Abstract
With the advancements in user-generated content sites, their prominence for multimedia information dissemination is increasing on a rapid scale. An information about which content is going to be popular and to what extent has a big set of application area including load balancing of servers, online marketing strategy decisions, recommendation systems, disaster management, etc. Big tech-giants, to gain a competitive advantage for strategic decision making, are employing various techniques for an accurate and efficient popularity prediction mechanism based on past trends and current scenario. Thus, there is a need for a detailed and comprehensive study in the field of popularity prediction so as to identify the current trends, in order to tap benefits from the huge potential the field offers. This paper deals with a detailed survey of the literature of popularity prediction of online content covering all the aspects of popularity and the interplay between the contents at various platforms, relationship between the granularity levels and various feature selection methods used for different approaches to prediction. A bibliometrics analysis of the selected approaches is also conducted with a discussion of their advantages and limitations. This will help the researchers to focus on emerging trends of online content popularity prediction algorithms.
Similar content being viewed by others
References
Ager B, Schneider F, Kim J, Feldmann A (2010) Revisiting cacheability in times of user generated content. INFOCOM IEEE Conf Comput Commun Workshops 2010:1–6. https://doi.org/10.1109/INFCOMW.2010.5466667
Ahmed M, Spagna S, Huici F, Niccolini S (2013) A peek into the future: predicting the evolution of popularity in user generated content. WSDM’13 33:19–24. https://doi.org/10.1109/INTERNET.2010.13
Arapakis I, Cambazoglu BB, Lalmas M, Arbor A, Anderson A, Watts DJ, Chen Z (2016) Improving Movie Gross Prediction through News Analysis. ICWSM 1:449–454. https://doi.org/10.1145/1963192.1963222
Arapakis I, Cambazoglu BB, Lalmas M (2017) On the feasibility of predicting popular news at cold start. J Associat Inform Sci Technol 68(5):1149–1164. https://doi.org/10.1002/asi.23756
Asur S and Huberman BA (2010) Predicting the future with social media. Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010, 1, 492–499. https://doi.org/10.1109/WI-IAT.2010.63
Avramova Z, Wittevrongel S, Bruneel H and De Vleeschauwer D (2009) Analysis and modeling of video popularity evolution in various online video content systems: Power-law versus exponential decay. 1st International Conference on Evolving Internet, INTERNET 2009, 95–100. https://doi.org/10.1109/INTERNET.2009.22
Azuaje F (2006) Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques 2nd edition. BioMedical Engineering OnLine, 5(1). https://doi.org/10.1186/1475-925x-5-51
Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on Twitter. Proc Fourth ACM Int Conf Web Search Data Mining. https://doi.org/10.1145/1935826.1935845
Bandari R, Asur S and Huberman BA (2012) The pulse of news in social media: Forecasting popularity. ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, (February 2012), 26–33. Retrieved from http://arxiv.org/abs/1202.0332
Bao P (2016) Modeling and predicting popularity dynamics via an influence-based self-excited Hawkes process. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 1897–1900. https://doi.org/10.1145/2983323.2983868
Barreda A, Bilgihan A (2013) An analysis of user-generated content for hotel experiences. J Hosp Tour Technol 4(3):263–280. https://doi.org/10.1108/JHTT-01-2013-0001
Berger J, Milkman K (2009) What makes online content viral? J Mark Res. https://doi.org/10.2139/ssrn.1528077
Beutel A, Prakash BA, Rosenfeld R and Faloutsos C (2012) Interacting viruses in networks: Can both survive? In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 426–434). Retrieved from https://doi.org/10.1145/2339530.2339601
Bi Y, Wu W, Zhu Y (2013) CSI: Charged system influence model for human behavior prediction. Proc - IEEE Int Conf Data Mining, ICDM. https://doi.org/10.1109/ICDM.2013.136
