Media virality

Strategic modelling for content creators

Hey check out this tiktok

Contagion of ideas and opinions is particularly well studied in the case of media. I know a little about about this, thanks to my own masters thesis.

For a deep dive, why not excavate the references in the ANU Computational Media Group which does an excellent job in this realm?


Achab, Massil, Emmanuel Bacry, StΓ©phane GaΓ―ffas, Iacopo Mastromatteo, and Jean-Francois Muzy. 2017. β€œUncovering Causality from Multivariate Hawkes Integrated Cumulants.” In PMLR.
Andris, Clio Maria, Caglar Koylu, and Mason A. Porter. 2021. β€œHuman-Network Regions as Effective Geographic Units for Disease Mitigation.” SocArXiv.
Aral, Sinan, Lev Muchnik, and Arun Sundararajan. 2009. β€œDistinguishing Influence-Based Contagion from Homophily-Driven Diffusion in Dynamic Networks.” Proceedings of the National Academy of Sciences 106 (51): 21544–49.
Barnett, Lionel, Adam B. Barrett, and Anil K. Seth. 2009. β€œGranger Causality and Transfer Entropy Are Equivalent for Gaussian Variables.” Physical Review Letters 103 (23): 238701.
Bessi, Alessandro. 2016. β€œOn the Statistical Properties of Viral Misinformation in Online Social Media.” arXiv:1609.09435 [Physics, Stat], September.
Broniatowski, David A., Amelia M. Jamison, SiHua Qi, Lulwah AlKulaib, Tao Chen, Adrian Benton, Sandra C. Quinn, and Mark Dredze. 2018. β€œWeaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate.” American Journal of Public Health 108 (10): 1378–84.
Dodds, Peter Sheridan. 2017. β€œSlightly Generalized Generalized Contagion: Unifying Simple Models of Biological and Social Spreading.” arXiv:1708.09697 [Physics], August.
Du, Nan, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song. 2016. β€œRecurrent Marked Temporal Point Processes: Embedding Event History to Vector.” In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1555–64. KDD ’16. New York, NY, USA: ACM.
Du, Nan, Le Song, Manuel Gomez-Rodriguez, and Hongyuan Zha. 2013. β€œScalable Influence Estimation in Continuous-Time Diffusion Networks.” In Advances in Neural Information Processing Systems, 3147–55.
Du, Nan, Le Song, Ming Yuan, and Alex J. Smola. 2012. β€œLearning Networks of Heterogeneous Influence.” In Advances in Neural Information Processing Systems, 2780–88.
FernΓ‘ndez, Miriam, Alejandro BellogΓ­n, and IvΓ‘n Cantador. 2021. β€œAnalysing the Effect of Recommendation Algorithms on the Amplification of Misinformation.” arXiv:2103.14748 [Cs], March.
Gomez-Rodriguez, Manuel, Jure Leskovec, David Balduzzi, and Bernhard SchΓΆlkopf. 2014. β€œUncovering the Structure and Temporal Dynamics of Information Propagation.” Network Science 2 (01): 26–65.
Gomez-Rodriguez, Manuel, Jure Leskovec, and Bernhard SchΓΆlkopf. 2013. β€œStructure and Dynamics of Information Pathways in Online Media.” In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, 23–32. WSDM ’13. New York, NY, USA: ACM.
Hurd, T. R., and James P. Gleeson. 2012. β€œOn Watts’ Cascade Model with Random Link Weights.” arXiv:1211.5708 [Cond-Mat, Physics:physics], November.
Jakesch, Maurice, Kiran Garimella, Dean Eckles, and Mor Naaman. 2021. β€œ#Trend Alert: How a Cross-Platform Organization Manipulated Twitter Trends in the Indian General Election.” arXiv:2104.13259 [Cs], April.
Khim, Justin, Varun Jog, and Po-Ling Loh. 2016. β€œComputationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis.” arXiv:1611.00350 [Cs, Stat], November.
Kim, Minkyoung, Lexing Xie, and Peter Christen. 2012. β€œEvent Diffusion Patterns in Social Media,” 8.
Kong, Quyu, Marian-Andrei Rizoiu, Siqi Wu, and Lexing Xie. 2018. β€œWill This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos.” Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18, 175–78.
Kong, Quyu, Marian-Andrei Rizoiu, and Lexing Xie. 2020. β€œModeling Information Cascades with Self-Exciting Processes via Generalized Epidemic Models.” In Proceedings of the 13th International Conference on Web Search and Data Mining, 286–94. WSDM ’20. New York, NY, USA: Association for Computing Machinery.
Liu, Ka-Yuet, Marissa King, and Peter S. Bearman. 2010. β€œSocial Influence and the Autism Epidemic.” American Journal of Sociology 115 (5): 1387.
