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.

October 2016: A Social Influence Approach for Group User Modeling in Group Recommendation Systems

posted Oct 25, 2016, 5:05 AM by Symeon Papadopoulos

Junpeng Guo, Yanlin Zhu, Aiai Li, Qipeng Wang, Weiguo Han, "A Social Influence Approach for Group User Modeling in Group Recommendation Systems", IEEE Intelligent Systems, vol. 31, no. , pp. 40-48, Sept.-Oct. 2016, doi:10.1109/MIS.2016.28

Abstract: While many studies on typical recommender systems focus on making recommendations to individual users, social activities usually involve groups of users. Issues related to group recommendations are increasingly becoming hot research topics. Among the differences between individual and group recommender systems, the most significant is social factors of group users. Social factors, including personality, expertise factor, interpersonal relationships, and preference similarities, widen the gap between group and individual recommendations. A new approach focuses on the impact of social factors on group recommender systemsâa computational model integrating the influences of personality, expertise factor, interpersonal relationships, and preference similarities. Comparative experiments are conducted on two datasets. The experimental results show that the proposed approach can provide more accurate and satisfactory group recommendations, especially when social influences are significant.

September 2016: Semantic Filtering for Social Data

posted Sep 24, 2016, 8:48 AM by Symeon Papadopoulos

Amit Sheth, Pavan Kapanipathi, "Semantic Filtering for Social Data", IEEE Internet Computing, vol. 20, no.4, pp. 74-78, July-Aug. 2016, doi:10.1109/MIC.2016.86

Abstract: More than a billion users on the Web are on social networks sharing and consuming short and real-time updates. Consumers of social data face information overload. Although information filtering can help, challenges that are specific to the short-text and real-time nature of social networks must be addressed. Knowledge bases -- particularly those derived from crowd-sourced platforms such as Wikipedia -- can be harnessed for building an intelligent and effective information-filtering system for social networks.

August 2016: Itrust - interpersonal trust measurements from social interactions

posted Aug 18, 2016, 12:42 AM by Symeon Papadopoulos

Xiaoming Li, Qing Yang, Xiaodong Lin, Shaoen Wu, Mike Wittie, "Itrust: interpersonal trust measurements from social interactions", IEEE Network, vol. 30, no. 4, pp. 54-58, Jul-Aug 2016, doi: 10.1109/MNET.2016.7513864

Abstract: Interpersonal trust is widely cited as an important component in several network systems such as peer-to-peer networks, e-commerce, and semantic web. However, there has been less research on measuring interpersonal trust due to the difficulty of collecting data that accurately reflect interpersonal trust. Currently, friends of a user in almost all OSNs are indistinguishable, that is, there is no explicit indication of the strength of trust between a user and his/her close friends, as opposed to acquaintances. To address this issue, we quantify interpersonal trust by analyzing the social interaction frequencies between users and their friends on Facebook. We consider bidirectional interacting data in OSNs to deconstruct a user's social behavior and apply principal component analysis to estimate interpersonal trust. A Facebook app, itrust, is developed to collect interaction data and calculate interpersonal trust. Results show that itrust achieves more accurate interpersonal trust measurements than existing methods.

July 2016: Social Diffusion Analysis With Common-Interest Model for Image Annotation

posted Jul 11, 2016, 9:05 AM by Symeon Papadopoulos

Chenyi Lei, Dong Liu, Weiping Li, "Social Diffusion Analysis With Common-Interest Model for Image Annotation", Transactions on Multimedia, vol. 18, no. 4, pp. 687-701, Apr. 2016, doi: 

Abstract: Automatic image annotation has been extensively studied, mostly from a content-based approach, whose effectiveness is restricted by the “semantic gap” between low-level image features and semantic annotations, and by the irrelevance of annotations to image content. We propose a social diffusion analysis approach to image annotation, which exploits abundant social diffusion records about how images are disseminated within online social networks. Specifically, we propose a common-interest model to analyze social diffusion records, with the assumption that the diffusion pattern of an image in social networks is highly related to the relevance between image annotations and user preferences. In our proposed model, user preferences are represented as common interests of pairwise users instead of individual user interests. We find the notion of common interests not only facilitates the analysis of social diffusion patterns, but also leads to more accurate profiling of user preferences compared to individual interests. Based on the common-interest model, we design an image annotation framework via social diffusion analysis, which consists of the mining of common interests from social diffusion records, the feature extraction from diffusion graphs and common interests, and the automatic annotation by the learning-to-rank method. Experimental results on real-world data sets show that our proposed common-interest based approach outperforms individual-interest based methods, and also achieves superior performance than state-of-the-art content-based image annotation methods.

June 2016: Spammers Are Becoming "Smarter" on Twitter

posted Jun 23, 2016, 9:32 AM by Symeon Papadopoulos

Chao Chen, Jun Zhang, Yang Xiang, Wanlei Zhou, Jonathan Oliver, "Spammers Are Becoming "Smarter" on Twitter", IT Professional, vol.18, no. 2, pp. 66-70, Mar.-Apr. 2016, doi:10.1109/MITP.2016.36

While researchers develop various approaches to detect Twitter spam, spammers thwart their efforts with more complex spamming strategies. The authors' in-depth analysis of more than 570 million tweets revealed three new spamming strategies: coordinated posting behavior, finite-state machine-based spam template, and passive spam.

