The Net-Savvy Executive

It's Friday, and I've been writing long posts lately, so here's a simple idea: A metaphor is a mixed drink. In business, we use a lot of metaphors, some better than others.

Some are easy for beginners. They're simple and sweet, and they lose their appeal over time.

Some are difficult at first, but surprisingly good when you figure them out.

Some are old, traditional, and still on point. The classics.

Some are just outdated.

Some pack a lot of ingredients into a simple effect. All that work for so little result.

Some are gimmicky and less clever than they think. Sparkly!

Some appear simple but are capable of important subtleties.

Some look better than they are.

Some think they're metaphors but are actually similes.

The next time you're stuck in a meeting and the metaphors start to fly, you can amuse yourself by figuring out which drink a metaphor would be. It's more stealthy than shouting "bingo" after one too many clichés.

Happy weekend.

Photo by Kurman Communications, Inc.

It's bad enough when people are wrong as they express facts and opinions on the Internet. Mistakes happen. But there's more going on. Some people are intentionally adding noise to the online world, in an attempt to mislead users and analysts. Garbage in, garbage out, so how do we catch the garbage before it becomes part of the analysis?

This post is the second in a series. The first is Can You Trust Social Media Sources? Most of my posts aren't this long; the next will be nice and short.

Catching and deleting spam and other garbage in social media data is one side of an arms race, just like email spam and computer viruses. Developers of social media analysis platforms work to eliminate spam from their results, and spammers develop new tactics to dodge the filters. As long as the incentives remain, people will find ways to game the system.

For most analysts, the main response is to pick a platform that does a decent job of catching the undesirable content. Most do some sort of machine learning to identify and filter spam, and while the results are imperfect, they're useful as a first step. The second step is to allow users to flag content as spam, and it's good if the system learns from that action. A third step is to allow users to blacklist a site altogether; once you know it's not what you're looking for, there's no need to rely on the spam-scoring engine.

Evaluating questionable data
This is where I'd love to give you the magic button that reveals deceptive content. I'd like to have the Liar Liar power, too, but that's not going to happen. Instead, I have some ideas of how to think about questionable results. Most of them are in the form of questions. Some are more probabilistic than definitive, but I think they could be helpful.

• Consider your purpose
  Your sensitivity to garbage in your data depends on what you're doing with it. If you're monitoring for customer service purposes, flag the spam and move on. If you're reporting on broad trends, you might get better results through sampling, or by focusing on high-quality sources. If you're looking for weak signals, you may not have the luxury of ignoring the low signal-to-noise ratio of a wide search. As always, match the effort to the objective.

  Some people actually need to look at spam—consider the legal department. If a link leads to a site selling counterfeit merchandise and you're in a trademark protection role, the spam is what you're looking for.

• Consider the source (person)
  Who posted the item in question, and what do you know about them? Is the poster a known person? What do you know from the individual profile? Who does the person work for? What groups is the person connected to? Does the person typically discuss the current topic? Is the person's location consistent with the information shared?

  If you're not sure whether the poster is a person or a persona, develop a profile. A persona is like a cover identity; it can be strong or weak. Does the persona have a presence on multiple networks? Since when? Is it consistent across networks? Does it have depth, or is every post on the same topic? Who does the persona associate with online, and what do you know about them? Do the persona's connections reveal the complexity of relationship types that real people develop (school, work, family, etc.)? Do the profiles and connections give information about background that can be checked?

  For questionable sources, think about the different types of data that might reveal something through social network analysis.

  Back at the Social Media Analytics Summit, Tom Reamy described work by researchers to identify the political leanings of writers, based on their language choices (writing about non-political topics). Can we use text analytics to add information about native language, regional differences, and subject-matter expertise to individual profiles?

• Consider the source (site)
  Where was the data posted? What do you know about the site? Is it a known or probable pay-to-play or disinformation site? Is it a content-scraping site? Does it have information from a single contributor (such as a blog) or from many (such as a crowdsourcing site)? What else is posted to the site? Where is it hosted? Who owns it? Where are they based? What can you learn from the domain registration?

  What's the online footprint of the site? Is it linked to real people in social networks? Is it used as a source by other people? Credibility flows through networks; do known, credible (not necessarily influential) people link to it and share its content in their networks? Does it appear to have bought its followers, or are they real people?