Pinto, Julio Cesar Louzada, and Tijani Chahed. 2014. β€œModeling Multi-Topic Information Diffusion in Social Networks Using Latent Dirichlet Allocation and Hawkes Processes.” In Proceedings of the 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 339–46. SITIS ’14. Washington, DC, USA: IEEE Computer Society.
Ribeiro, Manoel Horta, Raphael Ottoni, Robert West, VirgΓ­lio A. F. Almeida, and Wagner Meira. 2019. β€œAuditing Radicalization Pathways on YouTube.” arXiv:1908.08313 [Cs], August.
Rizoiu, Marian-Andrei, Timothy Graham, Rui Zhang, Yifei Zhang, Robert Ackland, and Lexing Xie. 2018. β€œ#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate.” arXiv:1802.09808 [Cs], May.
Rizoiu, Marian-Andrei, Swapnil Mishra, Quyu Kong, Mark Carman, and Lexing Xie. 2018. β€œSIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations.” In Proceedings of the 2018 World Wide Web Conference, 419–28. Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee.
Rizoiu, Marian-Andrei, Lexing Xie, Scott Sanner, Manuel Cebrian, Honglin Yu, and Pascal Van Hentenryck. 2017. β€œExpecting to Be HIP: Hawkes Intensity Processes for Social Media Popularity.” In World Wide Web 2017, International Conference on, 1–9. WWW ’17. Perth, Australia: International World Wide Web Conferences Steering Committee.
Roca, Carlos P, Moez Draief, and Dirk Helbing. 2011. β€œPercolate or Die: Multi-Percolation Decides the Struggle Between Competing Innovations.”
Saichev, A., and D. Sornette. 2011. β€œHierarchy of Temporal Responses of Multivariate Self-Excited Epidemic Processes.” arXiv:1101.1611 [Cond-Mat, Physics:physics], January.
Salganik, Matthew J., and Duncan J. Watts. 2008. β€œLeading the Herd Astray: An Experimental Study of Self-Fulfilling Prophecies in an Artificial Cultural Market.” Social Psychology Quarterly 74 (4): 338.
Shalizi, Cosma Rohilla, and Andrew C. Thomas. 2011. β€œHomophily and Contagion Are Generically Confounded in Observational Social Network Studies.” Sociological Methods & Research 40 (2): 211–39.
Sharma, Amit, Jake M. Hofman, and Duncan J. Watts. 2015. β€œEstimating the Causal Impact of Recommendation Systems from Observational Data.” Proceedings of the Sixteenth ACM Conference on Economics and Computation - EC ’15, 453–70.
Shen, Yanning, Brian Baingana, and Georgios B. Giannakis. 2016. β€œNonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity.” arXiv:1610.06551 [Stat], October.
Shin, Minjeong, Alasdair Tran, Siqi Wu, Alexander Mathews, Rong Wang, Georgiana Lyall, and Lexing Xie. 2021. β€œAttentionFlow: Visualising Influence in Networks of Time Series.” In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 1085–88.
Tran, Alasdair, Alexander Mathews, Cheng Soon Ong, and Lexing Xie. 2021. β€œRadflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series.” In Proceedings of the Web Conference 2021, 730–42. Ljubljana Slovenia: ACM.
Watts, Duncan J., and Peter Sheridan Dodds. 2007. β€œInfluentials, Networks, and Public Opinion Formation.” Journal of Consumer Research 34 (4): 441–58.
Whittaker, Joe, SeΓ‘n Looney, Alastair Reed, and Fabio Votta. 2021. β€œRecommender Systems and the Amplification of Extremist Content.” Internet Policy Review 10 (2).
Wu, Siqi, and Paul Resnick. n.d. β€œCross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don’t Talk to Conservatives,” 12.
Wu, Siqi, Marian-Andrei Rizoiu, and Lexing Xie. 2019. β€œEstimating Attention Flow in Online Video Networks.” Proceedings of the ACM on Human-Computer Interaction 3 (CSCW): 1–25.
Xie, Lexing, Apostol Natsev, Xuming He, John Kender, Matthew Hill, and John R. Smith. 2013. β€œTracking Large-Scale Video Remix in Real-World Events.” arXiv:1210.0623 [Cs], May.
Yang, Shuang-Hong, and Hongyuan Zha. 2013. β€œMixture of Mutually Exciting Processes for Viral Diffusion.” In Proceedings of The 30th International Conference on Machine Learning, 28:1–9.
Yu, Honglin, Lexing Xie, and Scott Sanner. 2015. β€œThe Lifecyle of a Youtube Video: Phases, Content and Popularity,” 10.
Zhao, Zhe, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. β€œRecommending What Video to Watch Next: A Multitask Ranking System.” In Proceedings of the 13th ACM Conference on Recommender Systems, 43–51. RecSys ’19. New York, NY, USA: Association for Computing Machinery.

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