<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vasiliki Gkatziaki</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DynamiCITY : Revealing city dynamics from citizens social media broadcasts</style></title><secondary-title><style face="normal" font="default" size="100%">Information Systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Smart City Applications</style></keyword><keyword><style  face="normal" font="default" size="100%">Social Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Urban Dynamics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0306437917300650</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">-</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Manolis G. Vozalis</style></author><author><style face="normal" font="default" size="100%">Konstantinos Diamantaras</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">George Sarigiannidis</style></author><author><style face="normal" font="default" size="100%">Konstantinos Ch. Chatzisavvas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sentiment analysis leveraging emotions and word embeddings</style></title><secondary-title><style face="normal" font="default" size="100%">Expert Systems with Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Online user reviews</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S095741741630584X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">69</style></volume><pages><style face="normal" font="default" size="100%">214 - 224</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Abstract Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that automating retrieval and detection of sentiments expressed for certain products and services embeds complex processes and pose research challenges, due to the textual phenomena and the language specific expression variations. This paper proposes a fast, flexible, generic methodology for sentiment detection out of textual snippets which express people’s opinions in different languages. The proposed methodology adopts a machine learning approach with which textual documents are represented by vectors and are used for training a polarity classification model. Several documents’ vector representation approaches have been studied, including lexicon-based, word embedding-based and hybrid vectorizations. The competence of these feature representations for the sentiment classification task is assessed through experiments on four datasets containing online user reviews in both Greek and English languages, in order to represent high and weak inflection language groups. The proposed methodology requires minimal computational resources, thus, it might have impact in real world scenarios where limited resources is the case.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Gkatziaki, Vasiliki</style></author><author><style face="normal" font="default" size="100%">Vakali, Athena</style></author><author><style face="normal" font="default" size="100%">Anthopoulos, Leonidas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CityPulse: A platform prototype for smart city social data mining</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of the Knowledge Economy</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">344–372</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Stefanos Antaris</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cloud-based architectures for Geo-located blogosphere dynamics detection</style></title><secondary-title><style face="normal" font="default" size="100%">Smart Cities</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cloud service deployment</style></keyword><keyword><style  face="normal" font="default" size="100%">geo-located blogosphere dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">social geo-located data clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">social networks and wisdom of the crowd</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social networking data threads emerge rapidly and such crowd-driven big data streams are valuable for detecting trends and opinions. For such analytics, conventional data mining approaches are challenged by both high-dimensionality and scalability concerns. Here, we leverage on the Cloud4Trends framework, for collecting and analyzing geo-located microblogging content, partitioned into clusters under cloud-based infrastructures. Different cloud architectures are proposed to offer flexible solutions for geo-located data analytics, with emphasis on incremental trend analysis. The proposed architectures are largely based on a set of service modules which facilitate the deployment of the experimentation on Cloud infrastructures. Several experimentation remarks are highlighted to showcase the requirements and testing capabilities of different cloud computing settings.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Neil Shah</style></author><author><style face="normal" font="default" size="100%">Alex Beutel</style></author><author><style face="normal" font="default" size="100%">Christos Faloutsos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ND-SYNC: Detecting Synchronized Fraud Activities</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Knowledge Discovery and Data Mining, 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-18032-8_16</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">201â€“214</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Neil Shah</style></author><author><style face="normal" font="default" size="100%">Christos Faloutsos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Cao, Tru</style></author><author><style face="normal" font="default" size="100%">Lim, Ee-Peng</style></author><author><style face="normal" font="default" size="100%">Zhou, Zhi-Hua</style></author><author><style face="normal" font="default" size="100%">Ho, Tu-Bao</style></author><author><style face="normal" font="default" size="100%">Cheung, David</style></author><author><style face="normal" font="default" size="100%">Motoda, Hiroshi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Retweeting Activity on Twitter: Signs of Deception</style></title><secondary-title><style face="normal" font="default" size="100%">PAKDD (1)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">9077</style></volume><pages><style face="normal" font="default" size="100%">122-134</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-18037-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">User communities evolution in microblogs: A public awareness barometer for real world events</style></title><secondary-title><style face="normal" font="default" size="100%">World Wide Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer US</style></publisher><pages><style face="normal" font="default" size="100%">1269-1299</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In social media, users' interactions are affected by real-world events which influence emergence and shifts of opinions and topics. Interactions around an event-related topic can be captured in a weighted network, while identification of connectivity and intensity patterns can improve understanding of users' interest on the topic. Community detection is studied here as a means to reveal groups of social media users with common interaction patterns in such networks. The proposed community detection approach identifies communities exploiting both structural properties and intensity patterns, while dynamics of communities' evolution around an event are revealed based on an iterative community detection and mapping scheme. We investigate the importance of considering interactions' intensity for community detection via a benchmarking process on synthetic graphs and propose a generic framework for: i) modeling user interactions, ii) identifying static and evolving communities around events, iii) extracting quantitative and qualitative measurements from the communities' timeline, iv) leveraging measurements to understand the events' impact. Two real-world case studies based on Twitter interactions demonstrate the framework's potential for capturing and interpreting associations among communities and events.