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.
So the question could be: what is the ratio male/female on other social networking sites?
Just, for comparative reasons (and a bit for fun too), I compiled the following table based on the Social Network Analysis Report by Ignite Social Media. The table is sorted so that first lines are sites in which there are relatively more females than males. I’m not familiar with all the sites but it seems that sites more populated by women are the very social and playful (such as Haboo, Bebo, Myspace, Xanga, Facebook). On the other side of the spectrum there are sites populated most by males: sites showing what’s interesting right now thanks to social bookmarking such as Reddit, Digg, Identi.ca, and “professional” network sites such as Linkedin and Plaxo.
This table is not “scientific” in any way as well (for instance, percentages in the report are gathered from Google Ad Planner and Google Insights for Search).
Consider the following table just as more food for thought. Does it confirm your intuitions? Or should I say prejudices? ;)
The first day of Sunbelt is finished: it was very hot … meaning there were some problems with conditioning air not working ;)
I met some cool people: in particular
(1) Mathieu Bastian of Gephi, great open source program for visualization of networks,
(2) Jure Leskovec of Stanford, hands-down best talk up to now, who spoke about “Predicting Positive and Negative Links in Online Social Networks”, work on Wikipedia, Slashdot and Epinions signed social networks (they even cited me in the paper and used the Epinions trust network I made available time ago on Trustlet.org!),
(3) Filippo Menczer of Indiana University, whose great Scholarometer widget I recently embedded on my blog and who is doing many different great works.
Some people are using the hashtag #sunbelt on Twitter, you might enjoy posts tagged as #sunbelt as made visible by visibletweets (iframed below)
Last point, I’m at Sunbelt with my colleagues in the SoNet group, Michela Ferron and Asta Zelenkauskaite. Tomorrow we will present two recent works: one about
social networks in Wikipedia, the other about social capital and enterprise2.0 platform usage.
While you are flying Lufthansa, Lufthansa automatically sends status updates to your social networks (twitter, facebook, …) and show all the updates on a globe. It’s travel made social. Check MySkyStatus.com
During our weekly SoNet internal research meeting, my colleague Napo presented the paper “Predicting the Future With Social Media” by Sitaram Asur and Bernardo A. Huberman, archived on arXiv in March 2010. Using Twitter posts, they are able to forecast box-office revenues for movies, outperforming market-based predictors. They also do sentimental analysis on Twits by asking Mechanical Turk to tag few twits as positive, neutral, negative and then they train LingPipe to predict the positiveness of all the other millions of twits. Read it! Very interesting paper!
Every public tweet, ever, since Twitter’s inception in March 2006, will be archived digitally at the Library of Congress, the largest library in the world.
I’m still totally puzzled by how a so simple service (basically you can post 140 chars of text and nothing more) got so widely used! A typical Matthew effect (the rich gets richer)!
See more on official Library of Congress blog post “How Tweet It Is!: Library Acquires Entire Twitter Archive”.
Attending the great conference Le reti socievoli (sociable nets) at Larica group of Univ Urbino.
Behind the speakers, the beamer shows live tag clouds of twitter posts (hashtag: #retisocievoli) by visibletweets (embedded below). Good example of audience live-participating to a conference!
I’ll make my first try to livetwitter a conf. Follow me at http://twitter.com/phauly.
Social psychology research has found that both men and women are more likely to hire a male applicant than a female applicant with an identical record (Steinpres et al., 1999).
Deaux & Emswiller (1974) found that success is more frequently attributed to “skill” for males and “luck” for females, even when the evaluators are presented with evidence of equal success for both genders.
Beginning in the 1970s symphony orchestras started requiring musicians to audition behind screens; since that time, the number of women hired has increased fivefold and the probability that a woman will advance from preliminary rounds has increased by 50% (Goldin and Rouse, 2000).
Although men and women follow a similar number of Twitter users, men have 15% more followers than women. Men also have more reciprocated relationships, in which two users follow each other. This “follower split” suggests that women are driven less by followers than men, or have more stringent thresholds for reciprocating relationships.
This is intriguing, especially given that females hold a slight majority on Twitter: we found that men comprise 45% of Twitter users, while women represent 55%. To get this figure, we cross-referenced users’ “real names” against a database of 40,000 strongly gendered names.
Even more interesting is who follows whom. We found that an average man is almost twice more likely to follow another man than a woman. Similarly, an average woman is 25% more likely to follow a man than a woman.
Finally, an average man is 40% more likely to be followed by another man than by a woman. These results cannot be explained by different tweeting activity – both men and women tweet at the same rate.
These results are stunning given what previous research has found in the context of online social networks. On a typical online social network, most of the activity is focused around women – men follow content produced by women they do and do not know, and women follow content produced by women they know.
I presentation I gave on June 10th 2009 at Trentino Sviluppo, local agency in charge of developing local businesses. It is about the how and why (and why not) of using social networking systems such as Facebook or Twitter for small businesses. The slides are released under Creative Commons By-Attribution so share them, play with them, tear them apart! The only exception are the two photos below for which I don’t know who the copyright holder is. If you know please get in contact with me. Thanks!