Demand Media, the newly public content company that’s doing everything it can to avoid the term “content farm,” has been engaged in a head-to-head battle with Google since just before Demand issued its public shares. That battle got jacked up a notch or two recently, when the search engine tweaked its algorithm to crack down on what it calls “low quality content” and Demand’s eHow unit got caught in the crossfire. Demand says this isn’t a big deal, but the reality is that its biggest partner is also its biggest enemy, and that’s going to dog the company — and the stock price — for the foreseeable future.

Google’s first content-farm related algorithm update in February (known as the “Panda” update, the nickname of one of the engineers involved in designing it) hit a number of content companies fairly hard, pushing down results from Associated Content and Suite101, among others. But Demand’s eHow site — which represents a large proportion of the company’s content business and also a substantial chunk of its revenues — escaped with very little impact, which came as a surprise to many. In fact, some estimated the site’s pages were actually showing up higher in search, not lower.

That changed dramatically with Google’s latest algorithm tweak, however. It’s not clear whether the search giant rolled out its latest update specifically to target eHow and others who were missed in the first go-round, but according to at least two tests — one from Sistrix and one from Cnet  – eHow pages are now showing up as much as 65 percent lower in Google results. And the new algorithm changes were based in part on feedback from users about what webpages they found least useful.

Demand Media published a blog post late Sunday night saying the Google algorithm change did affect eHow’s rankings, but that estimates of the severity of the decline were “significantly overstated.” In the kind of careful language befitting a newly public entity, the blog post — which included a legal disclaimer almost as long as the post itself — said Demand expects to generate year-over-year page view growth “comparable to or greater than the year-over-year page view growth reported for Q2 2010,” which was 25 percent. The company also reaffirmed its financial guidance for 2011. Despite those reassurances, however, the stock price fell by close to 10 percent in early trading on Monday.

In other words, as far as Demand is concerned, things are just fine. But investors shouldn’t be quite so sanguine. For one thing, Google continually tweaks its algorithm, and there’s no reason to believe this is the last update related to content farms. And the big picture for Demand remains the same: namely, it relies on Google for an estimated 40 percent of its traffic and about 30 percent of its revenue, as described in its IPO securities filings, and therefore, the search giant holds the keys to its ongoing success in that market. Google has made it clear that “content farms” are a problem that needs fixing. As Demand said in the “risk factors” section of its prospectus:

Google may from time to time change its existing, or establish new, methodologies and metrics for valuing the quality of Internet traffic and delivering cost-per-click advertisements. Any changes in these methodologies, metrics and advertising technology platforms could decrease the amount of revenue that we generate from online advertisements.

Demand has said it’s working on boosting the quality of its content (something other “crowdsourced” content distributors such as Examiner.com are also doing in order to avoid the wrath of the great and powerful Google), but this is also likely going to increase costs at the company, another thing investors should be aware of. That and the need to constantly look over its shoulder at what Google is doing with its algorithm are going to be the biggest challenges facing Demand for some time to come.

Post and thumbnail photos courtesy of Flickr user Crystaljingsr

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Content Farms: The Players, The Benefits, The Risks
• Demand Media: Search Spam or the Future of Content?

For those keeping track at home, that kind of revenue growth is pretty fantastic, but not terribly surprising. After all, YouTube serves more than 2 billion videos a day, including more than 100 million a day on mobile devices. And the site has very aggressively ramped up advertising over the past year, placing more ads against partner videos and those that run on mobile devices.

Pichette didn’t state revenue numbers, but doubling growth is not totally out of line with previous analyst projections. Barclay’s Capital analyst Doug Anmuth estimated that YouTube pulled in $450 million in revenues during 2009. While Anmuth’s forecast last January called for 55 percent growth, others were less conservative. Last March, Citigroup analyst Mark Mahaney estimated that YouTube would generate nearly $1 billion in sales for the full year.

While doubling revenue is a plus, it’s important to note that Pichette said nothing about YouTube’s profitability. The question of when the online video site would finally turn a profit has been much debated over the past several years. Despite forecasts early last year that YouTube could become profitable in 2010, it seems that the unit — which Google paid $1.65 billion for back in 2006 — still isn’t in the black.

Coincidentally, the Google call was hosted on YouTube and suffered from some pretty dramatic stuttering and lag, which is surprising in part because it was audio-only.

Related content on GigaOM Pro:

• Web-based Strategies for Engaging TV Viewers
• Three Reasons Over-The-Top TV Apps Will Beat Big-Cable
• Connected Consumer 2011: What Not to Expect

Priority Inbox, which attempts to deliver the most relevant emails to the top of a user’s inbox screen, combines the behaviors of all Gmail users with your personal preferences and behaviors to deliver an inbox where your most important mail gets read first.

It’s as if your doctor could compare your physical complaints with all of the symptoms experienced by people everywhere in the world, in order to deliver a diagnosis in a few seconds. Not impressive if you have a cold, but if you suffer from a rare disorder, it’s amazing. The paper is chock full of math and explanations of how Google does this at scale (sharding databases and using Bigtable across tens of thousands of servers), but the crux of the matter is Google trying to apply machine learning to determine what each Priority Inbox user cares most about. To do that requires a computer connected to the cloud, and Google’s back-end servers. It’s an illustration of how massive computing power in the cloud and a client device can interact in ways that benefit users.

