Paper by Sotirios Koussouris, Fenareti Lampathaki, Gianluca Misuraca, Panagiotis Kokkinakos, and Dimitrios Askounis: “Despite the availability of a myriad of Information and Communication Technologies (ICT) based tools and methodologies for supporting governance and the formulation of policies, including modelling expected impacts, these have proved to be unable to cope with the dire challenges of the contemporary society. In this chapter we present the results of the analysis of a set of promising cases researched in order to understand the possible impact of what we define ‘Policy Making 2.0’, which refers to ‘a set of methodologies and technological solutions aimed at enabling better, timely and participative policy-making’. Based on the analysis of these cases we suggest a bouquet of (mostly ICT-related) practical and research recommendations that are relevant to researchers, practitioners and policy makers in order to guide the introduction and implementation of Policy Making 2.0 initiatives. We argue that this ‘decalogue’ of Policy Making 2.0 could be an operational checklist for future research and policy to further explore the potential of ICT tools for governance and policy modelling, so to make next generation policy making more ‘intelligent’ and hopefully able to solve or anticipate the societal challenges we are (and will be) confronted today and in the future.
Using Crowds for Evaluation Tasks: Validity by Numbers vs. Validity by Expertise
Paper by Christoph Hienerth and Frederik Riar: “Developing and commercializing novel ideas is central to innovation processes. As the outcome of such ideas cannot fully be foreseen, the evaluation of them is crucial. With the rise of the internet and ICT, more and new kinds of evaluations are done by crowds. This raises the question whether individuals in crowds possess necessary capabilities to evaluate and whether their outcomes are valid. As empirical insights are not yet available, this paper deals with the examination of evaluation processes and general evaluation components, the discussion of underlying characteristics and mechanism of these components affecting evaluation outcomes (i.e. evaluation validity). We further investigate differences between firm- and crowd-based evaluation using different cases of applications, and develop a theoretical framework towards evaluation validity, i.e. validity by numbers vs. the validity by expertise. The identified factors that influence the validity of evaluations are: (1) the number of evaluation tasks, (2) complexity, (3) expertise, (4) costs, and (5) time to outcome. For each of these factors, hypotheses are developed based on theoretical arguments. We conclude with implications, proposing a model of evaluation validity.”
A Few Useful Things to Know about Machine Learning
A new research paper by Pedro Domingos: “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.”
Follow the money: A study of cashtags on Twitter
Paper by Martin Hentschel and Omar Alonso at FirstMonday: “The popularity of Twitter goes beyond trending topics, world events, memes, and popular hashtags. Recently a new way of sharing financial information is taking place in social media under the name of cashtags, stock ticker symbols that are prefixed with a dollar sign. In this paper we present an exploratory analysis of cashtags on Twitter. Specifically, we investigate how widespread cashtags are, what stock symbols are tweeted more often, and which users tweet about cashtags in general. We analyze relationships among cashtags and study hashtags in the context of cashtags. Finally, we compare tweet performance to stock market performance. We conclude that cashtags, in particular in combination with other cashtags or hashtags, can be very useful for analyzing financial information and provide new insights into stocks and companies.”
Behavior Analysis in Social Media
Paper by Reza Zafarani and Huan Liu in IEEE Intelligent Systems (Volume 29, Issue 4, 2014): “With the rise of social media, information sharing has been democratized. As a result, users are given opportunities to exhibit different behaviors such as sharing, posting, liking, commenting, and befriending conveniently and on a daily basis. By analyzing behaviors observed on social media, we can categorize these behaviors into individual and collective behavior. Individual behavior is exhibited by a single user, whereas collective behavior is observed when a group of users behave together. For instance, users using the same hashtag on Twitter or migrating to another social media site are examples of collective behavior. User activities on social media generate behavioral data, which is massive, expansive, and indicative of user preferences, interests, opinions, and relationships. This behavioral data provides a new lens through which we can observe and analyze individual and collective behaviors of users.”
Federalism and Municipal Innovation: Lessons from the Fight Against Vacant Properties
New Paper by Benton Martin: “Cities possess a far greater ability to be trailblazers on a national scale than local officials may imagine. Realizing this, city advocates continue to call for renewed recognition by state and federal officials of the benefits of creative local problem-solving. The goal is admirable but warrants caution. The key to successful local initiatives lies not in woolgathering about cooperation with other levels of government but in identifying potential conflicts and using hard work and political savvy to build constituencies and head off objections. To demonstrate that point, this Article examines the legal status of local governments and recent efforts to regulate vacant property through land banking and registration ordinances.”