Bielski A and Trzcinski T (2018) Pay attention to virality: understanding popularity of social media videos with the attention mechanism. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018-June, 2398–2400. https://doi.org/10.1109/CVPRW.2018.00309
Borghol Y, Mitra S, Ardon S, Carlsson N, Eager D, Mahanti A (2011) Characterizing and modelling popularity of user-generated videos. Perform Eval 68(11):1037–1055. https://doi.org/10.1016/j.peva.2011.07.008
Bughin JR (2007) How companies can make the most of user-generated content. The McKinsey Quarterly, (August), 1–4. Retrieved from http://www0.cs.ucl.ac.uk/staff/d.quercia/others/ugc.pdf
Cao Q, Shen H, Gao J, Wei B and Cheng X (2020) Popularity prediction on social platforms with coupled graph neural networks. WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, 70–78. https://doi.org/10.1145/3336191.3371834
Carlinet Y, Huynh TD, Kauffmann B, Mathieu F, Noirie L and Tixeuil S (2012) Four months in daily motion: Dissecting user video requests. IWCMC 2012 - 8th International Wireless Communications and Mobile Computing Conference, 613–618. https://doi.org/10.1109/IWCMC.2012.6314274
Castillo C, El-Haddad M, Pfeffer J, Stempeck M (2014) Characterizing the life cycle of online news stories using social media reactions. Proc ACM Conf Comput Support Cooperat Work, CSCW. https://doi.org/10.1145/2531602.2531623
Cha M, Kwak H, Rodriguez P, Ahnt YY, Moon S (2007) I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. Proc ACM SIGCOMM Int Measur Conf, IMC. https://doi.org/10.1145/1298306.1298309
Chen Y-LL, Chang C-LL (2019a) Early prediction of the future popularity of uploaded videos. Expert Syst Appl 133:59–74
Chen YL, Chang CL (2019b) Early prediction of the future popularity of uploaded videos. Expert Syst Appl 133:59–74. https://doi.org/10.1016/j.eswa.2019.05.015
Chen G, Kong Q, Xu N, Mao W (2019a) NPP: A neural popularity prediction model for social media content. Neurocomputing 333:221–230. https://doi.org/10.1016/j.neucom.2018.12.039
Chen Y, Tao G, Xie Q, Song M (2019b) Video attention prediction using gaze saliency. Multimedia Tools Appl 78(19):26867–26884. https://doi.org/10.1007/s11042-016-4294-1
Chen J, Song X, Nie L, Wang X, Zhang H and Chua TS (2016) Micro tells macro: Predicting the popularity of micro-videos via a transductive model. MM 2016 - Proceedings of the 2016 ACM Multimedia Conference, 898–907. https://doi.org/10.1145/2964284.2964314
Cheng Xu, Dale C and Liu J (2007) Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study. Retrieved from http://arxiv.org/abs/0707.3670
Cheng X, Dale C and Liu J (2008) Statistics and social network of youtube videos. 2008 16th International Workshop on Quality of Service, 229–238. https://doi.org/10.1109/IWQOS.2008.32
Cheng J, Adamic L, Dow A, Kleinberg J and Leskovec J (2014) Can Cascades be Predicted? WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web. https://doi.org/10.1145/2566486.2567997
Corchs S, Fersini E, Gasparini F (2019) Ensemble learning on visual and textual data for social image emotion classification. Int J Mach Learn Cybern 10(8):2057–2070. https://doi.org/10.1007/s13042-017-0734-0
Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585
Crane R and Sornette D (2008) Robust dynamic classes revealed by measuring the response function of a social system. https://doi.org/10.1073/pnas.0803685105
Deng Z, Yan M, Sang J, Xu C (2015) Twitter is faster: personalized time-aware video recommendation from twitter to Youtube. ACM Trans Multimedia Comput Commun Appl. https://doi.org/10.1145/2637285
Deza A and Parikh D (2015) Understanding image virality. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12-June, 1818–1826. https://doi.org/10.1109/CVPR.2015.7298791
Dezsö Z, Almaas E, Lukács A, Rácz B, Szakadát I and Barabási AL (2006) Dynamics of information access on the web. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 73(6). https://doi.org/10.1103/PhysRevE.73.066132
Ding W, Shang Y, Guo L, Hu X, Yan R and He T (2015) Video popularity prediction by sentiment propagation via implicit network. International Conference on Information and Knowledge Management, Proceedings, 19–23-Oct-, 1621–1630. https://doi.org/10.1145/2806416.2806505
Dou H, Zhao X, Zhao Y and Wen J-R (2018) Predicting the popularity of online content with knowledge-enhanced neural networks.
