Our research is broadly concerned with automated undertanding of rich media, esp. pictures and language; and modeling of collective online behaviours. We specialize and innovate in different method in machine leanring and optimization, recent favorites include: stochastic time series models, sequence and language models with neural netoworks, matrix and tensor factorisation, active learning, structured prediction models.

ML for social media

The role and influence of socialbots on Twitter
Papers: ICWSM’18
Measuring and predicting engagement in online videos
Papers: ICWSM’18
Linking epidemic models and Hawkes Processes
Papers: WWW’18
Inferring Private Traits over Time from Wikipedia
Papers: WSDM’16

Image + Language

Other projects

Picturing Everyday Knowledge in Multimedia
Papers: CIKM’16, ACM MM’13

Selected topics of the recent past

Multimedia-hard problems

MM-hard refers to multimedia problems that require human-level insights and perception that can’t be realized with a single algorithmic approach. The notion of MM-hard has the potential to benefit multimedia research in two funda- mental ways. The first is to describe problems in terms of their (machine and human) difficulty; the second is to be able to do problem reduction — that is, convert one problem to another and compare problems.

Paper: IEEE Multimedia ‘14

Tracking visual memes in social media

We propose visual memes, or frequently reposted short video segments, for tracking large-scale video remix in social media. Video remixing is prevalent on social media platforms, it is part of “venacular creativity” (Burgess 2009) where users create “curated selections based on what they liked or thought was important”. Social influence are often characterized from text-based online interactions such as quoting or reweeting (Leskovec 2009). Our tool allows such metric to be developed for visual media. We found that:

  • Over 50% news-related videos contain remixed content, over 70% YouTube authors participate in remixing.
  • Remix probability does not correlate well with traiditional popularity metrics such as view count.
  • Influence analysis on visual remix overtime can reveal content importance and user roles.

Paper: ACM MM’11, TMM’13

Macroscopic patterns in online news discussions

We analyze the ICWSM’11 Spinn3r dataset containing over 60 million English documents. We observe surprising connections among the 161 wikipedia events it covers, and that over half (55%) of users only link to a small fraction of prolific users (1%), a notable departure from the balanced traditional bow-tie model of web content.

Paper: ICWSM ‘12

June 2, 2016
565 words


Recent updates

Getting in touch:
-- drop a line if you are interested in knowing more about our work, collaborating, or joining us.
We have two PhD openings in 2018: one on Modeling Online Attention and one on Picturing Everyday Knowledge. We are also looking for research fellow candidates with passion and compelling track record.
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