Featured Articles is a section of the Special Technical Community on Social Networks that will shed light to pieces of social network research that are considered interesting and promising. Each month one research article will be selected from the IEEE Computer Society Digital Library to be featured in this section. The content featured here will be curated by Symeon Papadopoulos. Eventually, the featured articles will become freely available from this page.
Daniele Quercia, Diego Saez, "Mining Urban Deprivation from Foursquare: Implicit Crowdsourcing of City Land Use," IEEE Pervasive Computing, vol. 13, no. 2, pp. 30-36, Apr.-June. 2014, doi:10.1109/MPRV.2014.31
Abstract: Research has shown a relationship between the physical characteristics of a city neighborhood (such as the presence of playgrounds and fast-food outlets) and neighborhood deprivation as defined in socioeconomic indices. Official land-use data has often been the source for such research. This article examines the viability of using social-networking data as an alternative source. The authors study all venues on the Foursquare location-mapping application across a variety of London census areas. They study the relationship between the presence of different venues in an area and its score on the socioeconomic Index of Multiple Deprivation. They conclude that knowing which venues are hosted by which community offers not only insights into neighborhood deprivation but also a reasonable way of predicting community deprivation scores at fine-grained temporal resolutions. This article is part of a special issue on pervasive analytics and citizen science.
Symeon Papadopoulos, Yiannis Kompatsiaris, "Social Multimedia Crawling for Mining and Search," Computer, vol. 47, no. 5, pp. 84-87, May 2014, doi:10.1109/MC.2014.135
Abstract: Social multimedia can be leveraged for a wide range of applications, but mining and search systems require innovative crawling solutions to meet both technical and policy-related obstacles.
Pinghui Wang, Wenbo He, Junzhou Zhao, "A Tale of Three Social Networks: User Activity Comparisons across Facebook, Twitter, and Foursquare," IEEE Internet Computing, vol. 18, no. 2, pp. 10-15, Mar.-Apr. 2014, doi:10.1109/MIC.2013.128
Abstract: Despite recent efforts to characterize online social network (OSN) structures and activities, user behavior across different OSNs has received little attention. Yet such information could provide insight into issues relating to personal privacy protection. For instance, many Foursquare users reveal their Facebook and Twitter accounts to the public. The authors' in-depth measurement study examines users' network activities and privacy settings across Facebook, Twitter, and Foursquare. Results show that user activities are highly correlated among these three OSNs, which causes information leakage for a large fraction of Foursquare users.
Wenyuan Yin, Tao Mei, Chang Wen Chen, Shipeng Li, "Socialized Mobile Photography: Learning to Photograph With Social Context via Mobile Devices," IEEE Transactions on Multimedia, vol. 16, no. 1, pp. 184-200, Jan 2014, doi:10.1109/TMM.2013.2283468
Abstract: The popularity of mobile devices equipped with various cameras has revolutionized modern photography. People are able to take photos and share their experiences anytime and anywhere. However, taking a high quality photograph via mobile device remains a challenge for mobile users. In this paper we investigate a photography model to assist mobile users in capturing high quality photos by using both the rich context available from mobile devices and crowdsourced social media on the Web. The photography model is learned from community-contributed images on the Web, and dependent on user's social context. The context includes user's current geo-location, time (i.e., time of the day), and weather (e.g., clear, cloudy, foggy, etc.). Given a wide view of scene, our socialized mobile photography system is able to suggest the optimal view enclosure (composition) and appropriate camera parameters (aperture, ISO, and exposure time). Extensive experiments have been performed for eight well-known hot spot landmark locations where sufficient context tagged photos can be obtained. Through both objective and subjective evaluations, we show that the proposed socialized mobile photography system can indeed effectively suggest proper composition and camera parameters to help the user capture high quality photos.