Fargetta G, Scrimali LRM (2020) Generalized Nash equilibrium and dynamics of popularity of online contents. Optim Lett. https://doi.org/10.1007/s11590-019-01528-4
Figueiredo F, Almeida J, Gonçalves M, Benevenuto F (2014) On the dynamics of social media popularity: a YouTube case study. ACM Trans Internet Technol. https://doi.org/10.1145/2665065
Gao S, Ma J, Chen Z (2015) Modeling and predicting retweeting dynamics on microblogging platforms. Proc Eighth ACM Int Conf Web Search Data Mining. https://doi.org/10.1145/2684822.2685303
Garvey MD, Samuel J and Pelaez A (2021) Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation. Decision Support Systems, 144, 113497. https://doi.org/10.1016/j.dss.2021.113497
Gelli F, Uricchio T, Bertini M, Bimbo A Del and Chang SF (2015) Image popularity prediction in social media using sentiment and context features. MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 907–910. https://doi.org/10.1145/2733373.2806361
Gómez V, Kaltenbrunner A and López V (2008) Statistical analysis of the social network and discussion threads in Slashdot. Proceeding of the 17th International Conference on World Wide Web 2008, WWW’08, 645–654. https://doi.org/10.1145/1367497.1367585
Gupta M, Gao J, Zhai C and Han J (2012) Predicting future popularity trend of events in microblogging platforms. ASIST.
Gürsun G, Crovella M, Matta I (2011) Describing and forecasting video access patterns. Proc - IEEE INFOCOM. https://doi.org/10.1109/INFCOM.2011.5934965
He K, Zhang X, Ren S and Sun J (2015) Deep residual learning for image recognition. Retrieved from http://arxiv.org/abs/1512.03385
Hessel J and Lee L (2019) Something’s brewing! Early prediction of controversy-causing posts from discussion features. NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 1, 1648–1659. https://doi.org/10.18653/v1/n19-1166
Hong L, Dan O and Davison BD (2011) Predicting popular messages in Twitter. Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, (January 2014), 57–58. https://doi.org/10.1145/1963192.1963222
Hsu C-F, Khabiri E and Caverlee J (2009). Ranking comments on the social web. 90–97. https://doi.org/10.1109/CSE.2009.109
Huang J, Tang Y, Hu Y, Li J, Hu C (2020) Predicting the active period of popularity evolution: a case study on Twitter hashtags. Inf Sci 512:315–326. https://doi.org/10.1016/j.ins.2019.04.028
Jamali S and Rangwala H (2009) Digging digg: comment mining, popularity prediction, and social network analysis. 2009 International Conference on Web Information Systems and Mining, WISM 2009, 32–38. https://doi.org/10.1109/WISM.2009.15
Joachims T (1998) Text Categorization with Support Vector Machines. Proc. European Conf. Machine Learning (ECML’98). https://doi.org/10.17877/DE290R-5097
Kaltenbrunner A, Gómez V, López V and Org VL (2007) Description and prediction of slashdot activity. https://doi.org/10.1109/LA-WEB.2007.59
Keneshloo Y, Wang S and Ramakrishnan N (2016) Predicting the shape and peak time of news article views. https://doi.org/10.1109/BigData.2016.7840875
Khosla A, Das Sarma A and Hamid R (2014) What makes an image popular? 867–876. https://doi.org/10.1145/2566486.2567996
Kim S-D, Kim S-H and Cho H-G (2011) Predicting the virtual temperature of web-blog articles as a measurement tool for online popularity. 449–454. https://doi.org/10.1109/CIT.2011.104
Kitchenham B (2004) Procedures for performing systematic literature reviews. Joint Technical Report, Keele University TR/SE-0401 and NICTA TR-0400011T.1, 33, 33. Retrieved from http://www.inf.ufsc.br/~aldo.vw/kitchenham.pdf
Klubička F and Fernández R (2018) Examining a hate speech corpus for hate speech detection and popularity prediction. Retrieved from http://arxiv.org/abs/1805.04661
Kobayashi R and Lambiotte R (2016) TiDeH: time-dependent Hawkes process for predicting retweet dynamics.
