Interview with Craig Newmark: How the Craigslist Meme Spread

One of the most ubiquitous and disruptive websites to emerge in the last 10 years is Craigslist. Impacting industries from real estate, news paper classifieds, careers and auctions the site has for the most part remained entirely free to use. A great example of organic, word-of-mouth spread I’ve always been interested in how the meme of Craigslist spread from city to city to become one of the most popular uses of the web.

I was lucky enough this week to get a chance to ask the site’s founder, Craig Newmark a few questions about exactly that. Here are his answers:

Dan: I think the social web is the great equalizer in terms of marketing. Non-profits don’t have to try to out-spend the big corporations anymore, they can simply out-think them and create contagious, well-intentioned ideas. If you were to give a non-profit just starting out one piece of advice on how they could “spread their meme” that you learned with Craigslist what would it be?

Craig: Anyone should seriously engage with their community about what they’re doing, including serious customer service. That means using email, Facebook, Twitter, any place where people in your community might hang out. Get feedback, and then, do something about it.

Dan: Did you do anything in the early days to help the site spread? Did you tell any “influential” people or send notes about it to any groups?

Craig: Never did any conscious networking, but I connected with lots of people via email and at industry events like launch parties. This was during the bubble years. I’m such a nerd, more so back then.

Dan: Do you remember any “tipping point” in the site’s history when the amount of people talking about it or using it seemed to take off? If yes, what do you attribute this to?

Craig: Never anything that I’d consider a tipping point. Our history is slow, continuous growth. In the race between tortoise and hare, well, we’re the slow guy.

Dan: In terms of the site’s initial spread, what do you think was most important the people who were using and talking about it, or the site’s features and content itself?

Craig: I think both equally important, that from the beginning we were clearly about people working with each other to help each other out. That’s somehow communicated directly between people, and from the look and feel of the site. There’s no fat on the site.

Dan: Of that most important element, what do you think was most key in the site’s early growth? (Ie What characteristic of its fans or what trait of the site?)

Craig: I think it had to do with the obviousness of the collaborative approach and the consistent culture of trust that grew. It has to do with the everyday practice of universal shared values like “treat people like you want to be treated” and “give the other person a break.” Now and then, we should be our brother’s keeper.

Dan: Do you have any knowledge into how newly added cities reach a “critical mass” of Craigslist usage? How do people in new cities find out about it? Does usage in a new city suddenly blow up or does it ramp up slowly?

Craig: No real knowledge, almost always a surprise. Might have to do with people moving from a CL city to a new one, where they spread the word. That’s the only guess I have from observing rapid growth city sites, like Las Vegas and Hawaii.

Dan: If you don’t mind sharing, what were the biggest sources of traffic in Criagslist’s early days? What are they now?

Craig: I think, then and now, jobs, housing, stuff for sale.

Dan: What are your three favorite contagious ideas spreading around the web right now?

Craig: The notion that we gotta help each other out to survive, and that social media is key to making that happen.

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Weekends and Afternoons Show the Highest Twitter CTRs

Want more clicks? My new data suggests that you should Tweet your links in afternoons, evenings and on weekends.

Continuing the study of Twitter clickthrough rates I started last week, I added over 100 more of the most followed Twitter accounts to my database and indexed click data on over 20,000 links Tweeted by those accounts. In all of the data below, I measured CTR as the number of clicks a link received, divided by the number of followers the sending account had on the day it Tweeted it. As I noted in my other post, this number can be over 100% due to ReTweets that may use the same link.

The graphs below shows the percentage of difference in CTR at each hour or day from the specific average for each account. I did it this way to account for the wide variation in CTRs between accounts (some accounts have much higher rates than others).

The first data point I analyzed is time of day (EST). It showed the expected afternoon/evening preference seen in my other Twitter stats.

Next I looked at days of the week, which showed a much less expected weekend preference. I believe this is due to the “link fatigue” present during the weekdays, where there is a much higher level of activity and many more links are posted.

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Modeling ReTweet Dynamics

Earlier this year I read a paper called “Modeling Blog Dynamics” in which they propose a method of modeling the spread of links through the blogosphere using zero-crossing random walks and exploitation vs. exploration applied to a logical flowchart model:

The authors suggested that the model could be used in influence maximization algorithms which aim to identify key, influential individuals in a given social network for the purposes of viral marketing. I was intrigued by the possibilities and have been tossing around a possible flowchart model of how individuals decide to ReTweet specific Tweets since reading that paper. Here’s my first attempt:

There are three steps in the process where a marketer can increase the chances of a specific Tweet being ReTweeted. The first step indicates that a user must be following the sender of the target Tweet; the second step means that they must actually see the Tweet in question (try to imagine what percentage of your friend’s timeline you actually see). Step three is where the user must find some motivation to ReTweet it.

