Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201

Discovering Polarized Communities in Signed Networks

Signed networks contain edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge). The model is simple but powerful and it can capture novel and interesting structural properties of real-world phenomena. The analysis of signed networks has many applications from modeling discussions in social media, to mining user reviews, and to recommending products in e-commerce sites. In this paper we consider the problem of discovering polarized communities in signed networks. In particular, we search for two communities (subsets of the network vertices) where within communities there are mostly positive edges while across communities there are mostly negative edges. We formulate this novel problem as a "discrete eigenvector" problem, which we show to be NP-hard. We then develop two intuitive spectral algorithms: one deterministic, and one randomized with quality guarantee $\sqrt{n}$ (where $n$ is the number of vertices in the graph), tight up to constant factors. We validate our algorithms against non-trivial baselines on real-world signed networks. Our experiments confirm that our algorithms produce higher quality solutions, are much faster and can scale to much larger networks than the baselines, and are able to detect ground-truth polarized communities

Language, twitter and academic conferences

Using Twitter during academic conferences is a way of engaging and connecting an audience inherently multicultural by the nature of scientific collaboration. English is expected to be the lingua franca bridging the communication and integration between native speakers of different mother tongues. However, little research has been done to support this assumption. In this paper we analyzed how integrated language communities are by analyzing the scholars’ tweets used in 26 Computer Science conferences over a time span of five years. We found that although English is the most popular language used to tweet during conferences, a significant proportion of people also tweet in other languages. In addition, people who tweet solely in English interact mostly within the same group (English monolinguals), while people who speak other languages interact more with different lingua groups. Finally, we also found higher interaction between people tweeting in different languages.These results suggest a relation between the number of languages a user speaks and their interaction dynamics in online communitie

Exploring new digital affordances of city life

First Online: 14 November 2019Digital services are becoming an integral part of the city fabric and are increasingly central to the ways in which we collectively experience cities. In this work, we are particularly concerned with the infrastructural elements that cities may offer to enable a deeper connection with their inhabitants. Our research goal is to uncover emerging signs of new Urban Digital Affordances that are making their way into the city fabric and changing behaviours of citizens and visitors. Based on the results of a photo survey involving 4 European cities, we have identified 5 emerging concepts of physical-digital services in urban spaces: Locative, Anchors, Hybrids, False Hybrids and Digital Counterparts. Understanding the properties of these engagement concepts can help cities to be much more efficient in promoting creative uses of digital technologies in urban space. It may also help to develop new infrastructures that explore these same principles to accomplish new forms of citizen engagement and move beyond ephemeral high-profile installations or generic and basic infrastructures.(undefined

The use of big mobile data to gain multilayered insights for Syrian refugee crisis

This study aims to shed light on various aspects of refugees’ lives in Turkey using mobile call data records of Türk Telekom, enriched with numerous local data sets. To achieve this, we made use of several statistical and data mining techniques in addition to a novel methodology to find home and work-time anchors of mobile phone users we developed. Our results showed that refugees are highly mobile as a survival strategy—a significant number of whom work as seasonal workers. Most prefer to live in relatively low-status neighborhoods, close to city transport links and fellow refugees. The ones living in these neighborhoods appear to be introverts, living in a closed neighborhood. However, the middle- and upper-class refugees appear to be the opposite. Fatih, İstanbul was found as an important hub for refugees. Finally, the officially registered refugee numbers do not reflect the real refugee population in Turkey. Due to their high mobility, refugees lag behind in keeping up-to-date information about their residential address, resulting in a significant discrepancy between the official numbers and the real numbers. We think that policymakers can benefit from the proposed methods in this study to develop real-time solutions for the well-being of refugees

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

When coupled with spatio-temporal context, location-based data collected in mobile cellular networks provide insights into patterns of human activity, interactions, and mobility. Whilst uncovered patterns have immense potential for improving services of telecom providers as well as for external applications related to social wellbeing, its inherent massive volume make such ‘Big Data’ sets complex to process. A significant number of studies involving such mobile phone data have been presented, but there still remain numerous open challenges to reach technology readiness. They include efficient access in privacy-preserving manner, high performance computing environments, scalable data analytics, innovative data fusion with other sources–all finally linked into the applications ready for operational mode. In this chapter, we provide a broad overview of the entire workflow from raw data access to the final applications and point out the critical challenges in each step that need to be addressed to unlock the value of data generated by mobile cellular networks