Note: This is a repost of Episode 7 do to an issue with our podcast host.
We kick off talking about the #fuckyouwashington hash tag that Jeff Jarvis started.
Does the profanity bother us? No. Does Twitter censoring the hashtag bother us? Yes.
We also delve deeper into Lion after using it for a week. We discuss AirDrop, the feature seemingly few are talking about. We go deeper into Lion’s new security features.
We also discuss how Patrick may get a new computer setup, and we spend a long time debating Macbook Airs vs. Macbook Pros.
And we had several other topics that we wanted to touch one, but this podcast aims to dig deep into topics. So that’s all we got to.
We’d also like to thank Alan Smodic for joining us during our Google+ Hangout before the show. Each week before the show we have a public Google+ Hangout where we go over what we’ll talk about on the show and try to get some early thoughts out. We’ll tweet out the link and share it on our Google+ accounts.
Listen to this week’s podcast:
China’s versions of Twitter, called weibos, were able to get much of the truth about the train crash there that killed 36 people and injured many more out and past government censors.
Many people in China, despite censored and a state-controlled media, were not fooled by the Chinese governments official accounts that the weather and some other gobbledygook caused the two trains to collide. What makes services like Twitter so interesting for democracy and the freedom of information is that new messages come in so fast that they are really hard to censor:
“I call it the microblogging revolution,” Zhan Jiang, a professor of international journalism and communications at Beijing Foreign Studies University, said in an interview on Thursday. “In the last year, microbloggers, especially Sina and Tencent, have played more and more a major role in coverage, especially breaking news.”
Just look at these messages that got by government censors:
Then the reaction began to pour in. “Such a major accident, how could it be attributed to weather and technical reasons?” blogged Cai Qi, a senior Zhejiang Province official. “Who should take the responsibility? The railway department should think hard in this time of pain and learn a good lesson from this.”
From a Hubei Province blogger: “I just watched the news on the train crash in Wenzhou, but I feel like I still don’t even know what happened. Nothing is reliable anymore. I feel like I can’t even believe the weather forecast. Is there anything that we can still trust?”
Twitter is like high school. The equivalent of being the popular captain of the football team or head cheerleader is to have a massive number of followers, but to be following very few. Some of the mega-celebrities, for example, have more than a million followers, but themselves are following like 12 people.
Google+, however, which was built by math and computer nerds, turns that scenario upside down. If you think about it: Your potential reach on G+ is affected more by who you follow than by who’s following you. Let me explain.
I can theoretically reach 25 million people, even if nobody is following me <sniff!>. If nobody is following me on Twitter, my tweets will reach zero people.
This only applies to those who check their incoming stream, but it’s an interesting take nonetheless. Twitter has been overrun, in some ways, by celebrities who do little interacting and little following. Google+ is trying to get away from that broadcast model.
Fascinating stuff, all revealed via your tweets:
The dataset was about 55% female, 45% male (which squares roughly with estimates of Twitter’s overall gender breakdown). Thus, by guessing “female” for every user, a computer would be right 55% of the time. Simply by examining the full name of the user, a computer was accurate about 89% of the time–a remarkable improvement, if not an especially interesting one, since first names are highly predictive of gender. The Mitre findings become intriguing, though, when the team limited its analysis to tweets alone. By scanning for patterns in all the tweets of a given user, Mitre’s program was able to guess the correct gender 75.8% of the time–a 20% improvement over the baseline. And even just by analyzing a single tweet of a user, it was right 65.9% of the time–an over 10% improvement over the baseline.