Maximizing the number of followers the Tweet’s original sender has is fairly straightforward, and most of my Science of ReTweets data has explored the ReTweet motivation percentage. I had not put much effort into analyzing statistics around the attention problem, but I’ve begun to.

Because there is no way to exactly measure what percentage of followers will actually read a given Tweet, the next best metric we have is click through percentages, so that is what I’ve been working with. You can expect to see more work to that end in the next few weeks.

My work has been concentrated on maximizing the contagiousness of ideas, whereas much of the aforementioned academic work focuses on the people involved in spreading ideas. So you can also expect to see me advance the concepts of “ReTweetability” I began a few months ago with the purpose of identifying influential users.

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Want More Clicks? Tweet Less

Tweet Much? Don’t Expect a High CTR. New data I’ve been working on seems to indicate that the more frequently you Tweet links, the fewer clicks you’ll get.

I’ve been working towards a statistical model of how an individual makes a decision to ReTweet a specific Tweet and in that process, I came across an interesting problem: before someone ReTweets something, they have to notice it. If you’re anything like me, you’re only able to actually read a small percentage of the total activity in your friend’s timeline, which means that very few of the Tweets I’m technically “exposed” to ever even have the chance of being ReTweeted.

As a measure of “attention,” I started looking into click-through data. The wonderful thing about is that it has an API that allows anyone to view the stats on any link. I grabbed as many of the Tweets of several of the most followed and link-heavy Twitter accounts as the Twitter API allows (it imposes a limit of 3,200 total Tweets accessible per user) and the number of clicks each link had gotten. For the time of each Tweet, I also pulled the number of followers that account had and calculated a followers-to-clicks conversion rate. I’ll call this rate CTR for simplicity’s sake. I was able to get this information for about 2000 Tweets. It is important to note that ReTweets of a containing Tweet (if the ReTweeter does not change the link) also count toward the total number of clicks, so it is possible in some cases for a link to have a CTR of over 100%.

Digging into this data, I started to notice an interesting trend: the higher the number of links an account Tweets in a given timeframe, the lower the CTR on each individual link. If you want your Tweet to get noticed and ReTweeted, you should slow down your posting rate.

First, I looked at this data hourly, by graphing the CTR of Tweets over the number of other Tweets posted in the same hour. The first graph below shows individual lines for each account measured; the second graph shows an average for all those accounts.

Then I looked at the numbers by day. The CTR fall-off in these graphs seems to be slower than those above, but the trend is still prominent.

I’ve got a bunch more stats and analysis to run on this dataset to isolate some factors that lead to increased CTR, and therefore increased attention. I’d also love your feedback on data points you’d like to see.

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To #SaveReTweets, Make Sure Everyone Knows How to ReTweet

I wrote a little while ago about how Twitter’s plans to mangle ReTweets with its Project ReTweet, and the danger that poses to the crowd-invented functionality. After having several conversations on the topic and wondering what we could to do save ReTweets, I’ve come to the conclusion that the only thing to do is make sure that everyone knows how to ReTweet the original way. Then, once (or if) Twitter goes ahead with Project ReTweet, we can all continue to use the old format. If you like ReTweets, help save them by spreading this post around to ensure that everyone understands the commonly accepted method.

What is a ReTweet?

Normally, when you post a Tweet, only those people who are following you will see it. ReTweeting occurs when one of those followers copies your Tweet and posts it to their timeline. At that point, all of their followers will also see it. I’ve created an image below that explains this process.

How do I ReTweet?

The simplest way to ReTweet a post is to copy it from the original poster and paste it into the update box on your Twitter homepage. Here’s an example:

There are a few different ways people format ReTweets, but the most common way is this:

“@UserName” would be the username of the person who originally posted the Tweet you are ReTweeting and “original Tweet” is the text of that Tweet.

You can also add your own opinion of the content after the “original Tweet” text or before the “RT.” This is one of the most important things  the new Project ReTweet format is going to prevent.

Some people also use “h/t” (which stands for hat tip, from blogging) or “via.” Both of these standards are generally used when you are posting a link you found from someone else’s Tweet, but changing the text of the Tweet itself. RT is typically reserved for verbatim copies.

Many websites feature a little green and gray box (like the one at the top of this post) with a number and a button to “ReTweet.” If you’re reading something that you think your Twitter followers would like, just click the green button to share it with them. This isn’t a ReTweet in the sense described above, but the format is the same.

How do I ReTweet in a Third-Party Client?

You’ll also notice that down the right-hand-side of this post are screenshots from a variety of popular desktop and mobile Twitter applications. Each image shows you the app’s built-in one-click ReTweeting functionality. As you become a power user of Twitter, you’ll probably switch from using the web interface to one of these clients.

Want to Learn More?

