Welcome!

The Computational Media Lab at the ANU focus on computational problems for understanding online media and their interactions with and among humans. We develop core methods in machine learning and optimization, and use them to formulate and solve problems in multimedia undestanding and online behavior analysis.

You may be interested in sampling the recent blog posts below, look at our research summary, publications, all past posts, or navigate by categories and tags, such as: social media deep learning visualization stochastic process popularity privacy data language vision.

Visualizing Citation Patterns of Computer Science Conferences Posted on Aug 18, 2016
We plot the citation behavior over time for different subfields in computer science, using data from microsoft academic graph.


posted by Lexing Xie

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Expecting to be HIP Posted on Aug 16, 2016
This post outlines techniques for computing the expected event rate for Hawkes processes, or the so-called Hawkes Intensity Process (HIP).


posted by Lexing Xie


We give a brief overview to a new method for computing expected event rate in unit time for point processes. This is important for estimating interval-censored Hawkes processes – or when the volume of events, and not individual event times are known.

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How Long Do Papers Survive in the Collective Academic Memory? Posted on Aug 16, 2016
We plot the citation survival rate of many computer science conferences, using data from microsoft academic graph.


posted by Lexing Xie

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Three papers accepted to CIKM 2016 Posted on Jul 19, 2016
Congratulations to all authors!


We have great news! Three papers have been accepted to CIKM 2016.

  • “Feature Driven and Point Process Approaches for Popularity Prediction” - Swapnil Mishra, Marian-Andrei Rizoiu, and Lexing Xie

  • “Learning Ranks and Routes to Recommend Trajectories” - Dawei Chen, Lexing Xie, and Cheng Soon Ong

  • “Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches” - Dongwoo Kim, Lexing Xie, and Cheng Soon Ong

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Where are Ideas Coming from, and Going to? - Measuring citation flow in academic communities Posted on Jul 16, 2016
We plot the incoming and outgoing citation flow of many computer science conferences, using data from microsoft academic graph.


citation summary - NIPS

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Group Activity: Climbing Mt. Kosciuszko Posted on Apr 2, 2016
We climbed to the top of Australia -- Mt. Kosciuszko, braving strong gust and below-freezing windchill!


On the 2nd of April, group members climbed the top of Australia, Mt. Kosciuszko. Kosciuszko is the highest mountain in Australia a mountain with a height of 2,228 metres.

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Discussing two privacy concerns with Wikipedia Posted on Mar 18, 2016
We discussed issues about data quality and user privacy at Wikimedia Research Showcase.


posted by Marian-Andrei Rizoiu

Our work on the Evolution of Privacy Loss has caught the attention of Wikimedia Research – folks who host Wikipedia! We were invited to present our work with the larger Wikimedia community in the March 2016 edition of the Wikimedia Research Showcase and discussed a number of concerns in user privacy.

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Eight Years of WSDM: Increasing Influence and Diversifying Heritage Posted on Feb 22, 2016
We analyze data from microsoft academic graph to quantify the citation and reference patterns of this conference.


posted by Lexing Xie

We analyzed some data available from microsoft academic graph, in order to quantify the scientific heritage and citation impact over the first 8 years of the WSDM conferencetaking place this week in San Francisco! Here are some preliminary observations.

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Evolution of Privacy Loss in Wikipedia Posted on Jan 8, 2016
your online traces reveals who you are, more so over time


posted by Marian-Andrei Rizoiu

The digital traces left behind by the users in the online environment reveal more about them than they might like. As our recent WSDM’16 paper shows, machine learning algorithms can be used to uncover hidden links between an user’s past activity and her private traits – like gender, education level or religious views –, even for retired users.

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SentiCap: Generating Image Descriptions with Sentiments Posted on Jan 3, 2016
A technique and dataset for generating image captions with strong positive or negative sentiment.


posted by Alex Mathews


The recent progress on image recognition and language modeling is making automatic description of image content a reality. However, stylized, non-factual aspects of the written description are missing from the current systems.

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Recent updates

  • 2016-07 We have 3 papers accepted at CIKM 2016 conference, preprints are coming soon.
  • 2016-04 Group activity: We climbed Mt Kosciuszko - the top of Australia!
  • 2016-03 Lexing is elected as an IEEE CAS Distinguished Lecturer 2016-2017.
  • 2016-03 Andrei is featured at the March'16 Wikimedia Research Showcase for his WSDM paper on "Evolution of Privacy Loss". Well done!
  • SentiCap was recently presented at AAAI'16, Feb. 12–17
  • Getting in touch!
    -- if you are interested in knowing more about our work, or collaborating. We're looking for new people to join the team - one example project description here.