Featured Articles

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.

April 2014: Socialized Mobile Photography

posted Apr 12, 2014, 9:21 AM by Symeon Papadopoulos

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. 

March 2014: The Clash between Privacy and Automation in Social Media

posted Mar 12, 2014, 12:13 AM by Symeon Papadopoulos

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.

February 2014: Are We Getting Privacy the Wrong Way Round?

posted Feb 10, 2014, 8:48 AM by Symeon Papadopoulos

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.

January 2014: Analyzing Civil Unrest through Social Media

posted Jan 11, 2014, 7:49 AM by Symeon Papadopoulos

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.

December 2013: Understanding Social Computing Research

posted Dec 10, 2013, 9:50 AM by Symeon Papadopoulos

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.

November 2013: An Empirical Study of a Chinese Online Social Network Renren

posted Nov 10, 2013, 3:02 PM by Symeon Papadopoulos

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.

October 2013: Social-Network-Sourced Big Data Analytics

posted Oct 10, 2013, 11:28 AM by Symeon Papadopoulos

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.

September 2013: Sensing Trending Topics in Twitter

posted Sep 17, 2013, 8:37 AM by Symeon Papadopoulos

Luca Maria Aiello, Giorgos Petkos, Carlos Martin, David Corney, Symeon Papadopoulos, Ryan Skraba, Ayse Goker, Ioannis Kompatsiaris, Alejandro Jaimes, "Sensing Trending Topics in Twitter," IEEE Transactions on Multimedia, vol. 15, no. 6, pp. 1268-1282, June 2013, doi:10.1109/TMM.2013.2265080

Abstract: Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on n-grams cooccurrence and df-idft topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.

August 2013: Social Networking Privacy: Understanding the Disconnect from Policy to Controls

posted Aug 16, 2013, 3:42 AM by Symeon Papadopoulos

Pauline Anthonysamy, Phil Greenwood, Awais Rashid, "Social Networking Privacy: Understanding the Disconnect from Policy to Controls," Computer, vol. 46, no. 6, pp. 60-67, June 2013, doi:10.1109/MC.2012.326

Abstract: A proposed method for mapping privacy policy statements to privacy controls can help providers improve data management transparency, thereby increasing user trust.

July 2013: Bayesian-Inference-Based Recommendation in Online Social Networks

posted Jul 10, 2013, 10:16 AM by Symeon Papadopoulos

Xiwang Yang, Yang Guo, Yong Liu, "Bayesian-Inference-Based Recommendation in Online Social Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 4, pp. 642-651, April 2013, doi:10.1109/TPDS.2012.192

Abstract: In this paper, we propose a Bayesian-inference-based recommendation system for online social networks. In our system, users share their content ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a content rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop distributed protocols that can be easily implemented in online social networks. We further propose to use Prior distribution to cope with cold start and rating sparseness. The proposed algorithm is evaluated using two different online rating data sets of real users. We show that the proposed Bayesian-inference-based recommendation is better than the existing trust-based recommendations and is comparable to Collaborative Filtering (CF) recommendation. It allows the flexible tradeoffs between recommendation quality and recommendation quantity. We further show that informative Prior distribution is indeed helpful to overcome cold start and rating sparseness.

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