[Air-L] Friendly Reminder: Special Issue on Computational Propaganda and Political Big Data deadline coming June 1

[Gillian Bolsover]

Hi Everyone:  the submission deadline for our special issue of the journal Big Data is coming up June 1 and we are eager to see some manuscripts from this community if you feel ready to submit.  Please see below for details and be in touch with any questions about the possible topics or process.

Dr. Gillian Bolsover
Researcher
Oxford Internet Institute
University of Oxford
PGP Key: 17EC60B3

Call for Papers: Special Issue on Computational Propaganda and Political Big Data

We welcome manuscripts from scholars across the social and computer sciences, and are particularly interested in research from teams of authors from both domains of inquiry. Please submit your papers online to our web-based manuscript submission and peer-review at www.liebertpub.com/manuscript/big<http://www.liebertpub.com/manuscript/big>.

http://comprop.oii.ox.ac.uk/2017/01/17/call-for-papers-special-issue-on-computational-propaganda-and-political-big-data/

Computational propaganda—the use of information technologies for political purposes—is on the rise. Many different kinds of political actors use a wide range of computational systems, social media platforms, and big data analytics to understand and manipulate public opinion. The political use of algorithms over platforms like Twitter and Facebook has received much journalistic attention, but it can be difficult to relate the dissemination of content over social networks to changes in public opinion or voter preference. The firms behind these platforms, however, increasingly acknowledge that politically motivated algorithms and automation can have deleterious outcomes for public life. How does big data get used for political purposes? Can the behavioural impact of politically-motivated big data manipulation be measured? How does the structure, function or affordances of computational propaganda vary across platforms, issue areas, or country cases?

This Big Data special issue on Computational Propaganda and Political Big Data, scheduled for publication in December 2017, aims to advance our understanding of how the Internet can be used to spread propaganda, engage with citizens, and influence political outcomes. We welcome submissions that utilize big data or engage with methodological, theoretical, practical, and ethical issues associated with politicized use of big data. The special issue seeks to describe and discuss:

- the effects of computational propaganda, automated social actors and bots on Internet platforms, Internet users and political processes.
- measurement of the distribution and impact of fake news;
- linking, sharing, and citation structures across large numbers of voters or supporters;
- the political economy of big data mining;
- the political inferences that can be made by reverse engineering de-personalized data, analysing relational data, or assembling shadow profiles on people not represented in political data;
- the path from exposure to computational propaganda to behavioural change;
- the use of the drones, smart city sensor networks, the Internet of Things or proprietary device networks for gathering politically valuable big data.

The editors also  seek research on the computationally creative ways of mitigating the impact of computational propaganda: alert systems for identifying algorithmically-based political manipulation or high levels of automation over device networks and social media platforms;  big data driven systems for source verification or fact checking that might raise trust in computing; ways of detecting the origins of manipulative content on massive social network platforms.

The deadline for manuscript submission is June 1, 2017. We welcome manuscripts from scholars across the social and computer sciences, and are particularly interested in research from teams of authors from both domains of inquiry. Please submit your papers online to our web-based manuscript submission and peer-review at www.liebertpub.com/manuscript/big<http://www.liebertpub.com/manuscript/big>.