jodischneider.com/blog

A great image “Four types of evidence” appears in a recent paper on probabalistic argumentation schemes¹. The delineation of 4 types of evidence² serves the larger goal of the paper — which is to describe how to combine evidence of different types.

1. ‘Dempster-Shafer Argument Schemes‘ by Yuqing Tang, Nir Oren, Simon Parsons, and Katia Sycara (2013) in Proceedings of ArgMAS 2013. [↩]
2. These, the authors mention, were drawn from an earlier technical report: K. Stentz and S. Ferson. Combination of evidence in Dempster-Shafer theory. Technical Report SAND 2002-0835, Sandia National Laboratories, 2002. See especially pages 10-13. The context in that technical report, is sensor fusion using Dempster-Shafer Theory, which as I have since learned, is a common approach to combination of evidence. [↩]

Argumentation analysis can be simplified by thinking about the patterns used.
But what are the key patterns? Here are two diagrams showing different views.

Rahwan suggests 5 common basic argument structures: single, linked, convergent, serial, and divergent.¹

Iyad Rahwan. Mass argumentation and the Semantic Web. 2008.

Meanwhile, Wei and Praken give 5 possible argumentative structures that have one or two inferences.²

From Bin Wei and Henry Prakken. Defining the structure of arguments with AI models of argumentation.

Why 5 structures? Five connected structures emerge from having two types of inference — as unit I (single) and unit II (linked) inference. With two inferences of either type, we can make five patterns:³

(1) unit I argument (single)
(2) unit II argument (linked)
(3) multiple unit I argument (serial)
(4) multiple unit II argument
(5) mixed argument

What is interesting is to look at the differences: Rahwen doesn’t cover (4) multiple unit II and (5) mixed arguments. Meanwhile, Wei and Prakken’s list doesn’t include Rahwen’s convergent & divergent argumentation.

So which are the key patterns?

Single and linked arguments are fundamental, and serial arguments are mathematically simple and Rahwen suggests that they are common in use.⁴But the rest?

Convergent & divergent argumentation structures are both candidates: Wei and Prakken don’t cover these, I suspect, since each could be separated into two separate single arguments, which have the same premise (divergent) or conclusion (convergent). These structures can be important in practice: Convergent arguments give multiple reasons for coming to a conclusion — essential when no single reason suffices. The structure of divergent arguments seems to me to be most useful for showing contradictions in diverse conclusions, e.g. for reductio ad absurdum arguments; I’d love a real-world example of a divergent argument where keeping this structure is important.

1. Iyad Rahwan. Mass argumentation and the Semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web, 6(1):29–37, February 2008. http://dx.doi.org/10.1016/j.websem.2007.11.007 [↩]
2. Bin Wei and Henry Prakken. Defining the structure of arguments with AI models of argumentation. Computational Models of Natural Argument XII at ECAI 2012. Pages 60-64 in Proceedings. [↩]
3. Definition 9. The types of arguments can be defined as follows:
   (1) An argument A is a unit I argument iff A has the form B ⇒ ψ and subargument B is an atomic argument B : φ. We call the inference rule φ ⇒ ψ a unit I inference.
   (2) An argument A is a unit II argument iff A has the form B1,…,Bn ⇒ ψ and subarguments A : B1,…,Bn are atomic arguments B1 : φ1 ,. . . ,Bn : φn . We call the inference rule φ1,…,φn ⇒ ψ a unit II inference.
   (3) An argument A is a multiple unit I argument iff all inferences r1, . . . , rn in the argument A are unit I inferences.
   (4) An argument A is a multiple unit II argument iff all inferences r1, . . . , rn in the argument A are unit II inferences.
   (5) An argument A is a mixed argument iff A has at least one unit I subargument and unit II subargument.
   We display the diagrams of argument types in Figure 3. For simplicity, we assume n = 2 in these diagrams and show only one case of a mixed argument. [↩]
4. Statistics on argument use would be valuable, but we have limited information about this. Aracuaria DB? Output from argumentation mining? [↩]

Last March¹ I gave a reading group talk about knowledge representations of online disputes:

Titled “Turning social disputes into knowledge representations”, the talk was based primarily on two papers:

• Toni and Torroni. Bottom-up Argumentation. In: First International Workshop on the Theory and Applications of Formal Argumentation 2011 (TAFA), 16-22 July, 2011, Barcelona, Spain. http://www.doc.ic.ac.uk/~ft/PAPERS/tafaPT.pdf
• Benn, Buckingham Shum, Domingue, and Mancini. Ontological Foundations for Scholarly Debate Mapping Technology. In: 2nd International Conference on Computational Models of Argument (COMMA ’08), 28-30 May, 2008, Toulouse, France. http://oro.open.ac.uk/11939/

Online argumentation, and particularly knowledge representation from argumentation, is the overarching theme of my dissertation at DERI and as I get together the overall argument, I’ve been looking through my old slidedecks. My previous reading group talk, from November 2011, was about Using Controlled Natural Language and First Order Logic to improve e-consultation discussion forums, based on several papers by Adam Wyner and his colleagues; more recently Adam and I have started a fruitful collaboration, funded in part by the COST action on argumentation and a Short-Term Travel Fellowship from Science Foundation Ireland.

