The Impact Of Visual Attributes On Online Image Diffusion

dc.creatorTotti L.
dc.creatorCosta F.
dc.creatorAvila S.
dc.creatorValle E.
dc.creatorMeira Jr. W.
dc.creatorAlmeida V.
dc.date2014
dc.date2015-06-25T18:00:33Z
dc.date2015-11-26T14:56:19Z
dc.date2015-06-25T18:00:33Z
dc.date2015-11-26T14:56:19Z
dc.date.accessioned2018-03-28T22:08:22Z
dc.date.available2018-03-28T22:08:22Z
dc.identifier9781450326223
dc.identifierWebsci 2014 - Proceedings Of The 2014 Acm Web Science Conference. Association For Computing Machinery, v. , n. , p. 42 - 51, 2014.
dc.identifier
dc.identifier10.1145/2615569.2615700
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84904490651&partnerID=40&md5=cb5323485dca4e0f507fada60f806c26
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/87415
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/87415
dc.identifier2-s2.0-84904490651
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1255451
dc.descriptionLittle is known on how visual content affects the popularity on social networks, despite images being now ubiquitous on the Web, and currently accounting for a considerable frac- tion of all content shared. Existing art on image sharing fo- cuses mainly on non-visual attributes. In this work we take a complementary approach, and investigate resharing from a mainly visual perspective. Two sets of visual features are proposed, encoding both aesthetical properties (brightness, contrast, sharpness, etc.), and semantical content (concepts represented by the images). We collected data from a large image-sharing service (Pinterest) and evaluated the predic- tive power of different features on popularity (number of reshares). We found that visual properties have low pre- dictive power compared that of social cues. However, after factoring-out social in uence, visual features show consider- able predictive power, especially for images with higher ex- posure, with over 3:1 accuracy odds when classifying highly exposed images between very popular and unpopular. Copyright © 2014 ACM.
dc.description
dc.description
dc.description42
dc.description51
dc.descriptionACM Special Interest Group on Hypertext,,Hypermedia and the Web (SIGWEB)
dc.descriptionAnagnostopoulos, A., Kumar, R., Mahdian, M., In uence and correlation in social networks KDD '08
dc.descriptionAvila, S., Thome, N., Cord, M., Valle, E., De, A., Araújo, A., Pooling in image representation: The visual codeword point of view (2013) CVIU
dc.descriptionBakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J., Everyone's an in uencer: Quantifying in uence on twitter (2011) WSDM '11
dc.descriptionBakshy, E., Rosenn, I., Marlow, C., Adamic, L., The role of social networks in information diffusion (2012) WWW '12, , ACM
dc.descriptionBhattacharya, S., Sukthankar, R., Shah, M., A framework for photo-quality assessment and enhancement based on visual aesthetics Proc. Of the International Conference on Multimedia, MM '10
dc.descriptionBoureau, Y.-L., Bach, F., LeCun, Y., Ponce, J., Learning mid-level features for recognition CVPR'10
dc.descriptionBoyd, D., Golder, S., Lotan, G., Tweet, tweet, retweet: Conversational aspects of retweeting on twitter (2010) Proc. Of HICSS
dc.descriptionCha, M., Benevenuto, F., Haddadi, H., Gummadi, P.K., The world of connections and information ow in twitter (2012) Trans. On Systems, Man, and Cybernetics, Part A, 42 (4)
dc.descriptionCha, M., Haddadi, H., Benevenuto, F., Gummadi, K., Measuring user in uence in twitter: The million follower fallacy (2010) ICWSM
dc.descriptionCha, M., Mislove, A., Gummadi, K.P., A measurement-driven analysis of information propagation in the ickr social network WWW '09
dc.descriptionCheng, J., Adamic, L., Dow, A., Kleinberg, J., Leskovec, J., Can cascades be predicted? WWW'14
dc.descriptionComaniciu, D., Meer, P., Member, S., Mean shift: A robust approach toward feature space analysis (2002) Trans. On Pattern Analysis and Machine Intelligence
dc.descriptionDatta, R., Joshi, D., Li, J., Wang, J.Z., Studying aesthetics in photographic images using a computational approach (2006) ECCV
dc.descriptionDhar, S., Ordonez, V., Berg, T., High level describable attributes for predicting aesthetics and interestingness (2011) CVPR
dc.descriptionDow, A., Adamic, L., Friggeri, A., The anatomy of large facebook cascades (2013) ICWSM