• Consider other sources
  If you're going to do something serious—and I'll leave the definition of serious as an exercise for the reader—don't trap yourself in a new silo for social media data. What else do you know? What do other online sources say? Does the questionable data fit with what you're getting from sources outside of social media? Are you getting similar information from credible sources, or are all of the sources for the questionable data unknown?

  A few months ago, I heard Craig Fugate, the Administrator of the (US) Federal Emergency Management Agency (FEMA), tell a story about government agencies and unofficial sources of information. The story involved a suspected tornado and unconfirmed damage reports in social media. Government agencies prefer official reports from first responders and other trained observers, so the question was how to evaluate reports in social media.

  In the case of severe weather, one answer is to compare the reports with official sources of weather data. If radar indicated a likely tornado passing over a location a few minutes before the damage reports, then you'd know something important that should help evaluate those reports. What's the analogy for your task? Is there a hard-data source that can add relevant information? Does a geospatial view add a useful dimension (such as radar, post location, and photo metadata all in same location would, in the example)?

• Consider the incentives
  What does a potential adversary stand to gain by fooling you—or someone else looking at the same data—with false information? Who gains by leading you to an incorrect action? Who makes money on your decision? Who benefits from misleading other people with false information (think product reviews and propaganda)? Is questionable information in your system consistent with the aims of an interested party?

  Part of the challenge here is that false information could be intended to mislead anyone. The target could be an individual, a small group, or entire populations. Who gains? Is there a link from the source to an interested party?

• Consider the costs
  Part of what makes spam so frustrating is the volume level—there's a lot of the stuff around. At some point, the signal-to-noise ratio gets so low that the source becomes useless, unless you can identify and eliminate the junk. In a way, all that junk adds up to a sort of denial-of-service attack at the content layer. Is there a way to deal with that?

  A denial-of-service (DOS) attack and its scaled-up variant, the distributed denial-of-service (DDOS) attack, overload the targeted web site with simultaneous requests, causing it to become unavailable to real visitors. In 2010, Amazon weathered a DDOS attack without losing service. The explanation was that their normal operation looks a lot like a DDOS attack—lots of people visiting the site simultaneously. Their system was built to handle that kind of load, so the attack failed. One answer to a DDOS attack, then, is to have the capacity to handle the load.

  The social media analysis equivalent is to process it all, so what would that look like? Would a deeper analysis of known junk and its sources help improve the identification of junk? Would it tell you something useful about the parties that post the junk?

• Consider the consequences
  The final point is to revisit the first point. What are you trying to accomplish? What decision will you make based on the data, and what happens if the information was false? What if it was placed there to manipulate your response (even if the information itself is true)? Does the rest of the decision-making process have the safeguards to prevent costly errors?
  

The hard problem
One way to look at this is to go through the whole process while thinking "spam." Junk results are an annoyance if you're doing day-to-day monitoring for business, and they're a problem if you're doing quantitative analysis. The technology is improving, and you have options for dealing with spam in these settings.

Some junk isn't that hard to catch, especially once a person looks at it. Gibberish blog comments are easy to identify. Names and email that don't match are sort of obvious, too. Content scrapers and other low-quality sites tend to have a certain look. If you have time to look at the spam that evades your filters, you can catch a lot of it.

The real challenge comes in looking for intelligence—whether in business, finance, politics, or government—in the presence of a motivated and well-funded adversary. If someone wants to fool you—or at least keep you from using an online source—they can improve their chances by better imitating the good data surrounding their junk. The quick glance to identify spam becomes a bigger effort, with more uncertainty.

Pay-to-play blogs may have original content from professional writers, so you can't just look for poor quality. False personas may be developed over time, with extensive material to create a convincing backstory. Networks of such personas could post disinformation, along with more normal-looking content, across multiple sites. With time and resources, personas can appear solid, which is why governments are investing in them.

I think some of the techniques above could help, but it's really a new arms race. The problem for everyone else is that this arms race will tend to poison the social media well for everyone who wants to discuss the contested topics.

If your organization is interested in these topics, don't just read the blog. Call me. As long as this post is, it's the short version. Clients get the full story.

XKCD cartoon by Randall Munroe.

