Working on Content Sharing Survey Results

My survey on web content sharing collected around 450 responses and now I’m working on the task of decoding the type-in answers and calculating the results.

I asked the question on twitter, but I’d also like to ask it here, what formats would everyone like to see the results in? I’ve already had suggestions of: spreadsheet and slideshare in addition to the normal graphs-in-a-blog-post format. Any other awesome ideas?

Update: Its also been suggested that I do a downloadable PDF version of the report, like I did with the Link Attraction Factors report.

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How to Make and Spread Rumors

If you like this post, follow me on Twitter or take my survey about online content sharing.

In 1940, the British military formed an organization as a part of the Special Operations Executive, or SOE, called the “Underground Propaganda Committee” or UPC whose mission was to create and disseminate rumors as defensive weapons against the expected Nazi invasion of the the English mainland. They code-named the rumor weapons “sibs,” short for siblare, latin word “to hiss.” During the war they developed the craft and science of designing rumors and developed international networks of agents to spread the sibs. ( has a great history of the UPC.)

During World War II the Americans, under the Office of Strategic Services (OSS), which eventually became the CIA, began cultivating their own rumor-weapon technologies with the help of the UPC and scientist Robert Knapp, who also wrote about rumors in an academic context. Knapp’s work was adapted by the OSS in 1943 to create a sort of manual for rumor engineers during the war. This document was de-classified in 2004.

Criteria for a Successful Rumor

  • » The successful rumor is easy to remember.
  • » The successful rumor follows a stereotyped plot.
  • » The successful rumor is a function of the momentary interests and circumstances of the group.
  • » The successful rumor exploits the emotions and sentiments of the group.

The manual says rumors are made memorable by being simple, making concrete references, using stereotypical phrases and utilizing humor, many of the same characteristics possessed by the Homeric poems and the rest of the oral tradition.

A stereotyped plot, according to the manual is “the oldest story in the newest clothes.” Old structure, typically derived from local myths, stories or legends, filled in with new content makes the best rumors.

Academic work on rumors suggests that they are “collective sense making,” they are a society’s attempt to understand something that is happening where official or formal information is scarce or non-existent. The OSS paper says that good rumors are “provoked by” and provide interpretation or elaboration on a current event, filling a “knowledge gap.” If the locals heard a big boom earlier in the day, a rumor could easily be constructed to explain it if the authorities did not.

If a group is known to have a pre-disposition to mistrusting a certain other group, like Diggers disliking Microsoft, rumors about the evils of Microsoft are easy to spread. The manual says that successful rumors justify or articulate an emotion (such as hatred, fear or desire) widely held by the target population.

Targeting Rumors
The OSS/Knapp report sketches those people or “targets” that make good vectors for rumor spreading and what sorts of rumors will spread most virally amongst them.

1. Those people who are most eager for information about events which affect them are the best targets for rumors supplying such information.

2. People with fears, hopes and hostilities stemming from their involvement in the war are most affected by rumors that feed on those feelings.

The report also detailed certain kinds of information that should be gathered prior to designing a rumor for a specific group, including:

  • » What kinds of information the group is eager for.
  • » What information the group already has and what it lacks.
  • » The current fears, hopes and hostilities the group already has.
  • » The customary and traditional ways the group deals with those fears, hopes and hostilities.

Obviously, the information about the knowledge and emotions of the group should be gather in respect to what the rumor will be about. To use the Digger example again, if you wish to start a rumor about Microsoft, you should find out what Diggers want to know about Microsoft, what they already know and what they don’t know, how they feel about the group, and the traditional ways they express themselves about Microsoft.

Spreading Rumors

The British UPC developed specially designed networks around the world through which they could seed rumors for maximum effectiveness. Each individual rumor was not seeded into every active network, as that would have appeared too obvious; rather, the networks were chosen for their appropriateness to the specific sib.


SOE’s whispering network in Turkey was a typical example of how the machinery for spreading rumours worked. A Chief Whisperer was appointed who then recruited ten Sub-whisperers, each of whom was chosen because they had specially good contact with certain classes of people from politicians and Army officers to waiters and barbers, for example. Each Sub-whisperer was conscious of the fact that he, or she, was working for SOE, but although they knew the Chief Whisperer, they did not know the identities of any of the other Sub-whisperers. Each Sub-whisperer then recruited ten to twenty unconscious agents to whom they passed on rumours.

