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Marc Cheong

Lecturer in Data Science | Researcher in Philosophy

Publications
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About Me

I am a Lecturer in Data Science (currently at Monash University Australia). My research interest is in social network analysis and interdisciplinary applications of both continental and analytic philosophy coupled with data science methods.

With over ten years of academic experience, I am committed to facilitating quality interdisciplinary research and education: with a strong background in data science, social media analysis, and philosophical rigour.

Core skills include:

  • Social Network Analysis and Mining

  • Machine Learning and Natural Language Processing

  • Data Analysis and Visualisation (incl. Tableau)

  • Interdisciplinary IT and Humanities Lecturing

My Philosophy Research

I am interested in the intersection of technology (big data, social media, etc) and philosophy (existentialism, ethics, epistemology, and Experimental Philosophy)

Existentialism

My work deals with the analysis of contemporary social media from an existentialist lens, which has not been as actively researched since the days of de Beauvoir, Sartre, et al. My work currently challenges the notion of authenticity on social media, and why it runs counter to existentialist philosophy.

Ethics and Algorithms

Being trained as a computer scientist, then working as a Human-Computer Interaction (HCI) lecturer and data scientist, I am interested in the intersection between computer science and philosophy - in particular how basic building blocks (of algorithms and programming languages) - have an impact on the human usage of systems, and the wider societal impact (for better or for worse).

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Collaborators: Joanne Byrne (La Trobe)

Epistemology

I have been working together with Prof. Mark Alfano and his research team on applying data science and network analysis methods on the study of social epistemology. My contribution provides an empirical understanding of today's social media landscape. I am also passionate about empowering philosophers with state-of-the-art data science methods.

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Collaborators: Mark Alfano (ACU/TUDelft) | J Adam Carter (Glasgow) | Emily Sullivan (TUDelft)

My Data Science (and IT) Research

As one of the early global pioneers of Twitter research (ca. 2009), my PhD dissertation, "Inferring Social Behavior and Interaction on Twitter by Combining Metadata about Users & Messages", resulted in major contributions in Social Media Metadata analysis and inference generation using state-of-the-art data science approaches (known primarily as pattern recognition and data mining back then).

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Since then, I am recognised as a subject-matter expert in social media analysis, as well as the research methods and computational techniques used - including Python programming, cloud computing (AWS and Google Cloud), Tableau, Python, SQL - to name a few. My data analysis and research acumen have contributed to international research collabroations in a wide range of domains - from marketing, to mental health, to energy policy, to political science.

Social Media Analysis

My pioneering work on Twitter analysis led to the subsequent, ongoing, research interest in social media and social networks in general. Given any social network (real or simulated), I am interested in studying both intrinsic and extrinsic features of the network, with both automated and semi-automated techniques, and cross-disciplinary insights. A key pillar of this is the development and research of robust techniques to computationally perform what is otherwise laborious manual processing.

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Collaborators: David Green (Monash) | Sid Ray (Monash)

Applied Twitter Discourse Analysis

Research projects in this specific niche include (a) pioneering several research techniques, including Twitter-based survey and sampling, as well as natural language processing (combined with my forté of metadata analysis) to identify marketing behviours of craft beer pioneers versus their mainstream counterparts and (b) analysing the discourse on Twitter for energy policy and contemporary energy-related issues in Australia.

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Collaborators: Torgeir Aleti (RMIT) | Sue Bedingfield (Monash) | Paul Harrigan (UWA) | Kerri Morgan (Deakin) | Will Turner

Data Science for Mental Health

I have been part of a diverse research group of subject matter experts in studying low mental health and well-being (MHWB). One such study involved NLP methods for detecting depression markers in conversations, and the other is a mixed-methods approach on the use of social media amongst university students to find out their usage motivations and how it affects MHWB.

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Collaborators: Joanne Byrne (La Trobe) | Yen Cheung (Monash) | Eddie Robinson (Monash) | Jojo Wong (La Trobe)

Honours Students: Sudhir Mandarapu | Zhehui Yang

Selected Publications

Please find below a selected sample of recent publications with links to copies, where possible. The complete list is available on Google Scholar.

A list of publications prior to 2017 is available in archived form, which is a virtual handout shared with participants in the ASNAC 2016 conference, at http://users.monash.edu/~cmarc/asnac/.

Miscellany

This page is a current work in progress and will be expanded in due course.