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iOPENER

Our project iOPENER has been featured on NSF Discoveries!

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What is Collective Discourse?

My research ideas have recently been mainly about how we can characterize and exploit collective discourse.
So what is collective discourse?

With the growth of Web 2.0, millions of individuals involve in collective discourse. They participate in  online discussions, share their opinions, and generate  content about the same artifacts, objects, and news  events in Web portals like amazon.com,  epinions.com, imdb.com and so forth. This massive  amount of text is mainly written on the Web by non-expert individuals with different perspectives, and yet  exhibits accurate knowledge as a whole.

In social media, collective discourse is often a  collective reaction to an event. A collective reaction  to a well-defined subject emerges in response to an event (a movie release, a breaking story, a newly  published paper) in the form of independent writings (movie reviews, news headlines, citation sentences)  by many individuals.

A common characteristic of collective discourse, just like many other collective behaviors,  is the diversity among individuals engaging in it. This diversity is emerges in form of diverse perspectives that different people have about the discussed topic.  

The diversity of perspectives in non-expert contributions  in collective discourse can be exploited to discover various aspects about a subject that are otherwise  hard to unveil.

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Some Useful NLP Datasets (to be completed)

Reproducibility and Scientific Findings

What I liked the most about ACL submissions this year was the opportunity to upload datasets and source codes. Although it seems far-fetched to me that reviewers would try to reproduce the results reported in each paper, it at least, to some extend, encourages  reposting transparent, reproducible results.

Thanks to Chris Brockett, who shared an interesting relevant article a few days ago:
“John P. A. Ioannidis. Why Most Published Research Findings Are False. PLoS Medicine”. (a summary of this paper is written by the mathematician John Allen Paulos: http://abcnews.go.com/print?id=12510202)

What I liked about this paper was the 6 corollaries on the validity of scientific findings:

Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true.
Corollary 2: The smaller the effect sizes in a scientific field, the less likely the research findings are to be true.
Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are tobe true.
Corollary 4: The greater the flexibility in designs, definitions,outcomes, and analytical modes in a scientific field, the less likely the research findings are to be true.
Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research fi ndings are to be true.
Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true.

Corollary 6, which is my favorite, implies that the competitive nature in research and the urge to publish have had negative impacts on the quality of published research.

Finally, I am excited about ACL’s action on requesting datasets, and think we will start to see stronger measures in (hopefully) near future.


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