowledge representation, intelligent tools, and enhanced Web engineering can enhance collaborative work through smart service design and delivery, so it contributes to radically change the role of the semantic Web and applications

Let the Games Speak by Themselves: Towards Game Features Discovery Through Data-Driven Analysis and Explainable AI

The idea behind this work is to start exploring the application of data analytics and (explainable) machine learning techniques to better understand games and discover new features that will possibly help in effectively exploiting them in different socially useful domains. We prove the feasibility of the idea by: (i) collecting a large dataset of board game information; (ii) designing and testing an information processing pipeline for automatically discovering game categories and game mechanics, with some first encouraging results. In the future, we plan to further generalize this approach for different kinds of games and for discovering currently unknown but useful aspects, e.g. games or game features that could better foster Computational Thinking in education, those better suited to be applied in social distancing contexts, and so on

An Intelligent Dashboard for Assisted Tweet Composition in the Cultural Heritage Area (Work-in-progress)

Cultural Heritage institutions are nowadays using social media to communicate with citizens and tourists. However, providing actual effective communication is not an easy task, as every day millions of messages are posted through social media. Thus, getting visibility is not trivial. In this paper we present the architecture of a dashboard, accessible by mobile Android devices, to support museum social media managers in composing effective tweets by providing suggestions to improve message drafts. At this aim, the application exploits machine learning techniques over data related to tweets posted by museums in the past