Before you can pull insights from your data, you need data, but I'm hearing more concerns about data quality in social media analysis lately. Before, people asked about the traditional tradeoff in text queries: finding relevant content while excluding off-topic content. Lately, I'm hearing more about social data that's intentionally tainted. If you're looking for meaning in social media data, you may have to deal with adversaries.

Yes, and you've been playing without an opponent, which is, as you may have guessed, against the rules.
— "Anton Ego," Ratatouille

Ask a company with three initials as a name how many three-letter abbreviations are in use, and you get a sense of the challenge in finding relevant content. Common words as brand names pose a similar challenge (I always like the examples of Apple and Orange, because it's the one time you really can compare them). If people are honest and expressing their real opinions, it's hard enough to find what you're looking for.

The problem is, people aren't always honest. You also need to get rid of intentional noise in the data.

The analyst's adversaries

• Spam
  We've all seen online spam (sorry, Hormel, you must hate that term). Junk mail for hormones and drugs in email, junk comments on blogs, junk blogs, trashy web sites—the costs are so low that even microscopic conversion rates are profitable, so it persists. Some of that shows up in social media, which is the problem here.

  At the recent Social Media Analytics Summit, Dana Jacob gave a talk on the spam that finds its way into the search results of social media analysis platforms, skewing the numbers. One tidbit that Dana shared to illustrate the challenge: If you consider all of the creative misspellings, there are 600 quintillion (6 x 10²⁰) ways to spell Viagra. So removing all of the spam from your data is a challenge.

  Spam seems to come in two flavors, neither of which will help you understand public opinion or online coverage. One is designed to fool people, to get them to click a link. It may lead to malware or fraud, or to some sort of product for sale. The other is designed to fool search engines with keywords and links embedded in usually irrelevant text. It's usually obvious to a human reader, but the hope seems to be that some search engines will count the links in their ranking of the target site.

• Gaming analytics platforms
  Another presenter outlined a more direct challenge to the social media analyst when he described his system to game analytics systems with content farms and SEO tactics. He talked about using weaknesses in analytics systems to plant information in them. One slide described his methods as "weaponizing information in a predictive system," which doesn't leave a lot of room for exaggeration.

  He even used a real client as an example. The question is, how many others do the same thing, but discreetly? If you're looking for market intelligence in social media, do you trust your sources?

• Deception in crowdsourced data
  Another conversation went into the potential poisoning of the crowdsourcing well, in this case one of the crowdmapping efforts in a political conflict. If one party to the conflict entered false reports—perhaps to discredit the project or misdirect a potential response—could it be detected?

• Sockpuppets
  Beyond the crowdmapping context, can you detect opposition personas that post false reports in social media? It's a standard tactic in the government/political arena, but it could hit you in business, too. All you need is a motivated opponent.

It's a little farther afield, but read Will Critchlow's post on online dirty tricks for more ideas on how our tools can (will) be used against us. If you work with political clients, you'll want to understand how they work. For everyone else, it's another lesson toward being an informed voter.

Next: ideas for detecting deception
I don't mean to be all problem and no solution, but this post is already a long one. I'll share some ideas on how we might detect deception in social media in my next post. For now, I'll end with a happier observation: Sometimes, people lie in real life and get caught when they reveal the truth in social media.

Update: Part 2 is now up: Detecting Deception in Social Media

Photo by John Cooper.

When I visited Gnip a few months ago, I saw something I really liked: books, on shelves and ledges scattered around the office. There's a lot to know in this business, and I liked seeing a company make that small investment in developing their team. Are you making the same investment in yourself?

This is why I usually talk strategy and expanding horizons, rather than specific tactics and metrics: it's just too much to cover. When you start going into the details, you find that there are a lot of details. Really, a lot. If you want to read a detailed view of analytics in social media, you don't need a book, you need a shelf.

Loading the shelf
Social media analysis—monitoring, measuring and analyzing social media data—isn't a specialty, it's several, and that means it takes more than one book to cover the details. If you need to understand the whole landscape, you might consider some of these:

• Listen First, Stephen Rappaport
  
• Measure What Matters, Katie Paine
  
• Social Media Analytics, Marshall Sponder
  
• Social Media Metrics, Jim Sterne
  
• Social Media ROI, Olivier Blanchard
  

Naturally, I'm working on a book, too. We'll see how long that takes. :-)

Beyond the specialist books, I recommend an expanded reading list that gets into the broader view of analytics and big data, web analytics (which overlaps with social media), and visualization. If you want a challenge, you might go deeper into the science and technology that make the analysis possible. Plus, of course, you'll want to be informed about the thinking on marketing and management roles that you're probably supporting.

