Tag Archives: slides

Social Networking 4 Your Business talk

Few days ago, I gave a 4-hours talk in Bari for the initiative sponsored by Italian government and 4 universities “Imprenditori si diventa” (Entrepreneurs are made, not born). The presentation is embedded below.

It was a very interactive talk and I enjoyed it very much. I used for the first time VisibleTweets: students could write twitter messages with tag #isdsn and these tweets were automatically shown on another screen by VisibleTweets. Unfortunately not all students had a connection so it was less interactive than what I hoped but still very interesting [note for myself: VisibleTweets probably works better if the talk is given by at least two people because it is hard to read twits and talk, and the audience (as expected) challenges you and tries to “steal” the attention from you (to their witty twits)]. I also showed many videos (see the slides): from CommonCraft, from the movies Ratatouille and The pursuit of Happyness, some from Socialnomics.com and one by Corrado Guzzanti, an Italian comedian. It is incredible the power of movies in waking up your audience! ;)
The talk was full of real examples such as successes and failures in using Twitter, Facebook and other social media, both in the Italian context and worldwide (I didn’t avoid talking a bit about Wikipedia when exploring concepts such as wikinomics and crowdsourcing of course!)
There were some interesting projects by will-be entrepreneurs and I wish them all the best, for their future and the future of Italy.
Well, if you are interested in the slides, you can get them on Slideshare.

Tidbits from Wikipedia presentation at Wikysym by Andrew Lih “What Hath Wikipedia Wrought: Crowds Remaking the News”

The presentation (embedded below) consists of 148 slides. Below I selected few interesting ones.

Slide 42
• Wikitravel: only 5% of those who press “edit” actually save
• Wikipedia: 1/5 to 2/5
• WikiHow: 30% with guided editing
• Wikia: WYSIWYG editor >> 50%
Sources: Jack Herrick, WikiHow; Erik Zachte, Wikimedia Foundation

Slide 91:
An experiment by The Guardian on crowdsourcing journalism.
The Guardian obtained two million pages of explosive documents that outed your country’s biggest political scandal of the decade. They’ve had a team of professional journalists on the job for a month, slamming out a string of blockbuster stories as they find them in their huge stack of secrets.
How do you catch up? If you’re the Guardian of London, you wait for the associated public-records dump, shovel it all on your Web site next to a simple feedback interface and enlist more than 20,000 volunteers to help you find the needles in the haystack.
Your cost for the operation? One full week from a software developer, a few days’ help from others in his department, and £50 to rent temporary servers.

Review of “Feedback Effects between Similarity and Social Influence in Online Communities”

Today I presented to the other SoNetters a wonderful paper titled “Feedback Effects between Similarity and Social Influence in Online Communities” by David Crandall, Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, Siddharth Suri of Cornell University, presented at the 2008 KDD conference on Knowledge discovery and data mining. My review just under the slides I used for the presentation.

Besides the points already presented in the slides, here I add few points relevant for our research on Wikipedia.

Social influence: People become similar to those they interact with
Interaction ? similarity
Selection: People seek out similar people to interact with
Similarity ? interaction

They considered registered users to the English Wikipedia who have a user discussion page (~510,000 users as of April 2, 2007). They are responsible for 61% of edits to the roughly 3.4 million articles. They ignore actions by users without discussion pages, who tend to have very few social connections.

User’s activity vector v(t): number of times that he or she has edited each article up to that point in time t.
Similarity(u,v): similarity between activity vectors of user u and v.
Time of ?rst meeting for two users u and v = time at which one of them ?rst makes a post on the user discussion page of the other.

In principle, we could also try to infer social interactions based on posting to the interactions based on posting to the same article’s discussion page. Moreover, we found that using simple heuristics to infer interaction based on posts to article discussion pages produced closely analogous results to what we obtain from analyzing user discussion pages.

They ?nd that there is a sharp increase in the similarity between two editors just before they ?rst interact (selection), with a continuing but slower increase that persists long after this ?rst interaction (social influence).

They also create a model and estimate the unobservable parameters based on maximum-likelihood. The estimates are as follows:
* The parameter ?, the probability of communicating versus editing, was 0.058 (i.e. every 100 actions, 6 are talks while 94 are page edits). We can cite it and we can even verify this across different wikipedias and at different time slots.
* When considering article edits as actions, the article is chosen from one’s own interests with probability ? = 0.35, from a neighbor’s interests with probability ? = 0.081, from the overall interests of Wikipedia editors with probability ? = 0.5, and by creating a totally new article with probability ? = 0.069.
* When considering talks as actions, the user to communicate with is chosen randomly from the overall set of users with probability ? = 0.71, and someone who has engaged in a common activity with probability 1-? = 0.29

They also do some content analysis (30 instances of two users meeting for the ?rst time. We examined the content of the initial communication and any reply, looking for references to speci?c articles or other artifacts in Wikipedia. We also compared the edit history of the two users).
Of the 30 messages, 26 referenced a speci?c article, image, or topic. In 21 cases, the users had both recently worked on the artifact that was the subject of conversation.
The gap between co-activity and communication was usually short, often less than a day, though it stretched back three months in one case.
Informally, communications tended to fall into a few broad categories: o?ering thanks and praise, making requests for help, or trying to understand the editing.behavior of the other person.
This sample of interactions suggests that people most often come to talk to each other in Wikipedia when they become aware of the other person through recent shared activity around an artifact. Awareness then leads to communication, and often coordination.

A really wonderful paper!

Designing Your Reputation System and Designing Social Interfaces

10 practical questions for designing a reputation system. This talk was (partially!) given at the 2008 IA Summit. By Bryce Glass on Slideshare

Designing Social Interfaces – workshop talk given at Web 2.0 Expo

Video and slides of sci.bzaar.net merged with Omnisio

Thanks to David Orban I discovered Omnisio and so I took a chance to merge my slides with the video Gianandrea recorded during my sci(bzaar)net presentation.
Using Omnisio is very easy, you just provide the URL of a video online and the URL of slides on slideshare.net and then you can optionally synchronize slides with video by drag and drop.
You can see my video/slides on omnisio or embedded here below. Slides are in English but I spoke in Italian.
</p> <div><a href='http://www.omnisio.com'>Share and annotate your videos</a> with Omnisio!</div> <p>