The OSS manual also gave a little detail into how rumors should be spread:

  • » Design different rumors that reveal the same “information.”
  • » Plant such rumors in different suitable places.
  • » Design them so as to appear as of independent origin.

The OSS and the UPC both used a tactic where several rumors were constructed and seeded in such a way that they appeared to come from different sources and took different “routes” to expose the same information to the targets. This way when a person heard more than one source tell complimentary rumors, they were more likely to believe them.

Have you ever tried to start a rumor? I’d love to hear some stories.

If you liked this post, follow me on Twitter or take my survey about online content sharing.

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Why People Forward Chain Letters

Chris Garrett asked a question on twitter this morning:

Anyone know why people forward chain letters?

And since I’ve been doing some research on exactly that question recently, I thought I’d write a post detailing some of what I’ve found.

Probably the most important point is the idea that viral email chain letters are “virtual urban legends” and as such many of the motivations that cause people to spread urban legends are the same that make people forward those emails.

Many urban legends function as warnings, if you break with social rules and roles you will be punished. Go to lover’s lane with your boyfriend and a crazy serial killer will come and kill you or him (or both of you). Disregard your parents’ dislike of unhealthy fast food and you may end up eating a rat.

As I mentioned briefly in a post last week humans evolved with strong rewards to develop imitation and social learning skills. Evolutionarily, people have a motivation to be susceptible to social warnings and pass them on to their family and community.

In 1998 Edmund Chattoe published a paper for IRISS titled “Virtual Urban Legends: Investigating the Ecology of the World Wide Web” in it he studies traditional chain letters and virus warnings, and in it he cites an earlier paper by Woolgar and Russell (called “The Social Basis of Computer Viruses” I can’t find it on the web) when he says

users are rather inclined to believe in computer viruses as just ‘punishment’ for electronic promiscuity

Here the urban legend similarity is clear, if you disregard careful computer-based chastity and network with unsavory types (like downloading pirated music or programs) you’ll be punished with a computer virus.

Before email existed, chain letters propagated via snail mail and while they were still very virulent then, with the advent of the internet several factors changed which accelerated their spread. Obviously the speed of transmission and the ease at which someone can create and pass on a viral email is a lot greater than for traditional paper mail, and the anonymity of email reduces the risk to those who craft and continue the chains online.

Two of the key criterias of a successful meme are copying fidelity and fecundity, that is the less a message changes each time it is transmitted the greater the chance it has to retain the effective parts and continue spreading, and the more people it can be transmitted to the more successful it will be. Viral emails are copied verbatim and all the sender has to do is click “forward” and it an exact copy is sent to all of their friends.

In the paper mail chain letter world, most people had become immune to the over-the-top promises and demands so their effectiveness started to drop. With the ease of spread and the high copying fielding available to mental email viruses, they’ve been able to evolve into more subtle and unfalsifiable variations. The chances of a person passing the email on is great online, so the messages themselves need to require less instructions about their own replication, making them less obvious and more trustworthy. Chattoe says:

More generally, chain letters have increasingly stressed intangibles like ‘good luck’ rather than more concrete rewards. They thus render themselves immune from obvious falsification and tap into a human tendency to ‘superstition’, spotting patterns where none exist. If I fail to pass on a chain letter and shortly afterwards something bad happens, I may connect the two events and be more susceptible in future.

And while many (if not most) of these emails are debunked on sites like snopes, preliminary data from my survey shows that those who typically forward chain letters are typically less savvy users and may not know about snopes.

Perhaps the most subtle and powerful viral element of chain letters in email is the social proof that comes with many of them. Every time someone forwards one to his or her address book, another list of recipients and senders is attached to it, creating essentially a list of people who implicitly give authority to the message. If one person sends an email to another, the source may or may not be cited, and the sender’s reputation is the only real social authority the email carries, with a huge list of hundreds of others attached it, with popular viral emails, it suddenly appears that the message is common knowledge and the receiver is perhaps the only person left on the internet who wasn’t warned of the danger.

