Citizens and Institutions as Information Prosumers. The Case Study of Italian Municipalities on Twitter

The aim of this paper is to address changes in public communication following the advent of Internet social networking tools and the emerging web 2.0 technologies which are providing new ways of sharing information and knowledge. In particular public administrations are called upon to reinvent the governance of public affairs and to update the means for interacting with their communities. The paper develops an analysis of the distribution, diffusion and performance of the official profiles on Twitter adopted by the Italian municipalities (comuni) up to November 2013. It aims to identify the patterns of spatial distribution and the drivers of the diffusion of Twitter profiles; the performance of the profiles through an aggregated index, called the Twitter performance index (Twiperindex), which evaluates the profiles' activity with reference to the gravitational areas of the municipalities in order to enable comparisons of the activity of municipalities with different demographic sizes and functional roles. The results show that only a small portion of innovative municipalities have adopted Twitter to enhance e-participation and e-governance and that the drivers of the diffusion seem to be related either to past experiences and existing conditions (i.e. civic networks, digital infrastructures) developed over time or to strong local community awareness. The better performances are achieved mainly by small and medium-sized municipalities. Of course, the phenomenon is very new and fluid, therefore this analysis should be considered as a first step in ongoing research which aims to grasp the dynamics of these new means of public communication

Model Checking Social Network Models

A social network service is a platform to build social relations among people sharing similar interests and activities. The underlying structure of a social networks service is the social graph, where nodes represent users and the arcs represent the users' social links and other kind of connections. One important concern in social networks is privacy: what others are (not) allowed to know about us. The "logic of knowledge" (epistemic logic) is thus a good formalism to define, and reason about, privacy policies. In this paper we consider the problem of verifying knowledge properties over social network models (SNMs), that is social graphs enriched with knowledge bases containing the information that the users know. More concretely, our contributions are: i) We prove that the model checking problem for epistemic properties over SNMs is decidable; ii) We prove that a number of properties of knowledge that are sound w.r.t. Kripke models are also sound w.r.t. SNMs; iii) We give a satisfaction-preserving encoding of SNMs into canonical Kripke models, and we also characterise which Kripke models may be translated into SNMs; iv) We show that, for SNMs, the model checking problem is cheaper than the one based on standard Kripke models. Finally, we have developed a proof-of-concept implementation of the model-checking algorithm for SNMs.Comment: In Proceedings GandALF 2017, arXiv:1709.0176

The Rise of Mobile and the Diffusion of Technology-Facilitated Trafficking

In this report, researchers at the USC Annenberg Center on Communication Leadership & Policy (CCLP) reveal how those involved in human trafficking have been quick to adapt to the 21st-century global landscape. While the rapid diffusion of digital technologies such as mobile phones, social networking sites, and the Internet has provided significant benefits to society, new channels and opportunities for exploitation have also emerged. Increasingly, the business of human trafficking is taking place online and over mobile phones. But the same technologies that are being used for trafficking can become a powerful tool to combat trafficking. The precise role that digital technologies play in human trafficking still remains unclear, however, and a closer examination of the phenomenon is vital to identify and respond to new threats and opportunities.This investigation indicates that mobile devices and networks have risen in prominence and are now of central importance to the sex trafficking of minors in the United States. While online platforms such as online classifieds and social networking sites remain a potential venue for exploitation, this research suggests that technology facilitated trafficking is more diffuse and adaptive than initially thought. This report presents a review of current literature, trends, and policies; primary research based on mobile phone data collected from online classified sites; a series of firsthand interviews with law enforcement; and key recommendations to policymakers and stakeholders moving forward

Quantifying the invisible audience in social networks

This paper combines survey and large-scale log data to examine how well users’ perceptions of their audience match their actual audience on Facebook.AbstractWhen you share content in an online social network, who is listening? Users have scarce information about who actually sees their content, making their audience seem invisible and difficult to estimate. However, understanding this invisible audience can impact both science and design, since perceived audiences influence content production and self-presentation online. In this paper, we combine survey and large-scale log data to examine how well users’ perceptions of their audience match their actual audience on Facebook. We find that social media users consistently underestimate their audience size for their posts, guessing that their audience is just 27% of its true size. Qualitative coding of survey responses reveals folk theories that attempt to reverse-engineer audience size using feedback and friend count, though none of these approaches are particularly accurate. We analyze audience logs for 222,000 Facebook users’ posts over the course of one month and find that publicly visible signals — friend count, likes, and comments — vary widely and do not strongly indicate the audience of a single post. Despite the variation, users typically reach 61% of their friends each month. Together, our results begin to reveal the invisible undercurrents of audience attention and behavior in online social networks.Authored by Michael S. Bernstein, Eytan Bakshy, Moira Burke and Brian Karrer

Disseminating Research Information through Facebook and Twitter (DRIFT): presenting an evidence based framework

Background: The social media platform Facebook boasts over 1,284 million daily active users globally. It is also known that a large proportion of adults use the internet to seek health related information.Aim: to critically analyse the use of social media to engage parents of children with ADHD with clinical research findings.Methods: Observation and qualitative content analysis combined with Facebook insights was used to evaluate the levels of engagement and interaction with different types of research information.Results: Over 1100 people from 41 nations have engaged with the group. Sharing information through a range of Facebook functions was found to successfully achieve engagement and reach nationally and internationally for this demographic.Conclusion: Lay research users are eager to engage and understand clinical research and social media is an appropriate way to disseminate this. This article has proposed some methods and explanatory reasons for this phenomena.Implications for practice: It is known that social media can be used for effective communication. This article presents a much-needed evidence based framework that may be used by nursing and health researchers to successfully achieve this

Phantom cascades: The effect of hidden nodes on information diffusion

Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and sampling strategies, this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades. In this work we study the effect of hidden network structure on information diffusion processes. We characterise information cascades through activation paths traversing visible and hidden parts of the network. We quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error. Finally, we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network.Comment: Preprint submitted to Elsevier Computer Communication