Ranking of news items in rule-stringent social media based on users' importance: A social computing approach

In this paper an innovative social media news items ranking scheme is proposed. The proposed unsupervised architecture takes into consideration user-content interactions, since social media posts receive likes, comments and shares from friends and other users. Additionally the importance of each user is modeled, based on an innovative algorithm that borrows ideas from the PageRank algorithm. Finally, a novel content ranking component is introduced, which ranks posted news items based on a social computing method, driven by the importance of the social network users that interact with them. Initial experiments on real life social networks news items illustrate the promising performance of the proposed architecture. Additionally comparisons with three different ranking ways are provided (SUMF, RSN-CO and RSN-nCO), in terms of user satisfaction.

[1]  S. Ramya.  Web Image Re-Ranking Using Query-Specific Semantic Signatures , 2015 .

[2]  Han Zhong,et al.  Data-Brain Driven Documents Ranking for Constructing Brain Informatics Provenances , 2014, Brain Informatics and Health.

[3]  Larry S. Davis,et al.  Image ranking and retrieval based on multi-attribute queries , 2011, CVPR 2011.

[4]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[5]  Serge Fdida,et al.  From popularity prediction to ranking online news , 2014, Social Network Analysis and Mining.

[6]  Chien-Liang Liu,et al.  Movie Rating and Review Summarization in Mobile Environment , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Flavius Frasincar,et al.  Querying and ranking news items in the hermes framework , 2012, SAC '12.

[8]  Jasmine Novak,et al.  PageRank Computation and the Structure of the Web: Experiments and Algorithms , 2002 .

[9]  Wolfgang Wahlster,et al.  Towards the Internet of Services: The THESEUS Research Program , 2014, Cognitive Technologies.

[10]  Lei Zhang,et al.  A Tweet-Centric Algorithm for News Ranking , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.

[11]  Ponnurangam Kumaraguru,et al.  Credibility ranking of tweets during high impact events , 2012, PSOSM '12.

[12]  Claudia Nickel,et al.  Content Identification and Quality-Based Ranking , 2014, Towards the Internet of Services.

[13]  Qi Tian,et al.  Attribute-assisted reranking for web image retrieval , 2012, ACM Multimedia.

[14]  O. Perron.  Zur Theorie der Matrices , 1907 .

[15]  Naomichi Murakami,et al.  Emotional video ranking based on user comments , 2011, iiWAS '11.

[16]  Yi Yang,et al.  A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Giannis Tzimas,et al.  Improving Search Engines' Document Ranking Employing Semantics and an Inference Network , 2013, WEBIST.

[18]  Kazuaki Ando,et al.  Web news ranking for elementary school children based on degree of SNS users' attention and popular search queries among children , 2015, 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[19]  Xiaogang Wang,et al.  Web Image Re-Ranking UsingQuery-Specific Semantic Signatures , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Zhen Shu,et al.  Web document ranking via active learning and kernel principal component analysis , 2015 .

[21]  Chien-Liang Liu,et al.  An HMM-Based Algorithm for Content Ranking and Coherence-Feature Extraction , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Massimo Franceschet,et al.  PageRank , 2010, Commun. ACM.