Media virality

Strategic modelling for content creators

August 30, 2016 — July 15, 2021

Figure 1: 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?

Figure 2

1 References

Achab, Bacry, Gaïffas, et al. 2017. Uncovering Causality from Multivariate Hawkes Integrated Cumulants.” In PMLR.
Andris, Koylu, and Porter. 2021. Human-Network Regions as Effective Geographic Units for Disease Mitigation.”
Aral, Muchnik, and Sundararajan. 2009. Distinguishing Influence-Based Contagion from Homophily-Driven Diffusion in Dynamic Networks.” Proceedings of the National Academy of Sciences.
Barnett, Barrett, and Seth. 2009. Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables.” Physical Review Letters.
Bessi. 2016. On the Statistical Properties of Viral Misinformation in Online Social Media.” arXiv:1609.09435 [Physics, Stat].
Broniatowski, Jamison, Qi, et al. 2018. Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate.” American Journal of Public Health.
Dodds. 2017. Slightly Generalized Generalized Contagion: Unifying Simple Models of Biological and Social Spreading.” arXiv:1708.09697 [Physics].
Du, Dai, Trivedi, et al. 2016. Recurrent Marked Temporal Point Processes: Embedding Event History to Vector.” In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16.
Du, Song, Gomez-Rodriguez, et al. 2013. Scalable Influence Estimation in Continuous-Time Diffusion Networks.” In Advances in Neural Information Processing Systems.
Du, Song, Yuan, et al. 2012. Learning Networks of Heterogeneous Influence.” In Advances in Neural Information Processing Systems.
Fernández, Bellogín, and Cantador. 2021. Analysing the Effect of Recommendation Algorithms on the Amplification of Misinformation.” arXiv:2103.14748 [Cs].
Gomez-Rodriguez, Leskovec, Balduzzi, et al. 2014. Uncovering the Structure and Temporal Dynamics of Information Propagation.” Network Science.
Gomez-Rodriguez, Leskovec, and Schölkopf. 2013. Structure and Dynamics of Information Pathways in Online Media.” In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. WSDM ’13.
Hurd, and Gleeson. 2012. On Watts’ Cascade Model with Random Link Weights.” arXiv:1211.5708 [Cond-Mat, Physics:physics].
Jakesch, Garimella, Eckles, et al. 2021. #Trend Alert: How a Cross-Platform Organization Manipulated Twitter Trends in the Indian General Election.” arXiv:2104.13259 [Cs].
Khim, Jog, and Loh. 2016. Computationally Efficient Influence Maximization in Stochastic and Adversarial Models: Algorithms and Analysis.” arXiv:1611.00350 [Cs, Stat].
Kim, Xie, and Christen. 2012. “Event Diffusion Patterns in Social Media.”
Kong, Rizoiu, Wu, et al. 2018. Will 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.
Kong, Rizoiu, and Xie. 2020. Modeling Information Cascades with Self-Exciting Processes via Generalized Epidemic Models.” In Proceedings of the 13th International Conference on Web Search and Data Mining. WSDM ’20.
Liu, King, and Bearman. 2010. Social Influence and the Autism Epidemic.” American Journal of Sociology.
Pinto, and Chahed. 2014. Modeling 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. SITIS ’14.
Ribeiro, Ottoni, West, et al. 2019. Auditing Radicalization Pathways on YouTube.” arXiv:1908.08313 [Cs].
Rizoiu, Graham, Zhang, et al. 2018. #DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate.” arXiv:1802.09808 [Cs].
Rizoiu, Mishra, Kong, et al. 2018. SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations.” In Proceedings of the 2018 World Wide Web Conference.
Rizoiu, Xie, Sanner, et al. 2017. Expecting to Be HIP: Hawkes Intensity Processes for Social Media Popularity.” In World Wide Web 2017, International Conference on. WWW ’17.
Roca, Draief, and Helbing. 2011. Percolate or Die: Multi-Percolation Decides the Struggle Between Competing Innovations.”
Saichev, and Sornette. 2011. Hierarchy of Temporal Responses of Multivariate Self-Excited Epidemic Processes.” arXiv:1101.1611 [Cond-Mat, Physics:physics].
Salganik, and Watts. 2008. Leading the Herd Astray: An Experimental Study of Self-Fulfilling Prophecies in an Artificial Cultural Market.” Social Psychology Quarterly.
Shalizi, and Thomas. 2011. Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.” Sociological Methods & Research.
Sharma, Hofman, and Watts. 2015. Estimating the Causal Impact of Recommendation Systems from Observational Data.” Proceedings of the Sixteenth ACM Conference on Economics and Computation - EC ’15.
Shen, Baingana, and Giannakis. 2016. Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity.” arXiv:1610.06551 [Stat].
Shin, Tran, Wu, et al. 2021. AttentionFlow: Visualising Influence in Networks of Time Series.” In Proceedings of the 14th ACM International Conference on Web Search and Data Mining.
Tran, Mathews, Ong, et al. 2021. Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series.” In Proceedings of the Web Conference 2021.
Watts, and Dodds. 2007. Influentials, Networks, and Public Opinion Formation.” Journal of Consumer Research.
Whittaker, Looney, Reed, et al. 2021. Recommender Systems and the Amplification of Extremist Content.” Internet Policy Review.
Wu, and Resnick. n.d. “Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don’t Talk to Conservatives.”
Wu, Rizoiu, and Xie. 2019. Estimating Attention Flow in Online Video Networks.” Proceedings of the ACM on Human-Computer Interaction.
Xie, Natsev, He, et al. 2013. Tracking Large-Scale Video Remix in Real-World Events.” arXiv:1210.0623 [Cs].
Yang, and Zha. 2013. Mixture of Mutually Exciting Processes for Viral Diffusion. In Proceedings of The 30th International Conference on Machine Learning.
Yu, Xie, and Sanner. 2015. “The Lifecyle of a Youtube Video: Phases, Content and Popularity.”
Zhao, Hong, Wei, et al. 2019. Recommending What Video to Watch Next: A Multitask Ranking System.” In Proceedings of the 13th ACM Conference on Recommender Systems. RecSys ’19.