By the time you catch up, more good books will be out there, but that's ok, because a good analyst is always learning. The question is, what books would you add to the list?

From the comments:
Keith Paul recommends Social Media Metrics Secrets, by John Lovett.

Sentiment is the stoplight chart of social media analysis. It offers red and green candy for the boss, and a useful filter for the analyst who's moved beyond the mood ring. Still, sentiment analysis is the surest source of disagreement in social media analysis. Why is that?

The human vs. machine debate has been going on for years, because the software's always been close to the frontier of the science. I started writing about it in 2007; five years later, you can still find companies working closely with university researchers to find better technologies for scoring text. The lag between the lab and the commercial product is virtually zero.

The tradeoff for bringing new technology to market as soon as possible is that it won't be good enough at first. You could read The Innovator's Dilemma to see how that tends to play out. As long as text analytics remains an active area of research, today's products won't be as good as next year's, either.

Look more closely at the tools
The obvious question is, "What's good enough?" But you can run tests and evaluate your options, and I'm not usually a fan of the obvious questions. Instead, let's look at some questions that help you get under the hood of tools as you consider them. There's more to it than the usual discussion points suggest.

• Who or what scored the data?
  Start with the obvious question: is the content scored by a person or a computer program? If it's human, is it by your user, by a vendor analyst, or crowdsourced? If it's automated, is it the vendor's own system or another company's?

• How does the automated scoring work?
  If the system provides automated sentiment scoring, how does it work? The engine that does the scoring could be as simple as a word match or as advanced as one of those research projects that just left the lab. Listen for descriptions of machine-learning approaches or systems that parse the structure of individual sentences. For machine-learning-based systems, can users correct scores, and if so, does the system learn from the changes?

• At what level is sentiment scored and reported?
  Does a sentiment score reflect a document, a sentence, or a statement within a sentence? How are document-level scores determined? How does the system handle, for example, a positive statement about Brand Y in the midst of many negative statements about Brand X? How does it score documents with mixed sentiment (multiple statements with opposing sentiment)?

• What's the scale?
  How many points are used on the sentiment scale—three, five, 100? If there's a number associated with sentiment, is that an intensity scale or a confidence score?

• Does the system go beyond sentiment?
  Does the system analyze statements of opinion beyond sentiment? Can it identify emotions, preferences, or intent?

We could probably get well-informed people to debate each of those topics (sounds kind of fun, actually). Remember that this is an area of continuing research and development, and what's not possible today may be common next year. There's a reason I didn't take a position on the best approach in this post.

It's not sufficient simply to check the right boxes, especially with sentiment analysis. You need to stick with the topic for the long explanation from each vendor, if you want to understand what you're looking at. Let them make the case for their preferred approaches, and then you can make an informed choice.

Photo by Blue Funnies.

What do you get when you look at social media as a source of information about people? This topic usually goes off into a discussion of influence, a result of thinking of social media as media. What if, instead of influencers, you think of the people who participate in social media as individuals?

Obviously, you can go creepy if you do this wrong. Easily. But if you're careful about how you use the information, people are sharing a lot of information about themselves.

All you need to start is an indentifier—a name, email address, Twitter handle—and you start connecting dots. When people include, for example, a Twitter handle in a LinkedIn profile, you can have real name, location, employment, schools… Maybe links to more networks to continue the process, too.

It's not a question of probabilities. When people create links between their various network profiles, it's a clear statement that the accounts belong to the same person.

Why build when you can buy?
This is another niche for startups, of course. Several of which are working to reconcile public profiles across multiple social networks and using that information to create information-rich individual profiles. To make it even more useful, most of these companies offer APIs for integrating their profile data into other systems.

• Data.com (Salesforce)
  
• Fliptop
  
• FullContact
  
• PeekYou
  
• Rapportive
  

Your data plus detailed, individual profiles. What will you build?

Do you put social media data on a map? Location is a handy dimension for slicing, dicing, and visualizing your data. The question is, which location are you visualizing? Even a single tweet—in under 140 characters—can have four different locations.

