About

I am a postdoctoral researcher at the Oxford Internet Institute, Unviersity of Oxford, working on the Computational Propaganda project.

I received my PhD in Computer Science from the University of Southampton, UK. I was in the Web and Internet Science group, supervised by Prof Luc Moreau and Prof Susan Halford. My PhD focused on causal inference for estimating the social influence of online communications on real-world outcomes, at the individual and at the collective level. My PhD research was honoured with the Best Poster award for the poster accompanying my full-length paper (in proceedings) at the 2016 International Conference on Social Informatics in Seattle, Washington.

I hold a PhD in Computer Science from the University of Southampton (2018), an MSc in Operational Research from the University of Southampton (2014, Distinction, dissertation prize, full scholarship), and a BA (Hons) in Computer Science from the University of Cambridge (2012, 2.1, dissertation high commendation).


Research

Research Interests

My research focus is on developing computational models and methods for understanding and analysing patterns of behaviour in online interactions, informed by the social sciences. My research interests relate to the areas of online social influence, causal inference, social network analysis, computational social science, and data science.

Publications

Liotsiou, D. and Howard, P. N. (2019). Measuring the Influence of Online Misinformation: A Hierarchy of Social Media Data. The 5th Annual International Conference on Computational Social Science (IC2S2). 2019, Amsterdam, Netherlands. [extended abstract, poster]

Liotsiou, D., Kollanyi, B., and Howard, P. N. (2019). The Junk News Aggregator: Examining junk news posted on Facebook, starting with the 2018 US Midterm Elections. arXiv preprint arXiv:1901.07920.
Press​​ coverage: TechCrunch​, ​ Newsweek​, the Bulletin of the Atomic Scientists (video interview).

Howard, P. N., Ganesh, B., Liotsiou, D., Kelly, J., & François, C. The IRA, Social Media and Political Polarization in the United States, 2012-2018. (2018). Working Paper 2018.2. Oxford, UK: Project on Computational Propaganda.comprop.oii.ox.ac.uk. 46 pp. (Author order: Oxford P.I., then Oxford postdocs alphabetically, then collaborators from Graphika).
Selected news coverage: MSNBC interview, The Washington Post (cover story above the fold and further articles), The New York Times (cover story above the fold and further articles), PBS News Hour, ABC News, BBC, The Guardian, The Independent, Ars Technica (with interview quotes), Yahoo Finance (with interview quotes) [more details]

Liotsiou, D., Moreau, L., & Halford, S. (2016, November). Social Influence: From Contagion to a Richer Causal Understanding. In International Conference on Social Informatics (pp. 116-132). Springer International Publishing. [paper on SpringerLink, poster as pdf] Best Poster Award for the accompanying poster, 17-page full length paper in proceedings.

Working Papers

Liotsiou, D., Moreau, L., & Halford, S. A Causal Methodological Framework for Conceptualising and Measuring Social Influence in Online Communications Using Observational Data.

Liotsiou, D., Moreau, L., & Halford, S. Key Limitations of the Contagion Paradigm for Online Social Influence, and how to Address them.

Liotsiou, D., Kollanyi, B. & Howard, P. Comparing social media Engagement across Traditional News, Online News, and Junk News, in the Context of the 2018 US Midterm Elections.

Theses

Liotsiou D. (2018, October). Measuring the Social Influence of Online Communications at the Individual and Collective Level: A Causal Framework. PhD Thesis. [abstract]

Liotsiou D. (2014, September). Projecting Dental Care Need in England over the Next 20-30 Years. Masters Thesis. Sponsor Award.

Liotsiou D. (2012, June). Parallelising Ant Colony Optimisation-based Solutions to the Vehicle Routing Problem in Scala. Undergraduate Thesis. High Commendation.

Talks

Service

Reviewer for ACM Conference on Hypertext and Social Media (2019), EPJ Data Science (2018).



Teaching

During my PhD, I demonstrated and/or marked for the following courses and short seminars:

Courses

  • Java programming labs, undergraduate-level (demonstator)
  • Funtional programming in Scheme, undergraduate-level (marker)
  • Social network analysis, postgraduate-level (group project mentor)
  • Software engineering, undergraduate-level (group project mentor)

Seminars

  • Introduction to Data Science in Python, for secondary-education teachers
  • Introduction to Machine Learning in Python, for PhD students



Resources

Causality

Some good starting points for causal inference can be found below. This is by no means intended to be an exhaustive list, and the resources are in no particular order. I imagine I will be updating it from time to time. Some good resources for getting started on causality, which use Judea Pearl's do-calculus approach to causality:

  • Judea Pearl's 1999 IJCAI Award lecture on causality. On his website he has some more recent slides and tutorials too, but this is one of the two resources he recommends starting with.
  • Judea Pearl's overview paper on causality. It is very well written and fun to read. This paper is like a short version of his "Causality" book (see below).
  • Judea Pearl's Primer book on Causal Inference in Statistics (co-written with Madelyn Glymour and Nicholas P. Jewell). This is a very accessible introduction to causality and how to practically apply causal methods to data.
  • Judea Pearl's book on Causality, which is much longer and more technical than the Primer, and very well written.
  • Cosma Shalizi at Carnegie Mellon covers causality really well in his notes/book-in-progress "Advanced Data Analysis from an Elementary Point of View", chapters 20, and 24-28.
Beyond those, some other good resources (some using the potential outcomes framework rather than/in addition to Pearl's do-notation) are:
  • The Morgan-Winship book "Counterfactuals and Causal Inference", the 2nd edition. Particularly: chapter 1.1, parts II (especially 2.1-2.5), III (especially 3.1)
  • A brief overview of causal inference, focused on health-related applications, from the Causal Inference group at the Centre for Statistical Methodology of the London School of Hygiene and Tropical Medicine.
  • Blog posts:
    • Adam Kelleher's series of posts on Causal Data Science on Medium, which offers a very accessible introduction to causality.
    • Pearl's causality blog.
    • The "Statistical Modeling, Causal Inference, and Social Science" blog.
  • Materials from University courses:
    • David Blei at Columbia has started a course on causality, his reading list includes many of the above sources, among others.
    • Thomas Richardson at the University of Washington also teaches a course with this reading list.


Some conferences, workshops, meetings and other academic venues on causality are listed below, from 2017-2018, again in no particular order:

Personal

In my free time I enjoy drawing and painting (gouache and watercolor), photography (analogue and digital). I post my drawings and photographs on Instagram.

I also enjoy singing and playing music (guitar, ukelele). I have received classical trainng in music, having completed advanced music studies at the Conservatoir of Northern Greece, 1998-2007: Certificate in Theory of Music (Advanced Harmony, Figured Bass, Solfège, Dictée, Counterpoint, History and Morphology of Music, Choir, Piano) from the Greek Ministry of Culture, equivalent to ABRSM Music Theory Grade 7, and classical guitar studies (advanced, level 6/9). I also sung in choirs in Greece as a soprano, including singing solo, having won awards in national competitions with my school choir, and having performed Benjamin Britten's War Requiem with the Cyril and Methodius choir in conjunction with the London Symphony Chorus (2002).

Contact

Dimitra (Mimie) Liotsiou,
Oxford Internet Institute, University of Oxford,
1 St Giles, Oxford, OX1 3JS,
United Kingdom

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