The social proof factor goes back to information cascades and the understanding that humans base many of their decisions on the choices of others, it just makes good evolutionary and survival sense to do so. Even if you may think an email is a hoax, who are you to think that you know better than hundreds of your peers? And on the off-chance the email was true, and you didn’t pass it on you didn’t do your best to protect and enrich your friends and family.

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Survey on Web Content Sharing and Online Meme Transmission

I’ve had this idea rolling around in my head for a little while now, and I just got around to doing it.

I’m conducting a survey on what types of content people share online, where and how they share it, who they share it with, and the all important question, why they share it.

Click here to take the survey

Please spread this as widely as possible, I’ll publish the results as soon as I get a decent number of responses.

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Informational Cascades Prove Tipping Points Exist

Mediapost reported recently on the results of a survey done by research firm Pollara, and Marshall Kirkpatrick over at ReadWriteWeb commented on it in a post titled “There is No Tipping Point”.

A number of thinkers… and now the Pollara study have been arguing that large numbers of people do not make decisions based on the advice of a small number of powerful influencers…

The marketing world was really hoping they could just win over some existing social media power users and have everyone else fall like dominoes. This study says that’s not how it works.

The study says that while 80% of respondents were “very or somewhat more likely to consider buying products recommended by real-world friends and family” only 23% were similarly influenced by “well-known bloggers”. I do not disagree with this data, what I disagree with is the understanding of the mechanisms of social influence. A concept from economics called “Informational Cascades” shows us why the domino-effect/tipping point idea is valid, even in light of this study.

To understand this mechanism an example is helpful. Suppose there are two restaurants and a group of people on the street outside deciding which one to eat at. The most well-informed individuals (those with higher precision in making these types of decisions) will decide first and everyone will see some people start to line up outside of one restaurant. If the others know this person is of higher precision (and even if they don’t) a few people will follow their lead and join the line. Each new person who lines up outside of the restaurant sends a signal to the rest of the group (and in particular their friends and family) that this is the restaurant to pick. The more people who follow the signal, the stronger it gets and you have an Informational Cascade.

The idea is presented in rigorous detail in a paper called “Theory of Fads, Fashion, Custom and Cultural Change as Informational Cascades” written by Sushil Bikhchandani, David Hirshleifer and Ivo Welch and published in 1992. To paraphrase:

An informational cascade occurs when it is optimal for an individual, having observed the actions of those ahead of him to follow the behavior of the preceding individual without regard to his own information…

The prediction that a low-precision individual imitates a higher-precision predecessor is consistent with the evidence of numerous psychological experiments demonstrating that a subject’s previous failure in a task raises the probability that in further trials he will imitate a model performing the task (see Thelen, Dollinger, and Kirkland 1979, p. 146)…

an individual who wishes to bring about a social change, for example, introduce a desirable innovation such as an improved sanitary method in a peasant community, must focus his efforts on persuading early community leaders.

This phenomenon will happen even if the early deciders are not experts (although it is certainly enhanced if they are), or influential people normally, by the act of making a choice before others, they become influential to the rest of the group.

Real-world examples of Informational Cascades can be found everywhere, from the stock market and voting patterns to the fashion industry, but we may ask the question, why do humans put so much stock into choices made by others before them? For an answer to this we look to evolution.

For ancient (and modern) humans, learning by trial and error is energy and time expensive (not to mention dangerous), it is safer and more efficient to let someone else figure out what plants are safe to eat and then just imitate them. A 1980 paper by John Conlisk titled “Costly Optimizers and Cheap Imitators” showed that “imitators may have as high a long-run ‘fitness’ as optimizers“, and in 1992 Peter J. Richerson and Robert Boyd published a report called “Cultural Inheritance and Evolutionary Ecology” where they showed that in many instances social learning (like imitating your peers) is preferred by natural selection.

If we can agree that the choices and actions of others influence our own decisions then the mathematics of Information Cascades prove that as the size of the participating population increases, “Tipping Points” become inevitable.

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