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PP 348

Structuring Civic Engagement Through Data: How Civic Tech Is Shaping Citizenship

S. Baack

University of Groningen, Research Centre for Media and Journalism Studies, Groningen, Netherlands

Civic technologists develop tools that aim to solve civic problems by improving government services or by empowering citizens, for example with parlia‑

mentary monitoring websites or tools that help citizens to submit freedom of information requests. Even though the civic tech sector has grown substan‑

tially in recent years as it has been embraced by foundations, companies and even some governments, we know very little about the practices and values

promoted by civic tech applications, and how they might influence the relationship between citizens and their governments more broadly. This paper will

provide answers to these questions by presenting findings from a qualitative case study (interviews and content analysis) about the practices and values

of civic tech at mySociety

(www.mysociety.org)

, an NGO from the UK. mySociety is one of the oldest and most influential civic tech organizations today that

has pioneered many civic tech applications which are now considered standard, for example the parliamentary monitoring website TheyWorkForYou.com

or FixMyStreet.com, which helps citizens to report issues to local governments. Using detailed examples from this case study, it is suggested that civic tech

is primarily about resolving problems of scale through structured data. It aims at making civic engagement easier and more straightforward for citizens

by ‘translating’the bureaucratic and legal procedures followed by governments into user-friendly interfaces and accessible language. This translation does

not only depend on the affordances of structured data, i.e. on the ability to reorganize information through granular filtering, but on structuring data, on

developing a structure to organize information and on putting data into that structure. This process requires careful negotiations between the structure

of the data and the real-world processes this structure is supposed to represent. Drawing on Scott (1998), I will show how the data structures created and

utilized by civic tech applications are not just technical or static, but social and performative: they do not merely aim to describe but to shape the rela‑

tionship between citizens and their governments. I suggest that the type of citizenship promoted by civic tech comes close to what Schudson (1998) has

described as ‘monitorial citizenship’. I will provide some reflections on how civic tech supports monitorial citizens, but also how it emphasizes the more

problematic aspects of this type of citizenship. This paper offers new insights into the growing phenomenon of civic tech and shows how researching civic

tech is important for understanding how publics are being reconfigured with the‘datafication’of social life by demonstrating how civic tech applications are

facilitating civic engagement through data. It also provides a useful starting point to further explore and compare civic tech communities around the world

by focusing on one of the oldest and most influential civic tech organizations. References Schudson, M. (1998). The good citizen: A history of American civic

life. New York: Martin Kessler Books. Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed. New Haven:

Yale University Press.

PP 349

Understanding Smartphone’s Logs in the Era of Big Data

A. Rosales

, M. Fernández-Ardèvol

Universitat Oberta de Catalunya, Internet Interdisciplinary Institute, Castelldefels, Spain

The emergence of digital communication systems opens the door for tracking everyday activities. This lead to the so called era of Big data [1]. Terabytes

of data created by real life users in their everyday life activities that can be analyzed to understand human behavior. Big data is used in marketing, political,

communication and technological studies among others. However, it is not exempt of privacy, ethical and political concerns. Big opportunities and big

challenges come with. Tracked data are often seen as more objective data [3, 4], as they report what people actually do in their everyday settings. They are

often used together with qualitative studies, as each kind of data complement each other [5]. While it is meant to provide information about what people

do on their digital devices, they can not provide information about why people use them in different ways. In consequence, interpretations about what

people do based on tracked data are also challenging and limited. We focus on smartphones as they have come to be the most relevant communication and

entertainment device in Spain [6]. In addition, when smartphone’s companies allowed the development of third part apps, the possibility to track smart‑

phone activities became a research tool. However, all tracking systems depend on the information accessible in the operating systems, thus all of them have

access to the same information in each operating system. We aim at a better understanding of the complexity of analyzing tracked logs, and discuss some

guidelines both to understand and face big data studies. We base our analysis on a) our failures in previous studies pretending to analyze smartphone logs,

b) a literature review of papers reporting studies with smartphone logs, and c) the analysis of our own experience with tracked smartphones. Our analysis

highlight two issues related with the validity and reliability of the data. On the one hand, the valid interpretation of the time an app is used–because an app

can remain open when the user is not interacting with it. And on the other, the need of a reliable systematized approach to categorize apps –as categories

should be meaningful and replicable. Smartphone logs are easy to access but complex to use in human behavior studies. They can be used in comparative

statistics but possible biases must be taken into account. Conversely, logs seem to be less problematic in technical studies because the focus is mainly on

how the system works than on how individuals use digital devices. References 1. Boyd, D., Crawford, K.: Critical Questions for Big Data. Information, Com‑

mun. Soc. 15, 662–679 (2012). 3. Karikoski, J., Soikkeli, T.: Contextual usage patterns in smartphone communication services. Pers. Ubiquitous Comput.

17, 491–502 (2013). 4. Möller, A., Kranz, M., Schmid, B., Roalter, L., Diewald, S.: Investigating self-reporting behavior in long-term studies. Proc. CHI ’13.

2931–2940 (2013). 5. Ormen, J.,Thorhauge, a. M.: Smartphone log data in a qualitative perspective. Mob. Media Commun. (2015). 6. Clarke, J., Montesinos,

M., Montanera, R., Bermúdez, A.: Estudio Mobile. (2015).