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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Abstract: The increasing popularity of social interactions and geotagged, user-generated content has transformed the television viewing experience from laid-back video watching behavior into a "lean-forward"' socially engaged experience. This article describes a multiscreen, social TV system integrated with social sense via a second screen as a novel paradigm for content consumption. This new application is built upon the authors' cloud-centric media platform, which provides on-demand virtual machines for content platform services, including media distribution, storage, and processing. The media platform is also integrated with a Big Data social platform that crawls and mines social data related to the media content. Specifically, this new social TV approach consists of three key subsystems: interactive TV, social sense, and multiscreen orchestration. Interactive TV implements a cloud-based, social TV system, offering rich social features; social sense discovers the geolocation-aware public perception and knowledge related to the media content; and multiscreen orchestration provides an intuitive and user-friendly human-computer interface to combine the two other subsystems, fusing the TV viewing experience with social perception. The authors have built a proof-of-concept demo over a private cloud at the Nanyang Technological University (NTU), Singapore. Feature verification and performance comparisons demonstrate the feasibility and effectiveness of the proposed approach in transforming the TV viewing experience.
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