A customisable pipeline for continuously harvesting socially-minded Twitter users

On social media platforms and Twitter in particular, specific classes of users such as influencers have been given satisfactory operational definitions in terms of network and content metrics. Others, for instance online activists, are not less important but their characterisation still requires experimenting. We make the hypothesis that such interesting users can be found within temporally and spatially localised contexts, i.e., small but topical fragments of the network containing interactions about social events or campaigns with a significant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profile discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profiles dataset includes key network and content-based users metrics, enabling experimentation with user-defined score functions that characterise specific classes of online users. The paper describes the design and implementation of the pipeline and its empirical evaluation on a case study consisting of healthcare-related campaigns in the UK, showing how it supports the operational definitions of online activism, by comparing three experimental ranking functions. The code is publicly available.Comment: Procs. ICWE 2019, June 2019, Kore

Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

The Industry and Policy Context for Digital Games for Empowerment and Inclusion:Market Analysis, Future Prospects and Key Challenges in Videogames, Serious Games and Gamification

The effective use of digital games for empowerment and social inclusion (DGEI) of people and communities at risk of exclusion will be shaped by, and may influence the development of a range of sectors that supply products, services, technology and research. The principal industries that would appear to be implicated are the 'videogames' industry, and an emerging 'serious games' industry. The videogames industry is an ecosystem of developers, publishers and other service providers drawn from the interactive media, software and broader ICT industry that services the mainstream leisure market in games, The 'serious games' industry is a rather fragmented and growing network of firms, users, research and policy makers from a variety of sectors. This emerging industry is are trying to develop knowledge, products, services and a market for the use of digital games, and products inspired by digital games, for a range of non-leisure applications. This report provides a summary of the state of play of these industries, their trajectories and the challenges they face. It also analyses the contribution they could make to exploiting digital games for empowerment and social inclusion. Finally, it explores existing policy towards activities in these industries and markets, and draws conclusions as to the future policy relevance of engaging with them to support innovation and uptake of effective digital game-based approaches to empowerment and social inclusion.JRC.J.3-Information Societ