Assessing Social Value in Open Data Initiatives: A Framework
Paper by Gianluigi Viscusi, Marco Castelli and Carlo Batini in Future Internet Journal: “Open data initiatives are characterized, in several countries, by a great extension of the number of data sets made available for access by public administrations, constituencies, businesses and other actors, such as journalists, international institutions and academics, to mention a few. However, most of the open data sets rely on selection criteria, based on a technology-driven perspective, rather than a focus on the potential public and social value of data to be published. Several experiences and reports confirm this issue, such as those of the Open Data Census. However, there are also relevant best practices. The goal of this paper is to investigate the different dimensions of a framework suitable to support public administrations, as well as constituencies, in assessing and benchmarking the social value of open data initiatives. The framework is tested on three initiatives, referring to three different countries, Italy, the United Kingdom and Tunisia. The countries have been selected to provide a focus on European and Mediterranean countries, considering also the difference in legal frameworks (civic law vs. common law countries)”
Big Data: Google Searches Predict Unemployment in Finland
Paper by Tuhkuri, Joonas: “There are over 3 billion searches globally on Google every day. This report examines whether Google search queries can be used to predict the present and the near future unemployment rate in Finland. Predicting the present and the near future is of interest, as the official records of the state of the economy are published with a delay. To assess the information contained in Google search queries, the report compares a simple predictive model of unemployment to a model that contains a variable, Google Index, formed from Google data. In addition, cross-correlation analysis and Granger-causality tests are performed. Compared to a simple benchmark, Google search queries improve the prediction of the present by 10 % measured by mean absolute error. Moreover, predictions using search terms perform 39 % better over the benchmark for near future unemployment 3 months ahead. Google search queries also tend to improve the prediction accuracy around turning points. The results suggest that Google searches contain useful information of the present and the near future unemployment rate in Finland.”
Beyond just politics: A systematic literature review of online participation
Paper by Christoph Lutz, Christian Pieter Hoffmann, and Miriam Meckel in First Monday :”This paper presents a systematic literature review of the current state–of–research on online participation. The review draws on four databases and is guided by the application of six topical search terms. The analysis strives to differentiate distinct forms of online participation and to identify salient discourses within each research field. We find that research on online participation is highly segregated into specific sub–discourses that reflect disciplinary boundaries. Research on online political participation and civic engagement is identified as the most prominent and extensive research field. Yet research on other forms of participation, such as cultural, business, education and health participation, provides distinct perspectives and valuable insights. We outline both field–specific and common findings and derive propositions for future research.”
Reddit, Imgur and Twitch team up as 'Derp' for social data research
Alex Hern in The Guardian: “Academic researchers will be granted unprecedented access to the data of major social networks including Imgur, Reddit, and Twitch as part of a joint initiative: The Digital Ecologies Research Partnership (Derp).
Derp – and yes, that really is its name – will be offering data to universities including Harvard, MIT and McGill, to promote “open, publicly accessible, and ethical academic inquiry into the vibrant social dynamics of the web”.
It came about “as a result of Imgur talking with a number of other community platforms online trying to learn about how they work with academic researchers,” says Tim Hwang, the image-sharing site’s head of special initiatives.
“In most cases, the data provided through Derp will already be accessible through public APIs,” he says. “Our belief is that there are ways of doing research better, and in a way that strongly respects user privacy and responsible use of data.
“Derp is an alliance of platforms that all believe strongly in this. In working with academic researchers, we support projects that meet institutional review at their home institution, and all research supported by Derp will be released openly and made publicly available.”
Hwang points to a Stanford paper analysing the success of Reddit’s Random Acts of Pizza subforum as an example of the sort of research Derp hopes to foster. In the research, Tim Althoff, Niloufar Salehi and Tuan Nguyen found that the likelihood of getting a free pizza from the Reddit community depended on a number of factors, including how the request was phrased, how much the user posted on the site, and how many friends they had online. In the end, they were able to predict with 67% accuracy whether or not a given request would be fulfilled.
The grouping aims to solve two problems academic research faces. Researchers themselves find it hard to get data outside of the larges social media platforms, such as Twitter and Facebook. The major services at least have a vibrant community of developers and researchers working on ways to access and use data, but for smaller communities, there’s little help provided.
Yet smaller is relative: Reddit may be a shrimp compared to Facebook, but with 115 million unique visitors every month, it’s still a sizeable community. And so Derp aims to offer “a single point of contact for researchers to get in touch with relevant team members across a range of different community sites….”