Kong S, Mei Q, Feng L, Ye,F and Zhao Z (2014a) Predicting bursts and popularity of hashtags in real-time. Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, 927–930. https://doi.org/10.1145/2600428.2609476
Kong S, Ye F and Feng L (2014b) predicting future retweet counts in a microblog.
Krishnappa DK, Zink M, Griwodz C, Halvorsen P (2015) Cache-centric video recommendation: an approach to improve the efficiency of youtube caches. ACM Trans Multimedia Comput Commun Appl. https://doi.org/10.1145/2716310
Krumm J, Davies N, Narayanaswami C (2008) User-generated content. IEEE Pervasive Comput 7(4):10–11. https://doi.org/10.1109/MPRV.2008.85
Kupavskii A, Umnov A, Gusev G and Serdyukov P (2013) Predicting the audience size of a tweet. Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013, 693–696. Retrieved from http://twitter.com
Lakkaraju H and Ajmera J (2011) Attention prediction on social media brand pages. 2157–2160. https://doi.org/10.1145/2063576.2063915
Lee JG, Moon S, Salamatian K (2012) Modeling and predicting the popularity of online contents with Cox proportional hazard regression model. Neurocomputing 76(1):134–145. https://doi.org/10.1016/j.neucom.2011.04.040
Lee J, Moon S and Salamatian K (2010) An approach to model and predict the popularity of online contents with explanatory factors. Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010, 1, 623–630. https://doi.org/10.1109/WI-IAT.2010.209
Lee CS and Ma L (2012) News sharing in social media: The effect of gratifications and prior experience. Comput Hum Behav 28:331–339. https://doi.org/10.1016/j.chb.2011.10.002
Lerman K and Galstyan A (2008) Analysis of social voting patterns on Digg. Proceedings of the ACM SIGCOMM 2008 Conference on Computer Communications -1st Workshop on Online Social Networks, WOSP’08, 7–12. https://doi.org/10.1145/1397735.1397738
Lerman K and Hogg T (2010) Using a model of social dynamics to predict popularity of news. Proceedings of the 19th International Conference on World Wide Web, WWW ’10, 621–630. https://doi.org/10.1145/1772690.1772754
Lerman, K. (2010). Information contagion: social systems an empirical study of the spread of news on digg and twitter social networks predictability of complex view project social media view project. Retrieved from https://networkchallenge.darpa.mil
Li C, Liu J, Ouyang S (2016) Characterizing and predicting the popularity of online videos. IEEE Access 4:1630–1641. https://doi.org/10.1109/ACCESS.2016.2552218
Li L, Wu Y, Zhang Y, Zhao T (2019) Time+User dual attention based sentiment prediction for multiple social network texts with time series. IEEE Access 7:17644–17653. https://doi.org/10.1109/ACCESS.2019.2895897
Li H, Ma X, Wang F, Liu J and Xu K (2013) On popularity prediction of videos shared in online social networks. Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 169–178. https://doi.org/10.1145/2505515.2505523
Liao D, Xu J, Li G, Huang W, Liu W and Li J (2019) Popularity prediction on online articles with deep fusion of temporal process and content features. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 200–207. https://doi.org/10.1609/aaai.v33i01.3301200
Liben-nowell D, Kleinberg J (2003) The link prediction problem for social networks. J Am Soc Inform Sci Technol. https://doi.org/10.1002/asi.20591
Ling C, AbuHilal I, Blackburn J, De Cristofaro E, Zannettou S and Stringhini G (2021) Dissecting the meme magic: understanding indicators of virality in image memes. ACM Conference on Computer-Supported Cooperative Work and Social Computing, 5(Cscw), 1–24. https://doi.org/10.1145/3449155
