Assoc. Professor Lewis Mitchell
I'm a lecturer in applied mathematics at the University of Adelaide. My research interests are in computational social science, human dynamics and social networks, data assimilation, and the mathematics of weather and climate. Please see my website, http://maths.adelaide.edu.au/lewis.mitchell/, for further details.
Research Interests:
Computational social science
data assimilation
Human Dynamics
Social Networks
Projects
Publications
Book Chapters
(2017). How the internet knows if you're happy or sad.
The Conversation Yearbook 2017.
Invited talks, refereed proceedings and other conference outputs
(2019). GLaSS: Semi-supervised Graph Labelling with Markov Random Walks to Absorption.
(Aiello, L. Maria, Cherifi C., Cherifi H., Lambiotte R., Lió P., & Rocha L. M., Ed.).Complex Networks and Their Applications. 812, 304-315. doi: 10.1007/978-3-030-05411-3_25
(2018). SMERC: Social media event response clustering using textual and temporal information.
2018 IEEE International Conference on Big Data (Big Data). 8622082. doi: 10.1109/BigData.2018.8622082
(2018). Real-time Detection of Content Polluters in Partially Observable Twitter Networks.
(Champin, P-A., Gandon F., Gandon F., Lalmas M., & Ipeirotis P. G., Ed.).The Web Conference 2018. 1331 - 1339. doi: 10.1145/3184558.3191574
(2018). Super-blockers and the Effect of Network Structure on Information Cascades.
Companion of the The Web Conference 2018. 1435 - 1441. doi: 10.1145/3184558.3191590
(2017). Which friends are more popular than you? Contact strength and the friendship paradox in social networks.
The 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '17), . 103-108. doi: 10.1145/3110025.3110027
(2017). The nature and origin of heavy tails in retweet activity.
8th International Workshop on Modelling Social Media: Machine Learning and AI for Modelling and Analysing Social Media, Perth, 03 Apr 2017 - 07 Apr 2017. 1493-1498. doi: 10.1145/3041021.3053903
Journal Articles
(2018). Tweeting weight loss: A comparison of #thinspiration and #fitspiration communities on Twitter.
Body Image. 25, 133 - 138. doi: 10.1016/j.bodyim.2018.03.002
(2018). The quoter model: A paradigmatic model of the social flow of written information.
Chaos: An Interdisciplinary Journal of Nonlinear Science. 28(7), 075304. doi: 10.1063/1.5011403
(2018). The one comparing narrative social network extraction techniques.
arXiv preprint arXiv:1811.01467.
(2018). Pachinko Prediction: A Bayesian method for event prediction from social media data.
arXiv preprint arXiv:1809.08427.
(2018). Characterising seasonal influenza epidemiology using primary care surveillance data.
PLOS Computational Biology. 14(8), e1006377. doi: 10.1371/journal.pcbi.1006377
(2018). Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study.
Social Network Analysis and Mining. 8(1), 49. doi: 10.1007/s13278-018-0519-9
(2018). A shadowing-based inflation scheme for ensemble data assimilation.
Physica D: Nonlinear Phenomena. 380-381, 1 - 7. doi: 10.1016/j.physd.2018.05.002
(2018). The underestimated dynamics and impacts of water-based recreational activities on freshwater ecosystems.
Environmental Reviews. 26(2), 199 - 213. doi: 10.1139/er-2017-0024
(2017). The Lexicocalorimeter: Gauging public health through caloric input and output on social media.
PLOS ONE. 12(2), e0168893. doi: 10.1371/journal.pone.0168893
(2017). Simon's fundamental rich-get-richer model entails a dominant first-mover advantage.
Physical Review E. 95(5), 052301. doi: 10.1103/PhysRevE.95.052301
(2016). A data-driven model for influenza transmission incorporating media effects.
Royal Society Open Science. 3(10), doi: 10.1098/rsos.160481
(2016). Game story space of professional sports: Australian rules football.
Physical Review E. 93(5), doi: 10.1103/PhysRevE.93.052314
(2016). Tracking climate change through the spatiotemporal dynamics of the teletherms, the statistically hottest and coldest days of the year.
PLOS ONE. 11(5), doi: 10.1371/journal.pone.0154184
(2015). Human language reveals a universal positivity bias.
Proceedings of the National Academy of Sciences. 112(8), 2389-2394. doi: 10.1073/pnas.1411678112
(2015). Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristics: Fig. 1..
Proceedings of the National Academy of Sciences. 112(23), doi: 10.1073/pnas.1505647112
(2015). Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll.
PLOS ONE. 10(8), doi: 10.1371/journal.pone.0136092
(2015). Accounting for model error due to unresolved scales within ensemble Kalman filtering.
Quarterly Journal of the Royal Meteorological Society. 141(689), 1417-1428. doi: 10.1002/qj.2451