I've taken a real interest in applying geospatial analysis to social media over the past year. It's been especially appropriate in emergency management and some other discussions with government types. Mostly, though, it's just another lens to apply to social media data, another way to find some value in the data we have now.

So, you want to put social media activity on a map. It's worth thinking about what that location really represents. One little statement can have four distinct locations, depending on how you look at it:

1. Location of the service/server
   Internet-based communications happen in this virtual space where physical location is largely irrelevant, but everything runs on a computer somewhere—even in the cloud.

   You could even separate this one into two (or more) locations—the locations of the server and of the company that owns it—but for most of us, these are the least relevant locations. A few specialists need to know the physical or logical location of a server, but for the rest of us, there's nothing to see here.

2. Location of the account
   Look at an account on Twitter, Facebook, or other social network. Most of them have a place for users to provide their location. Its accuracy depends on the account owner, which is why you see so many Twitter accounts located in "Earth" or something similarly uninformative. During the pro-democracy protests in Iran, a lot of people set their Twitter locations to Tehran in sympathy with the protesters.

   At its most useful, the location associated with an account tells you a default location for a user—home base.

3. Location of the post
   Social and mobile are increasingly two aspects of the same technology-adoption trend, as more people take their social media through mobile devices. With geolocation tagging and location-based services, they're sharing their immediate location: "I am here, now." This is the location you're most likely to see represented on a map.

4. Location of the described event
   This last location won't be encoded in an API, because it's found in the content people share. When they talk about events in the real world, they mention places, possibly indirectly. You'll need a text analytics tool that recognizes locations to extract those. When they post pictures, the photos may include location metadata from the camera.

Let's put them all together with a couple of hypothetical examples. We'll ignore the location of the server, because it's not relevant for most uses.

• Let's say that I tweet about an event in Egypt (4) during a break at a conference in Washington (3). My account location (2) is in North Carolina. How does that compare with a geotagged photo (4) of the same event sent from Cairo (3) by an account that says it's located in Cairo (2)?

• It's another stormy day in the middle of America, and someone posts a picture of a damaged building (4) on Facebook. The account location (2) and post location (3) are nearly the same, and they're in the projected path of a tornado, based on National Weather Service radar data. Do you believe that a tornado hit the building?

Despite all of that muddying of the water, you're probably ok if you use the per-post geolocation data for most purposes. When in doubt, always remember to state your question clearly, and then you can pick the right data to answer it.

Illustration: Map of a Twitter status object by Raffi Krikorian.

Does your social media program include foreign language requirements? Even if your company does business in only one country, you might need multiple languages. The question is, how much capability do you need to check off the language box?

An email from a vendor contact in Tokyo reminded me of a conversation at the Tech@State conference a few weeks ago. We were talking about monitoring Arabic-language social media, and someone pointed out that their analysts know Arabic. They don't need translation; they just need to collect the content for their analysts to read.

It's an important distinction, and it led us into a conversation about what a monitoring platform needs to do to support different types of users. The short answer is, language capability is more than a one-box checklist item. You have to know your needs in order to evaluate tools.

International support in the software
Let's start with the scenario from that initial conversation: an organization is looking for a software platform for monitoring or analyzing social media content in a specific set of languages. Here are some tool capabilities that might back up a claim about language support:

1. Find content written in a language.
   Theoretically, all you need is support for the required character set, search terms in the desired language, and a broad range of sources. In practice, it's harder to collect content in some countries and languages than others. Ask about source coverage in the countries you need to include.

2. Translate foreign-language content.
   In the age of Google Translate and other machine-translation programs, it's easy to add a translate button to a tool. If your needs are simple, machine translation could be good enough.

3. Filter content by language.
   The most basic level of language support involves identifying the language used in a text. Based on some of my testing, that's harder than it looks. Tools that can identify source languages usually offer filters based on the language, which is useful for directing items to analysts who can read them, as well as for analysis of content by language.

4. Apply text analytics to content in the language.
   Adding more languages to the analytics engine of a social media platform is hard work. I've heard from several sources that adding sentiment analysis in another language, for example, is equivalent to starting over. If you want your tool to do text analytics in a specific language—sentiment, topics, entity extraction, and the rest—ask specifically if those features are supported in the languages you need to analyze.