Liu X, Bollen J, Nelson M, Sompel H (2005) Co-Authorship networks in the digital library research community. Inf Process Manage 41:1462–1480. https://doi.org/10.1016/j.ipm.2005.03.012
Lymperopoulos I (2016) Predicting the popularity growth of online content: model and algorithm. Inf Sci. https://doi.org/10.1016/j.ins.2016.07.043
Ma H, Qian W, Xia F, He X, Xu J, Zhou A (2013) Towards modeling popularity of microblogs. Front Comp Sci 7(2):171–184. https://doi.org/10.1007/s11704-013-3901-9
Martin T, Hofman J, Sharma A, Anderson A and Watts D (2016) Exploring limits to prediction in complex social systems. WWW ’16: Proceedings of the 25th International Conference on World Wide Web. https://doi.org/10.1145/2872427.2883001
Mccallum A and Nigam K (2001) A comparison of event models for naive bayes text classification. Work Learn Text Categ, 752.
McCreadie RMC, Macdonald C and Ounis I (2010) news article ranking: leveraging the wisdom of bloggers. Adaptivity, Personalization and Fusion of Heterogeneous Information, 40–48. Paris, FRA: LE CENTRE DE HAUTES ETUDES INTERNATIONALES D’INFORMATIQUE DOCUMENTAIRE.
Mekouar S, Zrira N and Bouyakhf E-H (2017) popularity prediction of videos in youtube as case study: a regression analysis study. Proceedings of the 2nd International Conference on Big Data, Cloud and Applications. https://doi.org/10.1145/3090354.3090406
Mitra S, Agrawal M, Yadav A, Carlsson N, Eager D and Mahanti A (2011) Characterizing Web-based video sharing workloads. ACM Transactions on the Web, 5(2). https://doi.org/10.1145/1961659.1961662
Moniz N, Torgo L (2019) A review on web content popularity prediction: issues and open challenges. Online Soc Networks and Media 12:1–20. https://doi.org/10.1016/j.osnem.2019.05.002
Moniz N, Torgo L, Eirinaki M, Branco P (2017) A framework for recommendation of highly popular news lacking social feedback. N Gener Comput. https://doi.org/10.1007/s00354-017-0019-x
Moniz N and Torgo L (2018) Multi-source social feedback of online news feeds. Retrieved from http://arxiv.org/abs/1801.07055
Moniz N, Torgo L and Eirinaki M (2016) Time-based ensembles for prediction of rare events in news streams. https://doi.org/10.1109/ICDMW.2016.0154
Morales G, Gionis A and Lucchese C (2012) From chatter to headlines: harnessing the real-time web for personalized news recommendation. WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 153–162. https://doi.org/10.1145/2124295.2124315
Myers SA, Leskovec J (2012) Clash of the contagions: cooperation and competition in information diffusion. Proc - IEEE Int Conf Data Mining, ICDM. https://doi.org/10.1109/ICDM.2012.159
Naveed N, Gottron T, Kunegis J and Che Alhadi A (2011) Bad news travel fast: a content-based analysis of interestingness on Twitter. ACM WebSci ’11, Koblenz, Germany. https://doi.org/10.1145/2527031.2527052
Nguyen MT, Le DH, Nakajima T, Yoshimi M and Thoai N (2019) Attention-based neural network: a novel approach for predicting the popularity of online content. Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019, 329–336. https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00058
Nia ZM, Khayyambashi MR (2021) Improving content popularity prediction with k-means clustering and deep-belief networks. Multimedia Tools Appl. https://doi.org/10.1007/s11042-020-10463-x
Oghina A, Breuss M, Tsagkias M and De Rijke M (2012) Predicting IMDB movie ratings using social media. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7224 LNCS, 503–507. https://doi.org/10.1007/978-3-642-28997-2_51
