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.

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.

January 2015: Recommending Venues Using Continuous Predictive Social Media Analytics

posted Jan 19, 2015, 8:36 AM by Symeon Papadopoulos


Marco Balduini, Alessandro Bozzon, Emanuele Della Valle, Yi Huang, Geert-Jan Houben, "Recommending Venues Using Continuous Predictive Social Media Analytics", IEEE Internet Computing, vol.18, no. 5, pp. 28-35, Sept.-Oct. 2014, doi:10.1109/MIC.2014.84

Abstract: The authors' Continuous Predictive Social Media Analytics system operates in real time on social media streams and graphs to recommend venues to visitors of geo- and temporally bounded city-scale events. By combining deductive and inductive stream reasoning techniques with visitor-modeling functionalities, this system semantically analyzes and links visitors' social network activities to produce high-quality link predictions when information about preferences is sparse. The authors demonstrate their system's quality with experiments on real-world data.

December 2014: Computational Social Science for the World Wide Web

posted Dec 10, 2014, 8:46 AM by Symeon Papadopoulos


Markus Strohmaier, Claudia Wagner, "Computational Social Science for the World Wide Web", IEEE Intelligent Systems, vol.29, no. 5, pp. 84-88, Sept.-Oct. 2014, doi:10.1109/MIS.2014.80

Abstract: In introducing the field of computational social science to the intelligent systems community, the authors discuss how this field can help advance the current state of understanding and engineering social-computational systems on the World Wide Web.

November 2014: Trust and Privacy Exploitation in Online Social Networks

posted Nov 11, 2014, 4:03 AM by Symeon Papadopoulos

Kaze Wong, Angus Wong, Alan Yeung, Wei Fan, Su-Kit Tang, "Trust and Privacy Exploitation in Online Social Networks", IT Professional, vol.16, no. 5, pp. 28-33, Sept.-Oct. 2014, doi:10.1109/MITP.2014.79

Abstract: Online social networks have been typically created for convenience -so they haven't been built from the ground up with security in mind. They often have confusing privacy settings and are susceptible to various kinds of attacks that exploit users' trust and privacy. In this article, the authors discuss security pitfalls in today's social networks, briefly introducing common attack methods. They implemented a proof-of-concept Facebook app, which is actually a harmless malware that uses common attack methods to demonstrate the vulnerability of online social networks. Although today's online social networks commonly offer users a variety of security settings, users tend to trust the information obtained from online social networks regardless of the settings. This kind of user mentality can be more crucial than technical aspects in determining the level of security in online social networks.

October 2014: Predicting Edge Signs in Social Networks Using Frequent Subgraph Discovery

posted Oct 11, 2014, 1:58 PM by Symeon Papadopoulos


Athanasios Papaoikonomou, Magdalini Kardara, Konstantinos Tserpes, Theodora A. Varvarigou, "Predicting Edge Signs in Social Networks Using Frequent Subgraph Discovery", IEEE Internet Computing, vol.18, no. 5, pp. 36-43, Sept.-Oct. 2014, doi:10.1109/MIC.2014.82

Abstract: In signed social networks, users are connected via directional signed links that indicate their opinions about each other. Predicting the signs of such links is crucial for many real-world applications, such as recommendation systems. The authors mine patterns that emerge frequently in the social graph, and show that such patterns possess enough discriminative power to accurately predict the relationships among social network users. They evaluate their approach through an experimental study that comprises three large-scale, real-world datasets and show that it outperforms state-of-the art methods.

September 2014: Real-Time Crisis Mapping of Natural Disasters Using Social Media

posted Sep 11, 2014, 11:05 AM by Symeon Papadopoulos


Stuart E. Middleton, Lee Middleton, Stefano Modafferi, "Real-Time Crisis Mapping of Natural Disasters Using Social Media," IEEE Intelligent Systems, vol. 29, no. 2, pp. 9-17, Mar.-Apr. 2014, doi:10.1109/MIS.2013.126

Abstract: The proposed social media crisis mapping platform for natural disasters uses locations from gazetteer, street map, and volunteered geographic information (VGI) sources for areas at risk of disaster and matches them to geoparsed real-time tweet data streams. The authors use statistical analysis to generate real-time crisis maps. Geoparsing results are benchmarked against existing published work and evaluated across multilingual datasets. Two case studies compare five-day tweet crisis maps to official post-event impact assessment from the US National Geospatial Agency (NGA), compiled from verified satellite and aerial imagery sources.

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