Sami Vihavainen, Airi Lampinen, Antti Oulasvirta, Suvi Silfverberg, Asko Lehmuskallio, "The Clash between Privacy and Automation in Social Media," IEEE Pervasive Computing, vol. 13, no. 1, pp. 56-63, Jan.-Mar. 2014, doi:10.1109/MPRV.2013.25
Abstract: Classic research on human factors has found that automation never fully eliminates the human operator from the loop. Instead, it shifts the operator's responsibilities to the machine and changes the operator's control demands, sometimes with adverse consequences, called the "ironies of automation." In this article, the authors revisit the problem of automation in the era of social media, focusing on privacy concerns. Present-day social media automatically discloses information, such as users' whereabouts, likings, and undertakings. This review of empirical studies exposes three recurring privacy-related issues in automated disclosure: insensitivity to situational demands, inadequate control of nuance and veracity, and inability to control disclosure with service providers and third parties. The authors claim that "all-or-nothing" automation has proven problematic and that social network services should design their user controls with all stages of the disclosure process in mind.
Kieron O'Hara, "Are We Getting Privacy the Wrong Way Round?," IEEE Internet Computing, vol. 17, no. 4, pp. 89-92, July-Aug. 2013, doi:10.1109/MIC.2013.62
Abstract: Individualists, communitarians, and technological determinists agree that privacy's benefits accrue to individuals, and that its costs (in terms of less security or efficiency) fall on society. As such, it is the individual's choice to give privacy away. However, privacy does benefit wider society in important respects, and so this consensus is flawed.
Ting Hua, Chang-Tien Lu, Naren Ramakrishnan, Feng Chen, Jaime Arredondo, David Mares, Kristen Summers, "Analyzing Civil Unrest through Social Media," Computer, vol. 46, no. 12, pp. 80-84, Dec. 2013, doi:10.1109/MC.2013.442
Abstract: Mining and analyzing data from social networks such as Twitter can reveal new insights into the causes of civil disturbances, including trigger events and the role of political entrepreneurs and organizations in galvanizing public opinion.
Maria R. Lee, Tsung Teng Chen, "Understanding Social Computing Research," IT Professional, vol. 15, no. 6, pp. 56-62, Nov.-Dec. 2013, doi:10.1109/MITP.2012.121
Abstract: Social computing is an emerging field, encompassing a wide range of topics. A broad understanding of the major topics involved in social computing is important for both scholars and practitioners. The authors present and analyze the voluminous social computing related studies to date, applying document co-citation analysis, pathfinder networks, core-document analysis, and the Herfindahl-Hirschman index. The results not only provide insight to this subject area but also afford a conduit for the future research in this discipline.
Jianwei Niu, Jing Peng, Lei Shu, Chao Tong, Wanjiun Liao, "An Empirical Study of a Chinese Online Social Network--Renren," Computer, vol. 46, no. 9, pp. 78-84, Sept. 2013, doi:10.1109/MC.2013.1
Abstract: Deeper knowledge of social networks' structure and temporal evolution enhances data mining for both research and education purposes. An empirical analysis of a Chinese social network, Renren, shows that it follows an exponentially truncated power law in degree distribution, and has a short average node distance.
Wei Tan, M. Brian Blake, Iman Saleh, Schahram Dustdar, "Social-Network-Sourced Big Data Analytics," IEEE Internet Computing, vol. 17, no. 5, pp. 62-69, Sept.-Oct. 2013, doi:10.1109/MIC.2013.100
Abstract: Very large datasets, also known as big data, originate from many domains, including healthcare, energy, weather, business, and social networks. Deriving knowledge is more difficult than ever when we must do it by intricately processing big data. Organizations rely on third-party, commodity computing resources or clouds to gather the computational resources required to manipulate data of this magnitude. Although social networks are perhaps among the largest big data producers, the collaboration that results from leveraging this paradigm could help to solve big data processing challenges. Here, the authors explore using personal ad hoc clouds comprised of individuals in social networks to address such challenges.