Ouyang S, Li C, Li X (2016) A peek into the future: Predicting the popularity of online videos. IEEE Access 4:3026–3033. https://doi.org/10.1109/ACCESS.2016.2580911
Petrovic S, Osborne M and Lavrenko V (2011) Rt to win! predicting message propagation in twitter. Proceedings of the Fifth International Conference on Weblogs and Social Media - ICWSM ’11, 586–589. Retrieved from, http://homepages.inf.ed.ac.uk/miles/papers/icwsm11.pdf%5Cnhttp://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewPDFInterstitial/2754/3209
Pinto H, Almeida JM and Gonçalves MA (2013) Using early view patterns to predict the popularity of YouTube videos. WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining, (February), 365–374. https://doi.org/10.1145/2433396.2433443
Purohit H, Ruan Y, Joshi A, Parthasarathy S and Sheth A (2011) Understanding user-community engagement by multi-faceted features: a case study on Twitter.
Richier C, Altman E, Elazouzi R, Altman T, Linares G, Portilla Y (2014) Modelling View-count Dynamics in YouTube. Retrieved from http://arxiv.org/abs/1404.2570
Rizoiu MA, Xie L, Sanner S, Cebrian M, Yu H and Van Henteryck P (2017) Expecting to be HIP: Hawkes intensity processes for social media popularity. 26th International World Wide Web Conference, WWW 2017, 735–744. https://doi.org/10.1145/3038912.3052650
Rodriguez P, Ahn Y-Y, Moon SB, Cha M, Kwak H, Moon S (2009) Analyzing the video popularity characteristics of large-scale user generated content systems. IEEE/ACM Trans Networking 17(5):1357. https://doi.org/10.1145/1665838.1665839
Roy SD, Mei T, Zeng W, Li S (2013) Towards cross-domain learning for social video popularity prediction. IEEE Trans Multimedia 15(6):1255–1267. https://doi.org/10.1109/TMM.2013.2265079
Shen H, Wang D, Song C and Barabási A-L (2014) Modeling and predicting popularity dynamics via reinforced poisson processes. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 291–297. AAAI Press.
Shulman B, Sharma A and Cosley D (2016) Predictability of Popularity: Gaps between Prediction and Understanding.
Su B, Wang Y and Liu Y (2016) A new popularity prediction model based on lifetime forecast of online videos. 2016 IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC), 376–380. https://doi.org/10.1109/ICNIDC.2016.7974600
Subramanian S, Baldwin T and Cohn T (2018) Content-based popularity prediction of online petitions using a deep regression model. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2, 182–188. https://doi.org/10.18653/v1/p18-2030
Szabo G, Huberman BA (2011) Predicting the popularity of online content. SSRN Electron J 53(8):80–88. https://doi.org/10.2139/ssrn.1295610
Tan Z, Zhang Y (2019) Predicting the Top-N popular videos via a cross-domain hybrid model. IEEE Trans Multimedia 21(1):147–156. https://doi.org/10.1109/TMM.2018.2845688
Tang S, Blenn N, Doerr C, Van Mieghem P (2011) Digging in the digg social news website. IEEE Trans Multimedia 13(5):1163–1175. https://doi.org/10.1109/TMM.2011.2159706
Tatar A, Leguay J, Antoniadis P, Limbourg A, de Amorim MD, Fdida S (2011) Predicting the popularity of online articles based on user comments. Proc Int Conf Web Int, Mining Semantics. https://doi.org/10.1145/1988688.1988766
Tatar A, de Amorim MD, Fdida S, Antoniadis P (2014) A survey on predicting the popularity of web content. J Internet Serv Appl 5(1):1–20. https://doi.org/10.1186/s13174-014-0008-y