5. Provide a user interface in a language.
   So far, this has all been about the content. If you're working with native-speaker analysts, though, you may also want to support them with a user interface in their language. I've talked with people at companies that monitor social media in multiple languages using teams in multiple countries. Giving them a UI in their own language(s) is a nice touch, and one that probably pays off in increased productivity.

International support in services
Now, let's look at the other side of the business: the services market. One easy way to add coverage of additional markets is to send the work to an agency that has those capabilities. The first question is, can they support the languages you need? The follow-up question is, how do they do it?

1. Multilingual analysts
   Is it adequate to have an analyst who knows the language? Depending on your circumstances, it could be.

2. Native language analysts
   Anyone who's studied a foreign language knows that it's easier to learn as a small child. Native fluency makes the analyst more likely to catch subtleties that a non-native speaker might miss.

3. Native analysts located in foreign market
   If your native-fluent analysts are current residents of the foreign country of interest, they may be better attuned to current events and cultural trends than their peers working in another country.

4. Vendor based in foreign market
   Social media analysis firms are virtually everywhere (try searching a country name in the directory). You can find native analysts who work abroad for international firms, and you can find them working for smaller firms based in their country. Working with foreign vendors adds complexity, but it could be the right answer in some circumstances.

It's easy to make up a list of languages and mark them yes or no. When I did my first report on companies in social media analysis in 2007, I didn't go much farther than that (I did ask about native fluency among analysts). If you're building a capability with international scope, be clear about the level of language support you need, and you'll be a big step closer to finding the right partners for your program.

I've been thinking lately about nuances in product requirements. More to follow.

Photo by David Weekly.

Quiz: A government agency wants to monitor social media in the course of performing its function. Is that an obvious use of public information, or further evidence of a dark conspiracy? Oh, good, I see lots of hands for both answers. Let's look at what's really going on here.

You have zero privacy anyway. Get over it.
—Scott McNealy (1999)

When people hear about social media monitoring by a government agency—such as the recent news of FBI, DHS, and CIA programs—the usual response is outrage about the perceived violation of privacy. People are living their lives online, and they don't want the government listening in.

Superficially, that's completely understandable. Most of us don't want people eavesdropping on us, even if we aren't hiding anything and don't harbor conspiracy theories. We just like our conversations to be kept within the group we think we're talking to. The usual response makes intuitive sense, even if we realize that these online conversations are, technically, public.

(By the way, I'm assuming that we're talking about governments in free, democratic countries here. Events over the last few years have clearly demonstrated the danger to people sharing information and opinions in countries with repressive regimes during times of instability. Sometimes, it's easy to decide whether the government is using or abusing people's information.)

Expectations of privacy
Where do we get this expectation of privacy in public places? Everybody knows that Twitter is public (unless you make your updates private), Facebook has public updates, YouTube is for the world, many forums are public, and blogs are a form of publishing, right?

How can we expect privacy in a public place?

Read that last sentence again, and I think we'll start to see what happened. We're not really talking about a public place—it's not a place at all. All of this Internet-based communication happens in a virtual space, which is shared by everyone. Virtual means almost, which also means not. A virtual space is not a real space; it's an artificial environment that is different from the real world in important ways.

The nature of public is one of those ways.

Public doesn't mean what it used to mean
Imagine having a conversation with a friend in a public place—a city street, maybe, next to a bus stop, or a sports stadium during a game. These are public places. We may have norms against eavesdropping, but someone standing close to you might hear your conversation. So your expectation of privacy is reduced, compared to when you have a conversation in a home or office.

The physical world imposes limits on the potential audience for conversations. Sound drops off over distance, and quickly. Other sounds in the environment block out the conversation, too. If you're talking while a bus leaves the stop or a big play happens on the field, even the person you're talking to might have trouble hearing you. A few feet away, you're inaudible. Across the street or stadium, you may as well not exist.

The Internet is different. A whisper on the other side of the world is as clear as a shout in a quiet room. A million people can talk at the same time, and we can pick out individual conversations—all of them. Say something today, and it's still there tomorrow. Time, distance and the crowd—none of them recreate the semi-privacy we experience in physical settings.