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Capturing Social Data Evolution Using Graph Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Internet Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">74-79</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The fast and unpredictable evolution of social data poses challenges for capturing user activities and complex associations. Evolving social graph clustering promises to uncover the dynamics of latent user and content patterns.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lin, Xuemin</style></author><author><style face="normal" font="default" size="100%">Manolopoulos, Yannis</style></author><author><style face="normal" font="default" size="100%">Srivastava, Divesh</style></author><author><style face="normal" font="default" size="100%">Huang, Guangyan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Community Detection in Social Media by Leveraging Interactions and Intensities</style></title><secondary-title><style face="normal" font="default" size="100%">WISE (2)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">community detection</style></keyword><keyword><style  face="normal" font="default" size="100%">user weighted interaction networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8181</style></volume><pages><style face="normal" font="default" size="100%">57-72</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-41153-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Samaras, Christos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ketikidis, Panayiotis H.</style></author><author><style face="normal" font="default" size="100%">Margaritis, Konstantinos G.</style></author><author><style face="normal" font="default" size="100%">Vlahavas, Ioannis P.</style></author><author><style face="normal" font="default" size="100%">Chatzigeorgiou, Alexander</style></author><author><style face="normal" font="default" size="100%">Eleftherakis, George</style></author><author><style face="normal" font="default" size="100%">Stamelos, Ioannis</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Requirements and architecture design principles for a smart city experiment with sensor and social networks integration</style></title><secondary-title><style face="normal" font="default" size="100%">Panhellenic Conference on Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">327-334</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-1969-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sensors talk and humans sense Towards a reciprocal collective awareness smart city framework</style></title><secondary-title><style face="normal" font="default" size="100%">ICC Workshops</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">collective aware applications</style></keyword><keyword><style  face="normal" font="default" size="100%">sensors data management</style></keyword><keyword><style  face="normal" font="default" size="100%">smart city</style></keyword><keyword><style  face="normal" font="default" size="100%">social networks mining</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">189-193</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Smart city infrastructures provide unique opportunities for innovative applications developing and testing. Sensor city installations offer the ground for experimenting with user-oriented services, which at the same time can test and improve the infrastructure itself. The proposed work summarizes principles and methodology for and experiment, entitled SEN2SOC which will bridge sensor measurements and social networks interactions via natural language generation for supporting smart city services. SEN2SOC aims at exploiting the SmartSantander infrastructure in a sensor to social reciprocal fashion such that the sensor measurements will be and communicated to the public (citizens,authorities, etc), while social networks users activities in relevance to sensors social postings will be analyzed and summarized both to verify sensors reporting and to develop collective aware applications.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Antaris, Stefanos</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Mille, Alain</style></author><author><style face="normal" font="default" size="100%">Gandon, Fabien L.</style></author><author><style face="normal" font="default" size="100%">Misselis, Jacques</style></author><author><style face="normal" font="default" size="100%">Rabinovich, Michael</style></author><author><style face="normal" font="default" size="100%">Staab, Steffen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Social networking trends and dynamics detection via a cloud-based framework design</style></title><secondary-title><style face="normal" font="default" size="100%">WWW (Companion Volume)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cloud service deployment</style></keyword><keyword><style  face="normal" font="default" size="100%">microblogs and blogosphere dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">Social networks social</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Data Clustering</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">1213-1220</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-1230-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Jain, Lakhmi C.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Massive Graph Management for the Web and Web 2.0</style></title><secondary-title><style face="normal" font="default" size="100%">New Directions in Web Data Management 1</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Computational Intelligence</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">331</style></volume><pages><style face="normal" font="default" size="100%">19-58</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-17550-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Karagiannidis, Savvas</style></author><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Kosmatopoulos, Andreas</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Liarokapis, Fotis</style></author><author><style face="normal" font="default" size="100%">Doulamis, Anastasios D.</style></author><author><style face="normal" font="default" size="100%">Vescoukis, Vassilios</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a User-Aware Virtual Museum</style></title><secondary-title><style face="normal" font="default" size="100%">VS-GAMES</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">user groups</style></keyword><keyword><style  face="normal" font="default" size="100%">user preferences</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual museum</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">228-235</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4577-0316-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Christos Zigkolis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Code Generation for Cultural Content Management</style></title><secondary-title><style face="normal" font="default" size="100%">Panhellenic Conference on Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">21-24</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-7838-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>