dc.description(2014), http://www.ebizmba.com/articles/social-networking-websites, eBiz MBA, MarGilbert, E., Bakhshi, S., Chang, S., Terveen, L., I need to try this?: A statistical overview of pinterest (2013) Proceedings of SIGCHI
dc.descriptionGjoka, M., Kurant, M., Butts, C.T., A walk in facebook: Uniform sampling of users in online social networks (2009) CoRR
dc.descriptionHanbury, A., Constructing cylindrical coordinate colour spaces (2008) Pattern Recogn. Lett, , Mar
dc.descriptionHong, L., Dan, O., Davison, B.D., Predicting popular messages in twitter (2011) Proc. WWW
dc.descriptionIsola, P., Xiao, J., Torralba, A., Oliva, A., What makes an image memorable? (2011) CVPR
dc.descriptionJiang, W., Loui, A., Cerosaletti, C., Automatic aesthetic value assessment in photographic images (2010) ICME
dc.descriptionJoshi, D., Datta, R., Luong, Q.-T., Fedorovskaya, E., Wang, J.Z., Li, J., Luo, J., Aesthetics and emotions in images: A computational perspective (2011) IEEE Signal Processing Magazine
dc.descriptionKe, Y., Tang, X., Jing, F., The design of high-level features for photo quality assessment (2006) CVPR
dc.descriptionKhosla, A., Sarma, A.D., Hamid, R., What makes an image popular? (2014) WWW, , April
dc.descriptionLerman, K., Jones, L., Social browsing on ickr (2006) CoRR, , abs/cs/0612047
dc.descriptionLeskovec, J., Backstrom, L., Kleinberg, J., Meme-Tracking and the dynamics of the news cycle KDD '09
dc.descriptionLi, C., Chen, T., Aesthetic visual quality assessment of paintings (2009) J. Sel. Topics Signal Processing
dc.descriptionLi, C., Gallagher, A.C., Loui, A.C., Chen, T., Aesthetic quality assessment of consumer photos with faces (2010) ICIP
dc.descriptionLowe, D.G., Object recognition from local scale-invariant features (1999) Proc. Of ICCV
dc.descriptionLuo, Y., Tang, X., Photo and video quality evaluation: Focusing on the subject Proc. ECCV'08
dc.descriptionMachajdik, J., Hanbury, A., Affective image classification using features inspired by psychology and art theory (2010) ACM Multimedia
dc.descriptionMacskassy, S.A., Michelson, M., Why do people retweet? Anti-homophily wins the day! ICWSM'11
dc.descriptionMarchesotti, L., Perronnin, F., Learning beautiful (and ugly) attributes (2013) BMVC, , IEEE
dc.descriptionMarchesotti, L., Perronnin, F., Larlus, D., Csurka, G., Assessing the aesthetic quality of photographs using generic image descriptors ICCV'11
dc.descriptionMyers, S.A., Zhu, C., Leskovec, J., Information diffusion and external in uence in networks (2012) Proc. Of SIGKDD
dc.descriptionOttoni, R., Pesce, J.P., Casas, D.B.L., Kumaraguru, P., Almeida, V., Ladies first: Analyzing gender roles and behaviors in pinterest (2013) ICWSM
dc.descriptionPerronnin, F., Sánchez, J., Mensink, T., Improving the fisher kernel for large-scale image classification (2010) Proceedings of ECCV
dc.descriptionSánchez, J., Perronnin, F., Mensink, T., Verbeek, J.J., Image classification with the fisher vector: Theory and practice (2013) IJCV, p. 105
dc.descriptionSloan, P., (2012), www.cnet.com/news/pinterest-crazy-growth-lands-it-As-Top-10-social-site, JanSmith, C., (2013), http://www.businessinsider.com/facebook-350-million-photos-each-day-2013- 9, SeptStieglitz, S., Dang-Xuan, L., Political communication and in uence through microblogging: An empirical analysis of sentiment in twitter messages and retweet behavior (2012) Proc. Of HICSS
dc.descriptionSuh, B., Hong, L., Pirolli, P., Chi, E.H., Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network Proc. SOCIALCOM'10
dc.descriptionThomee, B., Popescu, A., Overview of the imageclef 2012 ickr photo annotation and retrieval task (2012) CLEF
dc.descriptionVan De Weijer, J., Schmid, C., Verbeek, J., Learning color names from real-world images (2007) CVPR
dc.descriptionVu, C., Chandler, D.M., S3: A spectral and spatial sharpness measure 2009 First International Conference on Advances in Multimedia, p. 2009
dc.descriptionWang, W.-N., Yu, Y.-L., Jiang, S.-M., Image retrieval by emotional semantics: A study of emotional space and feature extraction (2006) SMC
dc.descriptionZarella, D., (2013) The Social Media Scientist, , http://danzarrella.com, June
dc.languageen
dc.publisherAssociation for Computing Machinery
dc.relationWebSci 2014 - Proceedings of the 2014 ACM Web Science Conference
dc.rightsfechado
dc.sourceScopus
dc.titleThe Impact Of Visual Attributes On Online Image Diffusion
dc.typeActas de congresos


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