The conversation at the bus stop and the isolated tweet are both public, and yet they're entirely different. The differences come back to the difference between the Internet and the physical world. People react to the perceived violations of privacy because they learned their ideas of public and private in the physical world, and the different physics of information in the virtual world break their mental models.

A clear dichotomy
The virtual world also breaks the in-between states of semi-private and semi-public. There's no semi online. Private is uncertain, too.

Three can keep a secret, if two of them are dead.
—Benjamin Franklin

Some online venues make the attempt to be private, but it's enforced with terms of service and technical measures that can be defeated. Any notion of privacy in online communications has an element of trust, which may be backed up by contracts or law. But it's not private in the same way as a conversation in a closed room.

Public discussions, on the other hand, are really public, in a globally ubiquitous way that the physical world can't match. Those open Twitter accounts and blog posts, the groups and forums that anyone can read. Comments on newspaper sites and book reviews. Videos and pictures uploaded all over the place. Anyone can see them—milliseconds or months later.

This isn't the first time
We've run into this qualitative change in the nature of public information before. Think about public records that the government keeps, such as on property transactions. These records have always been public, but pre-Internet, realities of the physical world created barriers to access.

If you wanted to look at property records, you had to go to the clerk in the appropriate local government office. You'd probably wait in line, and when it was your turn, you made your request. If you asked for something the clerk could find, you could look at the file, and you might pay ten cents a page to get a copy.

Where's the record today? It's on the web, with a database query engine that lets you look up properties by owner or address, with wild cards in your queries. If you don't find what you want, you look again—as many times as you like. When you find something interesting, you have all the information, which you can save or print as much as you like.

On other web sites, that same public record is aggregated with many others, mashed up in a map that shows house prices everywhere. Zoom out, get the big picture. Zoom in, find out what your neighbor paid for that house. It's the same public record, but putting it on a computer and making it available on the web completely changes what it means to be public.

The world changes faster than we adapt
We're so used to the constant rush of innovations and what we can do with them. We're not so good with thinking about the implications and adjusting our mental models. People start sharing their lives in these public channels, without thinking about what happens to the information. Remember the first stories of job applicants who shared the wrong pictures in Facebook?

Now, government agencies are opening up about their interest in what people have to say online, and we have this wounded sense of privacy based on expectations from the physical world. All that data is public, in the expanded sense of online public information. Did people think that officials wouldn't find it useful?

The value to government is obvious, but we need a reasoned discussion on the appropriate tradeoffs between government use and individual protection. All of which is far too much for an already long-winded blog post.

Related posts:

• Whose Ethics Apply in Social Media? (Oct 2010)
  
• Ethical Standards for Listening Vendors (Oct 2010)
  
• Ethics, open sources and CI (Mar 2007)
  
• When Geolocation is Too Good (Aug 2010)

Photo by Jeff Schuler.

I like blogs for developing and sharing ideas, but if you really want to see progress, you need to spend time with people, face to face. Notice which problems get them animated, and which topics bore them. Look in their eyes to see which ideas are working and which are not. Considering the unresolved questions of measurement and analytics in social media, spending some time together sounds like a great idea.

That's why I'm excited to be a part of the Social Media Analytics Summit, taking place April 17–18 in San Francisco. As conference chair, I get to present a couple of sessions, moderate a couple of panels, and generally stay in the middle of things throughout the event. Offstage, I plan to spend a lot of time listening to what people are doing and seeing how they respond to the other ideas in the room.

I also plan to have a very pleasant time with the people who would go to a conference dedicated to social media analytics. These conferences with very specific topics are always good for meeting interesting people. And, you know, business opportunities have been known to emerge in these gatherings, too.

The program includes some very sharp folks (I would know, I invited some of them), talking about the burning questions, effective strategies, and practical applications of social media analytics. It's a safe bet that everyone will learn something from this group, starting with the pre-summit interview series. As always, the conversations after the sessions will probably be even better.

Psst. You want a discount?
If you read this blog—and I think you do—the Social Media Analytics Summit is worth a look. This isn't social media in the context of a larger conference; it's all ours. If you decide to attend, use the discount code NATHAN300 to save $300 on your registration. Super Early Bird pricing is good until February 17, so you have a couple of weeks to think about it before the price goes up.

See you in San Francisco.

Photo by Abhishek Chhetri.