If you’re interested in learning more about ReTweets, I’ve posted about ReTweet etiquette and the science behind how to get more ReTweets.

And remember: the only way to save ReTweets is to make sure everyone knows how to ReTweet.

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Does Social Media Accelerate the Spread of Dangerous Ideas?

Is the social web becoming a dangerous platform for contagious, destructive ideas? As social media usage grows and becomes a hive mind of collective consciousness, it enables a number of positive things to happen, but it also presents a grave danger in the form of dangerous memes.

Dan Dennet gave a great TED talk that I’ve mentioned before where he explores dangerous memes. He defines these as parasitic ideas that subordinate genetic interests, in that they can flourish and spread even when they cause harm to the people who contract them. Examples of these are “ideas to die for” like communism, capitalism, religion, fascism and contagious suicide.

Memes are ideas that act as viruses and spread from person to person. In biological infection extreme dense populations often form worst breeding grounds. Many of history’s deadliest outbreaks started in the extremely dense populations of Asia. Cholera started in Bengal and spread across India in the early eighteen hundreds. The black death is widely believed to have begun in central Asia and the third bubonic plague pandemic began in the Yunnan province of China in 1855

If memes are idea viruses, population density can be compared to technologies that bring minds closer together. Social media not only does this, but it also increases the reach available to a single infected person and the frequency of contact that other minds have with new ideas.

The old, industrial media regime had several buffering factors that hindered the spread of contagious ideas. The gatekeepers of broadcast media companies often did extensive fact checking on new stories. The speed and frequency of idea transmission under the old media was also much less than that presented by social media.

We’ve already begun to see the beginnings of dangerous meme outbreaks in social media. Many are relatively benign like the celebrity death hoaxes of stars like Tila Tequila, Britney Spears and Zach Braff. We’ve all seen examples of incorrect “facts” spreading across Twitter at lightening speed through ReTweets and stock prices have felt the pain of a rumor posted to a social site.

More sinister variations on this theme have also begun to emerge including a suspected “web-based Suicide Cults” in England and Japan, a “flash mob riot” in Philadelphia, online gang recruitment, and racist and neo-Nazi social networking. The giant Unification Church “cult” also has strong presences on Facebook and Youtube.

These phenomenon are likely only the tip of the iceberg. How long will it be before a dangerous cult, racist faction or mass-panic inducing hoax emerges that has been specifically designed for social media contagiousness?

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Introducing Dr. TweetDreams

We love talking and speculating about dreams, and Twitter is the perfect place to discuss those subconscious thoughts, images and emotions. Dr. TweetDreams takes those Twitter dream musings and tells you what they mean. analyzes the elements of your Tweeted dreams to see what they mean for your past, present and future. Type your Twitter username into the box below and hit analyze to see your dream analysis, or browse Dr. TweetDreams’ collection of recent dreams mentioned on Twitter.

The original idea came from the lovely Alison Driscoll and I built it with a bunch of my fun natural language and Tweet processing tools.

I parsed a 100 year old book by Gustavus Hindman Miller called “10,000 Dreams Interpreted” to create a lexicon of dream symbols and their interpretations.

I analyze every Tweet that contains the phrase “had a dream” by breaking it down into word-stems (through a Porter stemming algorithm) and finding related stems through a combination of a Brill part-of-speech tagger and Princeton WordNet Synsets. These stems are then matched against the symbol lexicon and the interpretation of the dream detailed in the Tweet is displayed.

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Twitter’s Deal With Search Engines? I Called That.

News broke yesterday that Twitter is talking to major search engines (Google and Microsoft) about licensing Twitter’s full firehose API. Over the past few months I’ve been seeing signs leading to exactly this kind of thing; here’s why Google will jump on this data.

When Twitter announced their intentions to completely re-engineer how ReTweets work, I took a strong stance against the move, mostly because it means that 3rd party researchers will no longer be able to index and analyze ReTweets in the same way we can today.

I speculated about the reason behind the move:

By taking out the “RT @username,” Twitter is making it impossible for users to search for retweets themselves, says Zarrella. “They’re limiting how much you can analyze retweets.” Zarrella speculates as to whether the retweet button might have been created so that, down the road, Twitter can charge for different features, such as extensive tracking of retweets.

And more specifically, in a tweet, I noted an interesting relationship between Project ReTweet’s lead, Zhanna and Google (her LinkedIn profile says she works for both Google and Twitter):

Does @zhanna work for Twitter or Google?

And over the summer at SES Toronto, I gave a presentation, which I’ll be giving again at PubCon Vegas, that detailed the reason and the way Google should be using the Twitter stream to aid in real time search:

This move was coming. Twitter knows they have a valuable data resource on their hands and they’re starting to reel in the 3rd party developers and researchers who’ve been using it for free. I’m just glad I’ve got my 60 million plus ReTweets already indexed.

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