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.

November 2015: Trust Networks: Topology, Dynamics, and Measurements

posted Nov 10, 2015, 9:54 AM by Symeon Papadopoulos

Santa Agreste, Pasquale De Meo, Emilio Ferrara, Sebastiano Piccolo, Alessandro Provetti, "Trust Networks: Topology, Dynamics, and Measurements", IEEE Internet Computing, vol.19, no. 6, pp. 26-35, Nov.-Dec. 2015, doi:10.1109/MIC.2015.93

Abstract: Thanks to the availability of user profiles and records of activity, online social network analysis can discover complex individual and social behavior patterns. The emergence of trust between users of online services is one of the most important phenomena, but it's also hard to detect in records of users' interactions, and even harder to replicate by abstract, generative models. Here, the authors investigate the emergence of "trusted" users (over time) by studying the evolution of topological and centrality measures of the network of trust within the overall social network. To do so, large datasets of user activity are studied from Ciao and Epinions (two online platforms with an explicit notion of trust controlled by users), and Prosper (a micro-lending site where trust remains implicit). The implications of such findings are discussed, particularly regarding how to enable trust in online platforms and interaction, with implications for trust-based activities.

October 2015: Tracking Temporal Community Strength in Dynamic Networks

posted Oct 15, 2015, 11:04 AM by Symeon Papadopoulos

Nan Du, Xiaowei Jia, Jing Gao, Vishrawas Gopalakrishnan, Aidong Zhang, "Tracking Temporal Community Strength in Dynamic Networks", IEEE Transactions on Knowledge & Data Engineering, vol.27, no. 11, pp. 3125-3137, Nov. 2015, doi:10.1109/TKDE.2015.2432815

Abstract: Community formation analysis of dynamic networks has been a hot topic in data mining which has attracted much attention. Recently, there are many studies which focus on discovering communities successively from consecutive snapshots by considering both the current and historical information. However, these methods cannot provide us with much historical or successive information related to the detected communities. Different from previous studies which focus on community detection in dynamic networks, we define a new problem of tracking the progression of the community strength—a novel measure that reflects the community robustness and coherence throughout the entire observation period. To achieve this goal, we propose a novel framework which formulates the problem as an optimization task. The proposed community strength analysis also provides foundation for a wide variety of related applications such as discovering how the strength of each detected community changes over the entire observation period. To demonstrate that the proposed method provides precise and meaningful evolutionary patterns of communities which are not directly obtainable from traditional methods, we perform extensive experimental studies on one synthetic and five real datasets: Social evolution, tweeting interaction, actor relationships, bibliography, and biological datasets. Experimental results show that the proposed approach is highly effective in discovering the progression of community strengths and detecting interesting communities.

September 2015: Model-Based Forecasting of Significant Societal Events

posted Sep 17, 2015, 9:46 AM by Symeon Papadopoulos

Naren Ramakrishnan, Chang-Tien Lu, Madhav Marathe, Achla Marathe, Anil Vullikanti, Stephen Eubank, Scotland Leman, Michael Roan, John S. Brownstein, Kristen Summers, Lise Getoor, Aravind Srinivasan, Tanzeem Choudhury, Dipak Gupta, David Mares, "Model-Based Forecasting of Significant Societal Events", IEEE Intelligent Systems, vol.30, no. 5, pp. 86-90, Sept.-Oct. 2015, doi:10.1109/MIS.2015.74

Abstract: The authors discuss Embers's design, system architecture, and user interface and compare its forecasts to a master set of events called the gold standard report.

August 2015: Cross-Platform Social Event Detection

posted Aug 19, 2015, 5:47 AM by Symeon Papadopoulos

Maia Zaharieva, Manfred Del Fabro, Matthias Zeppelzauer, "Cross-Platform Social Event Detection", IEEE MultiMedia, vol.22, no. 3, pp. 14-25, July-Sept. 2015, doi:10.1109/MMUL.2015.31

Abstract: A large part of media shared on online platforms such as Flickr and YouTube is captured at various social events (such as music festivals, exhibitions, and sport events). While it is quite easy to share personal impressions online, it is much more challenging to identify content that is related to the same social event across different platforms. Here, the authors focus on the detection of social events in a data collection from Flickr and YouTube. They propose an unsupervised, multistaged approach that explores commonly available, real-world metadata for the detection and linking of social events across sharing platforms. The proposed methodology and the performed experiments allow for a thorough evaluation of the usefulness of available metadata in the context of social event detection in both single- and cross-platform scenarios. This article is part of a special issue on social multimedia and storytelling.