May 2016: How Socially Aware Are Social Media Privacy Controls?

posted May 12, 2016, 5:31 AM by Symeon Papadopoulos

Gaurav Misra, Jose M. Such, "How Socially Aware Are Social Media Privacy Controls?", Computer, vol.49, no. 3, pp. 96-99, Mar. 2016, doi:10.1109/MC.2016.83

 Social media sites are key mediators of online communication. Yet the privacy controls for these sites are not fully socially aware, even when privacy management is known to be fundamental to successful social relationships.

April 2016: Whither Social Media Governance?

posted Apr 20, 2016, 5:23 AM by Symeon Papadopoulos

Virgilio A.F. Almeida, Danilo Doneda, Yasodara Cordova, "Whither Social Media Governance?", IEEE Internet Computing, vol.20, no. 2, pp. 82-84, Mar.-Apr. 2016, doi:10.1109/MIC.2016.32

Abstract: Due to their enormous popularity, social network platforms such as Facebook, YouTube, and Twitter are constantly viewed as stand-ins for public spaces. Although they have their own rules, there are ethical aspects that demand governance to guarantee their compliance with human rights. This article seeks to explore the essentials that could impact social networks' governance, drawing attention to aspects of possible solutions.

March 2016: Distributed resource allocation in cloud-based wireless multimedia social networks

posted Mar 20, 2016, 5:57 AM by Symeon Papadopoulos

Guofang Nan, Zhifei Mao, Minqiang Li, Yang Zhang, Stein Gjessing, Honggang Wang, Mohsen Guizani, "Distributed resource allocation in cloud-based wireless multimedia social networks", IEEE Network, vol. 28, no. 4, pp. 74-80, Jul.-Aug. 2014, doi: 10.1109/MNET.2014.6863135

Abstract: With the rapid penetration of mobile devices, more users prefer to watch multimedia live-streaming via their mobile terminals. Quality of service provision is normally a critical challenge in such multimedia sharing environments. In this article, we propose a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution. We first motivate the use of cloud computing and social contexts in sharing live streaming. Then our WMSN architecture is presented with the description of the different components of the network. After that, we focus on distributed resource management and formulate the bandwidth allocation problem in a gametheoretical framework that is further implemented in a distributed manner. In addition, we note the potential selfish behavior of mobile users for resource competition and propose a cheat-proof mechanism to motivate mobile users to share bandwidth. Illustrative results demonstrate the best responses of different users in the game equilibrium as well as the effectiveness of the proposed cheating avoidance scheme.

February 2016: Trust Agent-Based Behavior Induction in Social Networks

posted Feb 17, 2016, 8:18 AM by Symeon Papadopoulos

Lei Li, Jianping He, Meng Wang, Xindong Wu, "Trust Agent-Based Behavior Induction in Social Networks", IEEE Intelligent Systems, vol.31, no. 1, pp. 24-30, Jan.-Feb. 2016, doi:10.1109/MIS.2016.6

Abstract: The essence of social networks is that they can influence people's public opinions and group behaviors form quickly. Negative group behavior influences societal stability significantly, but existing behavior-induction approaches are too simple and inefficient. To automatically and efficiently induct behavior in social networks, this article introduces trust agents and designs their features according to group behavior features. In addition, a dynamics control mechanism can be generated to coordinate participant behaviors in social networks to avoid a specific restricted negative group behavior.

January 2016: Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks

posted Jan 20, 2016, 12:22 PM by Symeon Papadopoulos

Xiaoping Zhou, Xun Liang, Haiyan Zhang, Yuefeng Ma, "Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks", IEEE Transactions on Knowledge & Data Engineering, vol.28, no. 2, pp. 411-424, Feb. 2016, doi:10.1109/TKDE.2015.2485222

Abstract: The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online social media network (SMN) platforms. Recognizing anonymous, yet identical users among multiple SMNs is still an intractable problem. Clearly, cross-platform exploration may help solve many problems in social computing in both theory and applications. Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users’ public profiles, are fragile. Some studies have attempted to match users based on the location and timing of user content as well as writing style. However, the locations are sparse in the majority of SMNs, and writing style is difficult to discern from the short sentences of leading SMNs such as Sina Microblog and Twitter. Moreover, since online SMNs are quite symmetric, existing user identification schemes based on network structure are not effective. The real-world friend cycle is highly individual and virtually no two users share a congruent friend cycle. Therefore, it is more accurate to use a friendship structure to analyze cross-platform SMNs. Since identical users tend to set up partial similar friendship structures in different SMNs, we proposed the Friend Relationship-Based User Identification (FRUI) algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and only UMPs with top ranks are considered as identical users. We also developed two propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better than current network structure-based algorithms.

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