1. March 28, 2012 [↩]

There may be an implied relationship between tweets (as between sentences) which is not made explicit.

Androgyny is a key trait of the most successful performers.
(Because)
A person’s fame depends on fans of the opposite sex who wish to be that person.

(via the twitterfunding list from the twitterfunding experiment).

See also: my favorite example argument on Twitter.

A tabletop storytelling interface called a Narration Negotiation and Reconciliation Table allows disagreements to be visually represented:

Points of Disagreement… can be dragged onto any part of a story to explicitly denote disagreement without preventing the story from continuing.

From A Reflection on Using Technology for Reconciliation through Co-Narration (PDF) by Oliviero Stock, Massimo Zancanaro of FBK-irst, Italy and Chaya Koren, Zvi Eisikovitz, Patrice L. (Tamar) Weiss of University of Haifa, Israel. In the CHI2012 HCI for Peace workshop.
The mutltitouch table interface was tested for peace reconciliation work with Israeli-Jewish and Palestinian-Arab teen boys.

I’d love a screenshot. Quick searching turned up a project description and an (unrelated) discussion of the role of narrative in reconciliation. I excerpt:

The textbooks juxtaposed both historical narratives on the same page: on the right side of the page, the Israeli narrative began with the birth of Zionism in the 19th century; on the left, the Palestinian narrative commenced with Napolean’s plans to establish a Jewish state in Palestine. Historical events faced off like soldiers in trenches; and while students were scrutinizing their positions, they were simultaneously recongnizing their own involvement in the conflict. This, of course, was an intended pedagogical tool carefully thought out by the authors of the book.

From Political Reconciliation and Narrative Negotiation (PDF): by Nadim Khoury of the Department of Politics at the University of Virginia.

This points out the obvious: reconciliation first requires understanding and externally representing the disagreements. Rooting out the disagreement in mundane situations discussed online, and providing representations for them, are a big part of my current work.

“Wikipedia discussions can thus be seen as a mirror of a stream of public consciousness, where those elements which are still not part of a shared consolidated heritage are object of a continuous negotiation among different points of view.”

There is No Deadline – Time Evolution of Wikipedia Discussions. (2012) Andreas Kaltenbrunner, David Laniado. arXiv:1204.3453v1

via summarizing it for Wikipedia Signpost, longer summary space on AcaWiki

Long discussions cause challenges for Wikipedians. That’s great motivation from some of my work.

Such discussions can often present a challenge to the editor who steps up to close them; “no consensus” is a common outcome for convoluted debates, a lack of resolution that opens the possibility of discussion starting all over again as the same issues continue to arise.

- Wikipedia Signpost, 2012-03-19

The report also links to Wikipedia’s essay on Too long; didn’t read; image from KnowYourMeme’s coverage of tl;dr.

Marcel asked:
>http://bottlenose.com/
>anyone with experiences and opinions about it?

Definitely worth trying–it focuses on your network in order to pull more interesting stuff to the fore. I put it in my bookmar bar when I first encountered it — it was briefly useful (slowed down the stream, found things that my network had heavily retweeted, making interesting suggestions of the few things I should read).

Its classification is ok — the genre classification seems decent (news/videos/pictures) — the message type classification (Question/Opinion/Notification/Check-In/How-To/etc) seems less exact, but may still be useful.

It kept suggesting the same things so I stopped checking it regularly — but I just checked it and am intrigued since they’ve added some features. In particular, they seem to be pulling out keywords (you can visualize one/all of people, topics, hashtags, message types–see screenshot). That might be especially interesting when doing exploratory searches.

There’s also a lot of customization possible — you can make your own rules for what to put in streams, and they have a wizard (screenshot below):

If there were a marketplace for sharing rules, that might be good — I’m not likely to spend time on customizing my own, so I’m just relying on the defaults (‘suggested for you’ and ‘popular’).

I’d be cautious of posting from Bottlenose without first checking the documentation — they accept posts of any length, but may also modify them (add hashtags, say).

I suppose for some people, the ability to pull in from multiple networks (for now Twitter & Facebook) could be useful, though there are lots of tools that do that.

I’d be curious to hear what other people think–have you found uses for Bottlenose?

-Jodi
PS-They seem to be going by klout score for invites for now; if you can’t get in that way, give me a shout (I’ve 10 invites if you want one).
—-
I’m taking a listserv post as the source of a blog post again; channeling jrochkind I suppose.

Today I’m at the CSCW workshop on Collective Intelligence as Community Discourse and Action.

The day started with an introduction from Gregorio Convertino to the previous workshops.

Then Simon Buckingham Shum provided mutually overlapping categories for the workshop topics:

• Empirical studies
• New Tools
• Discourse analysis
• Sociality and social networks
• Reflection and argumentation
• Annotation
• Crowdsourcing Dynamics
• Civic Intelligence
• Organizational Intelligence

I’m sorry that I’ll miss the World Cafe this evening (must run off for the doctoral colloqiuum). The plan is for the group to split into four topics for discussion:

1. What do we already know about CI?
2. Why should we care?
3. What are the major obstacles?
4. Tell me a CI story from the future

Twitter hashtag for the workshop is #cscw2012ci

Factor-based summarization of reviews is useful:

I’m currently looking for a review of social media summarization. Any pointers?