From Event Detection to Storytelling on...
Transcript of From Event Detection to Storytelling on...
![Page 1: From Event Detection to Storytelling on Microblogsacsweb.ucsd.edu/~jkalyana/papers/ASONAM_presentation.pdf · 2016-08-21 · - Groundtruth topics: hashtags. Topic Detection Baselines](https://reader033.fdocuments.us/reader033/viewer/2022050418/5f8de9f8b5e52a2a89629c3b/html5/thumbnails/1.jpg)
From Event Detection to Storytelling on Microblogs
Janani Kalyanam* Sumithra Velupillai Mike Conway Gert Lanckriet
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Research PipelineInteresting question
Collect data Analyze data (fit statistical models)
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Research PipelineInteresting question
Collect data Analyze data (fit statistical models)
![Page 4: From Event Detection to Storytelling on Microblogsacsweb.ucsd.edu/~jkalyana/papers/ASONAM_presentation.pdf · 2016-08-21 · - Groundtruth topics: hashtags. Topic Detection Baselines](https://reader033.fdocuments.us/reader033/viewer/2022050418/5f8de9f8b5e52a2a89629c3b/html5/thumbnails/4.jpg)
Photo credit: https://marqana.com/wp-content/uploads/embed_toomany.jpg
How do we make sense of this?
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Some tools
- Topic Modeling
- Event Detection
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Some tools - limitations
- Topic Modeling - very short documents
- Event Detection - detect only the onset of events
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Our focus in this work...
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Our focus
- Topic Modeling - very short documents
- Event Detection - detect only the onset of events
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Model
doc-term doc-top top-term
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Model
Very sparse
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Observation
The size of the vocabulary increases only marginally with increasing number of documents.
(Yan et. al. 2013)
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ModelThe term-by-term matrix is relatively denser.
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ModelThe term-by-term matrix is relatively denser.
Hence, decompose
term-term term-top top-term
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Event ProgressionAssume a set of documents arrive at every time step
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Event ProgressionAssume a set of documents arrive at every time step
Relate the current data to the past history
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Event ProgressionAssume a set of documents arrive at every time step
Relate the current data to the past history
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Event Progression
top-top top-term
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Quick Aside:
The “tracking matrix” - helps connect the present to the past. This will help build the “timelines” for events.
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Model
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Model
KNOWN
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Optimization- Collective Matrix Factorization (Singh and Gordon 2008)
- Details in the paper
- Implementation on github
https://github.com/kjanani/matrix_factorization/blob/master/matrix_factorization.py
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Interesting Question
Ebola Outbreak 2014. What really happened?
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Interesting Question: GoalsEbola Outbreak 2014. What really happened?
- Want list of all events that occurred.
- How did they progress?
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Tasks
- Topic Detection
- Event timelines
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Topic Detection
- Estimated topics: Estimate at every timestep. Each row is a distribution over words.
- Groundtruth topics: hashtags
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Topic Detection Baselines
- Two baselines from the event detection literature
- A few classic topic modeling baselines (NMF, LDA e.t.c.)
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Topic Detection Results
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Event Timelines
How to come up with timelines of events?
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Event Timelines
How to come up with timelines of events?
Look at the tracking matrix
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Tracking Matrix
- Tracking matrix is a square matrix of all positive entries
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Tracking Matrix
- Tracking matrix is a square matrix of all positive entries- It show how the topics have changed from to - Row-i tells how topic-i at time is related to all the topics at time
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New events
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Continuing events
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Evolving events
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Entropy H(X)
- Quantifies the amount of “randomness”
- Range [0, 2.32]
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Based on Entropy
- < 1 → continuing events- 1 <= H(X) <= 2 → evolving events- > 2 → new events- Also ending events
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Timeline Generation
- Look at heatmap- Connect dots to the previous timesteps (if possible)- Else, new events, or noise
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Timeline
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Example continuing events - memes
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Example of evolving events
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Thank you!
Questions?
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Photo credit: http://www.realityblurred.com/realitytv/images/2015/03/mtv-the-real-world.jpg AND http://globalpse.org/en/wp-content/uploads/2016/04/Social-media-e1459493030525.jpg
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Social Media Era