July 2015: Detecting and Preventing Online Identity Deception in Social Networking Services

posted Jul 10, 2015, 9:03 AM by Symeon Papadopoulos

Michail Tsikerdekis, Sherali Zeadally, "Detecting and Preventing Online Identity Deception in Social Networking Services", IEEE Internet Computing, vol.19, no. 3, pp. 41-49, May-June 2015, doi:10.1109/MIC.2015.21

Abstract: In the past decade social networking services (SNS) flooded the Web. The nature of such sites makes identity deception easy, offering a quick way to set up and manage identities, and then connect with and deceive others. Fighting deception requires a coordinated approach by users and developers to ensure detection and prevention. This article identifies the most prevalent approaches in detecting and preventing identity deception (from both a user's and developer's perspective), evaluating their efficiency, and providing recommendations to help eradicate this issue.

June 2015: Enhancing Cocreation Using Social Media

posted Jun 18, 2015, 3:09 AM by Symeon Papadopoulos

Shitalkumar Halale, G.R. Gangadharan, Lorna Uden, "Enhancing Cocreation Using Social Media", IT Professional, vol.17, no. 2, pp. 40-45, Mar.-Apr. 2015, doi:10.1109/MITP.2015.22

Abstract: Innovation is essential for firms to survive in today's global economy. Cocreation is a strategy where customers and providers participate in the innovation process. Social media is a perfect tool for cocreating value because it promotes communication between the organization and the customer. It's fast becoming an integral part of business operations and has many important uses, including innovation. This article explores the use of social media in cocreating business value. The authors' study provides a conceptual framework for understanding core elements and processes of cocreation using social media. Using the conceptual framework, they examine and analyze case studies from IBM and SAP in the context of use of social media in cocreation.

May 2015: Predicting Elections for Multiple Countries Using Twitter and Polls

posted May 10, 2015, 11:41 AM by Symeon Papadopoulos

Adam Tsakalidis, Symeon Papadopoulos, Alexandra I. Cristea, Yiannis Kompatsiaris, "Predicting Elections for Multiple Countries Using Twitter and Polls", IEEE Intelligent Systems, vol.30, no. 2, pp. 10-17, Mar.-Apr. 2015, doi:10.1109/MIS.2015.17

Abstract: The authors' work focuses on predicting the 2014 European Union elections in three different countries using Twitter and polls. Past works in this domain relying strictly on Twitter data have been proven ineffective. Others, using polls as their ground truth, have raised questions regarding the contribution of Twitter data for this task. Here, the authors treat this task as a multivariate time-series forecast, extracting Twitter- and poll-based features and training different predictive algorithms. They've achieved better results than several past works and the commercial baseline.

April 2015: Privacy Concerns for Photo Sharing in Online Social Networks

posted Apr 16, 2015, 9:14 AM by Symeon Papadopoulos

Kaitai Liang, Joseph K. Liu, Rongxing Lu, Duncan S. Wong, "Privacy Concerns for Photo Sharing in Online Social Networks", IEEE Internet Computing, vol.19, no. 2, pp. 58-63, Mar.-Apr. 2015, doi:10.1109/MIC.2014.107

Abstract: As wireless networks flourish, Internet users can access social network platforms (such as Facebook and Twitter) through personal electronic devices anywhere and anytime. However, because users often deploy social network platforms in a public network setting, a common concern remains about how to guarantee privacy for photo sharing. Although most platforms aim to protect such privacy, few are able to reach the goal. This work focuses on an interesting potential privacy risk, called the deletion delay of photo sharing, by pinpointing and investigating the risk's existence in some well-known social network platforms.

March 2015: Graph-Based Residence Location Inference for Social Media Users

posted Mar 10, 2015, 11:20 AM by Symeon Papadopoulos

Dan Xu, Peng Cui, Wenwu Zhu, Shiqiang Yang, "Graph-Based Residence Location Inference for Social Media Users", IEEE MultiMedia, vol.21, no. 4, pp. 76-83, Oct.-Dec. 2014, doi:10.1109/MMUL.2014.62

Abstract: Location information in social media is becoming increasingly vital in applications such as election prediction, epidemic forecasting, and emergency detection. However, only a tiny proportion of users proactively share their residence locations (which can be used to approximate the locations of most user-generated content) in their profiles, and inferring the residence location of the remaining users is nontrivial. In this article, the authors propose a framework for residence location inference in social media by jointly considering social, visual, and textual information. They first propose a data-driven approach to explore the use of friendship locality, social proximity, and content proximity for geographically nearby users. Based on these observations, they then propose a location propagation algorithm to effectively infer residence location for social media users. They extensively evaluate the proposed method using a large-scale real dataset and achieve a 15 percent relative improvement over state-of-the-art approaches.

February 2015: Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election

posted Feb 18, 2015, 10:55 AM by Symeon Papadopoulos

Vadim Kagan, Andrew Stevens, V.S. Subrahmanian, "Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election", IEEE Intelligent Systems, vol.30, no. 1, pp. 2-5, Jan.-Feb. 2015, doi:10.1109/MIS.2015.16

Abstract: This column discusses the authors' efforts to project the winner of the 2013 Pakistan and the 2014 Indian prime minister election using social network analysis and methods to create a diffusion estimation model.

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