Socialising around media

SAM is a social media platform that enhances the experience of watching video content in a conventional living room setting, with a service that lets the viewer use a second screen (such as a smart phone) to interact with content, context and communities related to the main video content. This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for users: semantic analysis, context awareness and dynamic communities. Both dataset-based and end user evaluations of system functionalities are reported in order to determine the effectiveness and efficiency of the components directly involved and the platform as a whole.

[1]  Fred D. Davis.  A technology acceptance model for empirically testing new end-user information systems : theory and results , 1985 .

[2]  Lora Aroyo,et al.  NoTube: The television experience enhanced by online social and semantic data , 2011, 2011 IEEE International Conference on Consumer Electronics -Berlin (ICCE-Berlin).

[3]  Mark Dredze,et al.  Entity Linking: Finding Extracted Entities in a Knowledge Base , 2013, Multi-source, Multilingual Information Extraction and Summarization.

[4]  Yorick Wilks,et al.  A Closer Look at Skip-gram Modelling , 2006, LREC.

[5]  Patricio Martínez-Barco,et al.  Evaluating a Sentiment Analysis Approach from a Business Point of View , 2015, TASS@SEPLN.

[6]  Hesham Mohamed El-Deeb,et al.  Suite of decision tree-based classification algorithms on cancer gene expression data , 2011 .

[7]  Pablo César,et al.  Usages of the Secondary Screen in an Interactive Television Environment: Control, Enrich, Share, and Transfer Television Content , 2008, EuroITV.

[8]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[9]  Andreas Menychtas,et al.  Efficient Context Management and Personalized User Recommendations in a Smart Social TV Environment , 2016, GECON.

[10]  J. Fleiss.  Measuring nominal scale agreement among many raters. , 1971 .

[11]  Yi Zhang,et al.  Bayesian graphical models for adaptive filtering , 2005, SIGF.

[12]  Arun Prakash Agrawal,et al.  Comparative analysis of Relational and Graph databases , 2013 .

[13]  Andrew Jones,et al.  Social TV: toward content navigation using social awareness , 2010 .

[14]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[15]  Fernando Berzal Galiano,et al.  The NOESIS open source framework for network data mining , 2015, 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K).

[16]  Fabio Giglietto,et al.  Second Screen and Participation: A Content Analysis on a Full Season Dataset of Tweets , 2014 .

[17]  Srinivasan Parthasarathy,et al.  Scalable graph clustering using stochastic flows: applications to community discovery , 2009, KDD.

[18]  Xuelong Li,et al.  Toward Multiscreen Social TV with Geolocation-Aware Social Sense , 2014, IEEE MultiMedia.

[19]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010, Community Detection and Mining in Social Media.

[20]  H. Akaike.  A new look at the statistical model identification , 1974 .

[21]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[22]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[23]  Jure Leskovec,et al.  Discovering social circles in ego networks , 2012, ACM Trans. Knowl. Discov. Data.

[24]  Preslav Nakov,et al.  SemEval-2013 Task 2: Sentiment Analysis in Twitter , 2013, *SEMEVAL.

[25]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .

[26]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[27]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[28]  Jure Leskovec,et al.  Mining of Massive Datasets, 2nd Ed , 2014 .

[29]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[30]  Michael E. Holmes,et al.  Visual attention to television programs with a second-screen application , 2012, ETRA '12.

[31]  Konstantinos Chorianopoulos,et al.  Personalized and mobile digital TV applications , 2006, Multimedia Tools and Applications.

[32]  Evelien D'heer,et al.  Second screen applications and tablet users: constellation, awareness, experience, and interest , 2012, EuroITV.

[33]  Geoff Holmes,et al.  Generating Rule Sets from Model Trees , 1999, Australian Joint Conference on Artificial Intelligence.

[34]  Julio Villena-Román,et al.  Overview of TASS 2015 , 2015, TASS@SEPLN.

[35]  George Karypis,et al.  Multilevel k-way Partitioning Scheme for Irregular Graphs , 1998, J. Parallel Distributed Comput..

[36]  Richard B. Silberstein,et al.  The Power of Social Television: Can Social Media Build Viewer Engagement? , 2014, Journal of Advertising Research.

[37]  Xuelong Li,et al.  Toward an SDN-enabled big data platform for social TV analytics , 2015, IEEE Network.

[38]  Anand Rajaraman,et al.  Mining of Massive Datasets , 2011 .

[39]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[40]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[41]  Jure Leskovec,et al.  Overlapping community detection at scale: a nonnegative matrix factorization approach , 2013, WSDM.

[42]  Erik Cambria,et al.  Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article] , 2014, IEEE Computational Intelligence Magazine.

[43]  Derek Greene,et al.  Tracking the Evolution of Communities in Dynamic Social Networks , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[44]  Hwee Tou Ng,et al.  Corpus-Based Approaches to Semantic Interpretation in NLP , 1997, AI Mag..

[45]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[46]  LeskovecJure,et al.  Discovering social circles in ego networks , 2014 .

[47]  Jehan Wickramasuriya,et al.  Analyzing Twitter for Social TV : Sentiment Extraction for Sports , 2011 .

[48]  David Geerts,et al.  Social experiences within the home using second screen TV applications , 2016, Multimedia Tools and Applications.

[49]  Fred J. Damerau,et al.  A technique for computer detection and correction of spelling errors , 1964, CACM.

[50]  Satoshi Sekine,et al.  A survey of named entity recognition and classification , 2007 .

[51]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[52]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[53]  Yiannis Kompatsiaris,et al.  User Community Discovery , 2015, Human–Computer Interaction Series.

[54]  Maarten de Rijke,et al.  Feeding the Second Screen: Semantic Linking based on Subtitles , 2013, DIR.

[55]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[56]  Yiannis Kompatsiaris,et al.  Community detection in Social Media , 2012, Data Mining and Knowledge Discovery.

[57]  David Geerts,et al.  In front of and behind the second screen: viewer and producer perspectives on a companion app , 2014, TVX '14.

[58]  David Tomas,et al.  Entity linking in media content and user comments: Connecting data to wikipedia and other knowledge bases , 2015, eChallenges e-2015 Conference.

[59]  Tanya Y. Berger-Wolf,et al.  Finding Communities in Dynamic Social Networks , 2011, 2011 IEEE 11th International Conference on Data Mining.