Tatar A, Antoniadis P, De Amorim MD and Fdida S (2012). Ranking news articles based on popularity prediction. Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, 106–110. https://doi.org/10.1109/ASONAM.2012.28
Timoshenko A, Hauser JR (2019) Identifying customer needs from user-generated content. Mark Sci 38(1):1–20. https://doi.org/10.1287/mksc.2018.1123
Trzciński T, Andruszkiewicz P, Bocheński T and Rokita P (2017) Recurrent neural networks for online video popularity prediction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10352 LNAI, 146–153. https://doi.org/10.1007/978-3-319-60438-1_15
Trzcinski T, Rokita P (2017) Predicting popularity of online videos using support vector regression. IEEE Trans Multimedia 19(11):2561–2570. https://doi.org/10.1109/TMM.2017.2695439
Tsagkias M, Weerkamp W, De Rijke M (2009) Predicting the volume of comments on online news stories. Int Conf Inform Know Manag Proc. https://doi.org/10.1145/1645953.1646225
Tsagkias M, Weerkamp W, de Rijke M (2010) News Comments: Exploring, Modeling, and Online Prediction. Advances in Information Retrieval. Lecture Notes in Computer Science, Berlin, Heidelberg, pp 191–203. https://doi.org/10.1007/978-3-642-12275-0_19
Tsagkias M (2017) UvA-DARE ( Digital Academic Repository ) Mining social media : tracking content and predicting behavior.
Tsur O and Rappoport A (2012) What’s in a Hashtag? Content based Prediction of the Spread of Ideas in Microblogging Communities. WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 643–652. https://doi.org/10.1145/2124295.2124320
Van Mieghem P, Blenn N, Doerr C (2011) Lognormal distribution in the digg online social network. Eur Phys J B 83(2):251–261. https://doi.org/10.1140/epjb/e2011-20124-0
Wallenta C, Ahmed M, Brown I, Hailes S and Huici F (2008) Analysing and modelling traffic of systems with highly dynamic user generated content. Retrieved from http://web4.cs.ucl.ac.uk/staff/C.Wallenta/research/wallenta_RN_08_10.pdf
Wang C, Ye M, Huberman BA (2012) From user comments to on-line conversations. Proc ACM SIGKDD Int Conf Knowledge Discovery Data Mining. https://doi.org/10.1145/2339530.2339573
Wang Z, Zhou J, Ma J, Li J, Ai J, Yang Y (2020) Discovering attractive segments in the user-generated video streams. Inform Proc Manag. https://doi.org/10.1016/j.ipm.2019.102130
Wang A, Zhang C and Xu Y (2016) A first view on mobile video popularity as time series. HotPOST 2016 - Proceedings of the 8th MobiHoc International Workshop on Hot Topics in Planet-Scale MObile Computing and Online Social Networking, 7–12. https://doi.org/10.1145/2944789.2944872
Wang K, Bansal M and Frahm JM (2018a). Retweet wars: tweet popularity prediction via dynamic multimodal regression. In: Proceedings - 2018a IEEE winter conference on applications of computer vision, WACV 2018a, 2018a-Janua, pp 1842–1851. https://doi.org/10.1109/WACV.2018.00204
Wang X, Fang B, Zhang H and Su S (2018b) Predicting the popularity of online content based on the weak ties theory. In: Proceedings - 2018b IEEE 3rd international conference on data science in cyberspace, DSC 2018b, pp 386–391. https://doi.org/10.1109/DSC.2018.00062
Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Sci Rep. https://doi.org/10.1038/srep00335
Weng L, Menczer F, Ahn Y-Y (2013) Virality prediction and community structure in social networks. Sci Rep 3:2522. https://doi.org/10.1038/srep02522
Wongsuparatkul E and Sinthupinyo S (2020) View count of online videos prediction using clustering view count patterns with multivariate linear model. In: Proceedings of the 8th International Conference on Computer and Communications Management, 123–129. https://doi.org/10.1145/3411174.3411186
