The volume of news increases everyday, triggering competition for users’ attention. Predicting which topics will become trendy has many applications in domains like marketing or politics, where it is crucial to anticipate how much interest a product or a person will attract. We propose a model for representing topic popularity behavior across time and to predict if a topic will become trendy in the future. Furthermore, we tested our proposal on a real data set from Yahoo News and analyzed the performance of various classifiers for the topic popularity prediction task. Experiments confirmed the validity of the proposed model.
AhmedM.SpagnaS.HuiciF. and NiccoliniS., A peek into the future: predicting the evolution of popularity in user generated content, In Proceedings of the sixth ACM international conference on Web search and data mining, 2013, pp. 607–616.
2.
ArapakisI.CambazogluB.B. and LalmasM., On the feasibility of predicting popular news at cold start, Journal of the Ass. for Inf. Science and Technology (2017).
3.
ArdonS.BagchiA.MahantiA.RuhelaA.SethA.TripathyR.M. and TriukoseS., Spatio-temporal and events based analysis of topic popularity in twitter, In Procs of ACM Int. Conf. on Information & Knowledge Management, 2013.
4.
BandariR.AsurS. and HubermanB.A., The pulse of news in social media: Forecasting popularity, ICWSM12 (2012), 26–33.
5.
BorattoL.FenuG. and MarrasM., The effect of algorithmic bias on recommender systems for massive open online courses, In Advances in Information Retrieval – 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I, volume 11437 of Lecture Notes in Computer Science, Springer, 2019, pp. 457–472.
6.
BoserB.E.GuyonI.M. and VapnikV.N., A training algorithm for optimal margin classifiers, In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT ’92, 1992, pp. 144–152.
7.
CastilloC.El-HaddadM.PfefferJ. and StempeckM., Characterizing the life cycle of online news stories using social media reactions, In 17th ACM conf. on Computer supported cooperative work & social computing, ACM, 2014, pp. 211–223.
8.
ChoudharyS.SandhuA.S. and PradhanT., Genetic algorithm based correlation enhanced prediction of online news popularity, In Computational Intelligence in Data Mining, Springer, 2017, pp. 133–144.
9.
Council of European Union, Council regulation (EU) no 679/2016, 2016.
10.
CucchiarelliA.MorbidoniC.StiloG. and VelardiP., What to write? a topic recommender for journalists, In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism, Copenhagen, Denmark, Sept. 2017. Association for Computational Linguistics, pp. 19–24.
11.
CucchiarelliA.MorbidoniC.StiloG. and VelardiP., A topic recommender for journalists, Information Retrieval Journal22(1-2) (2019), 4–31.
12.
DeshpandeD., Prediction & evaluation of online news popularity using machine intelligence, In 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), 2017, pp. 1–6.
13.
DowP.A.AdamicL.A. and FriggeriA., The anatomy of large facebook cascades, In ICWSM, 2013.
14.
FernandesK.VinagreP. and CortezP., A proactive intelligent decision support system for predicting the popularity of online news, In Portuguese Conference on Artificial Intelligence, 2015, pp. 535–546.
15.
GuanX.PengQ.LiY. and ZhuZ., Hierarchical neural network for online news popularity prediction, In Chinese Automation Congress’17, 2017, pp. 3005–3009.
16.
HoT.K., Random decision forests, In Proceedings of the Third International Conference on Document Analysis and Recognition, ICDAR, 1995, p. 278.
17.
LanhamR.A., The economics of attention: Style and substance in the age of information, Journal of Communication57 (2007).
18.
LeskovecJ.BackstromL. and KleinbergJ., Meme-tracking and the dynamics of the news cycle, In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 497–506.
19.
LevyP., Collective Intelligence: Mankind’s Emerging World in Cyberspace, Perseus books, 1997.
20.
MillerG.A., The magical number seven, plus or minus two: Some limits on our capacity for processing information, Psychological Review63(2) (1956), 81.
Prado-RomeroM.A.CeliA.StiloG. and Coto-SantiestebanA., Tstm: A model to predict topics’ popularity in news providers, in: NyströmI.Hernández HerediaY. and Milián Nú nezV., editors, Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications, Cham, 2019. Springer International Publishing, pp. 96–106.
23.
RamosG.BorattoL. and CaleiroC., On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms, Information Processing & Management57(2) (2020), 102058.
24.
SaúdeJ.RamosG.CaleiroC. and KarS., Reputation-based ranking systems and their resistance to bribery, in: RaghavanV.AluruS.KarypisG.MieleL. and WuX., editors, 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, November 18–21, 2017, IEEE Computer Society, 2017, pp. 1063–1068.
25.
SurowieckiJ., The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, Little, Brown, 2004.
26.
TatarA.AntoniadisP.AmorimM.D.D. and FdidaS., From popularity prediction to ranking online news, Social Network Analysis and Mining4(1) (2014), 174.
27.
TatarA.de AmorimM.D.FdidaS. and AntoniadisP., A survey on predicting the popularity of web content, Journal Internet Services and Apps5(1) (2014), 8.
28.
P. D. U.S. Census Bureau, Current population survey, annual social and economic supplement, 2013.
29.
Van CanneytS.LerouxP.DhoedtB. and DemeesterT., Modeling and predicting the popularity of online news based on temporal and content-related features, Multimedia Tools and Applications77 (2018), 1409–1436.
ZhaoJ.WuW.ZhangX.QiangY.LiuT. and WuL., A short-term prediction model of topic popularity on microblogs, in: DuD.-Z. and ZhangG., editors, Computing and Combinatorics, 2013, pp. 759–769.