Wu J, Zhou Y, Chiu DM, Zhu Z (2016b) Modeling dynamics of online video popularity. IEEE Trans Multimedia 18(9):1882–1895. https://doi.org/10.1109/TMM.2016.2579600
Wu T, Timmers M, Vleeschauwer DD and Leekwijck WV (2010) On the Use of Reservoir Computing in Popularity Prediction. 2010 2nd International Conference on Evolving Internet, pp 19–24. https://doi.org/10.1109/INTERNET.2010.13
Wu B, Mei T, Cheng W-H and Zhang Y (2016a) Unfolding temporal dynamics: predicting social media popularity using multi-scale temporal decomposition. https://doi.org/10.13140/RG.2.2.27504.66565
Wyrwoll C (2014) 10102010103.Pdf. In Social Media. https://doi.org/10.1007/978-3-658-06984-1
Xiong J, Yu L, Zhang D, Leng Y (2021) DNCP: An attention-based deep learning approach enhanced with attractiveness and timeliness of News for online news click prediction. Inform Manag. https://doi.org/10.1016/j.im.2021.103428
Xu J, Van Der Schaar M, Liu J, Li H (2015) Forecasting popularity of videos using social media. IEEE J Sel Top Sign Proces 9(2):330–343. https://doi.org/10.1109/JSTSP.2014.2370942
Xu W, Shi P, Huang J, Liu F (2018) Understanding and predicting the peak popularity of bursting hashtags. J Comput Sci 28:328–335. https://doi.org/10.1016/j.jocs.2017.10.017
Yang J, Leskovec J (2011) Patterns of temporal variation in online media. Proc Fourth ACM Int Conf Web Search Data Mining. https://doi.org/10.1145/1935826.1935863
Yano T, Cohen W and Smith N (2009) Predicting response to political blog posts with topic models. pp 477–485. https://doi.org/10.3115/1620754.1620824
Yin P, Luo P, Wang M and Lee WC (2012) A straw shows which way the wind blows: ranking potentially popular items from early votes. WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 623–632. https://doi.org/10.1145/2124295.2124370
Yu L, Cui P, Wang F, Song C, Yang S (2015) From micro to macro: uncovering and predicting information cascading process with behavioral dynamics. IEEE Int Conf Data Mining 2015:559–568
Zaman T, Fox EB, Bradlow ET (2014) A bayesian approach for predicting the popularity of tweets. Annal Appl Statist 8(3):1583–1611. https://doi.org/10.1214/14-AOAS741
Zhang Z, Yin Z, Wen J, Sun L, Su S, Yu P (2021) DeepBlue: Bi-layered LSTM for tweet popularity estimation. IEEE Trans Know Data Eng 34(10):4737–4752. https://doi.org/10.1109/TKDE.2021.3049529
Zhang W and Skiena S (2009) Improving movie gross prediction through news analysis. In: Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009, 1, 301–304. https://doi.org/10.1109/WI-IAT.2009.53
Zhao Q, Erdogdu MA, He HY, Rajaraman A, and Leskovec J (2015) SEISMIC: a Self-exciting point process model for predicting tweet popularity. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1513–1522. https://doi.org/10.1145/2783258.2783401
Zhou Y, Wu Z, Zhou Y, Hu M, Yang C, Qin J (2019) Exploring popularity predictability of online videos with Fourier transform. IEEE Access 7:41823–41834. https://doi.org/10.1109/ACCESS.2019.2907929
Author information
Authors and Affiliations
Contributions
Divya wrote the main manuscript & figures, Vikram Singh & Naveen Dahiya helped with the Tables, Proof-reading work.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Divya Jatain, Singh, V. & Dahiya, N. A multi-perspective micro-analysis of popularity trend dynamics for user-generated content. Soc. Netw. Anal. Min. 12, 147 (2022). https://doi.org/10.1007/s13278-022-00969-7
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-022-00969-7