The biggest challenge isn’t necessarily the huge data crunching on the back end; it’s accounting for what the paper’s authors and statisticians call “noise,” and what I call the oh-so-human tendency to do what we want, not what’s most productive. For example, in email, we waste a lot of time and productivity opening silly emails about Lindsay Lohan’s latest escapades while ignoring those from our boss:

Opening a mail is a strong signal of importance for our metric, but many users open a lot of mail that is “interesting” rather than “important”. Also, unlike spam classification, users do not agree on the cost of a false positive versus a false negative. Our experience showed a huge variation between user preferences for volume of important mail, which can not be correlated with their actions.”

The challenge for machine learning is to calculate the signal from the noise on a massive scale in real-time, so your LiLo emails get sent to the bottom of the stack, but can still be read.

The researchers say that for Googlers who receive similar volumes of mail, Priority Inbox users spend 6 percent less time reading mail overall, and 13 percent less time reading unimportant mail. So while Priority Inbox may end up making you more productive, you might have less to chat about at the virtual water cooler. Unless you use those time savings to hang out on Twitter.

Do you use Priority Inbox? Do you think it makes you more productive?

Related content from GigaOM Pro (sub. req.):

• Enabling the Web Work Revolution
• Report: The Real-Time Enterprise
• Who Owns Your Data in the Cloud?

With Demand Media currently planning a high-profile IPO and issues being raised about the impact of “content farms” on search results, there is a lot of attention being paid to the question of content aggregation and its effect on the web. OrganizedWisdom Health, a health-resource service, now believes it’s better — and more profitable — to organize free content rather than pay for low-wage material.

The New York-based startup has spent most of the past four years building up an index of “wisdom cards:” topical health entries that were written by contributors for a small fee. At any given moment, OrganizedWisdom contracted with 100 writers, who covered a wide array of medical topics. But exactly one year ago, the company dropped the paid-content route and embraced a free expert model. So where does the content come from? The startup keeps track of some 5,000 doctors and health experts who use social media, and collects their contributions into wisdom cards.

OrganizedWisdom does not pay these experts, and instead relies on their desire to share their wisdom, market themselves and raise their reputation. The doctors are vetted, and applicants for OrganizedWisdom are increasingly being turned away to keep quality high, the company says. The goal is to give the doctors a chance to share on a wider stage while giving users a helpful dynamic resource made up of organized expert content. In the end, it offers users a more reliable way to find health information, which is increasingly bogged down by bad search results, said Steve Krein, CEO and founder of OrganizedWisdom.

“Integrating an expert graph into the search process is the way to solve the filter problem,” said Krein, who previously founded Promotions.com. “You want to tie humans into the process to make it all more intelligent.”

The reliance on free content is reminiscent of Wikipedia and, more recently, Quora and Stack Overflow, two answer sites that have both grown quickly. But unlike those sites, OrganizedWisdom is focused on only working with experts, which it believes will allow it to maintain quality as it grows. The switch to free has actually improved the quality of content on the ad-supported site and helped the start-up get profitable by the middle of last year, something executives said wouldn’t have been possible under the old model.

“We were finding better experts by focusing on those who want to do it to improve their reputation or because it was their passion rather than get paid nominally,” said Unity Stoakes, the president and co-founder of OrganizedWisdom.

Krein said the goal is to ultimately get more doctors online, sharing their wisdom with patients. He said of the 700,000 doctors in the U.S., only a small fraction actually blog or share links, but the last year has brought more doctors willing to go online, which has enabled OrganizedWisdom to move in this direction. The company has raised $3 million from Seventy Six Capital and individual investors including Esther Dyson, former Time Warner CEO Jerry Levin and Jason Finger, founder of Seamless Web. The company is currently pursuing a second round of funding.

The change in business model shows that focusing on quality could potentially trump quantity, as well as being better for the bottom line. This may not do much to affect the giant content farms, whose businesses are based on carpet bombing the Web with SEO-optimized articles on everything under the sun. But if Google starts ratcheting up its efforts to weed out low-quality search results, OrganizedWisdom could be better positioned than Demand Media and its ilk.

Related GigaOM Pro content (sub req’d):

• Why Google Should Fear the Social Web
• Lessons From Twitter: How to Play Nice With Ecosystem Partners
• What We Can Learn From the Guardian’s Open Platform

Users can share uploaded videos with coworkers in much the same way they can share documents, and the video source files can be downloaded from anyone who has access to the clip or had it sent to them as an email attachment. The new video feature even supports some rudimentary versioning, but only the most recent version can be played back online.

One interesting aspect about the video playback capability is that it’s using YouTube’s video player, but uploaded videos don’t make use of YouTube’s unlimited storage. In other words: Your uploads are limited to 1GB total if you’re using a free version of Docs. Want more? Then you’ll have to pay up and buy extra storage.

That’s a curious arrangement, especially in light of the fact that YouTube itself also offers a number of ways to privately share videos. However, one could make the case that these are two different use cases. Want to share a video with the world? Then upload it to YouTube. Want to work on it together with your colleagues, with access to the source video files? Then keep it in Google Docs, where it can’t be published accidentally.

Google has long been rumored to be introducing a personal storage product, dubbed GDrive or Platypus at one time or another. It looks like video playback in Google Docs could be one more step toward the launch of this type of functionality, but it also looks like we shouldn’t expect for Google to give us unlimited free storage anytime soon.

Related GigaOM Pro Content (subscription required):

• Google’s New Route to Your Wallet: Music and Books
• Monetizing the Social Web Isn’t One Size Fits All
• New Use For Web Stats: Finding Hot Markets, Offline

1. Advanced Segments

Don’t feel limited by looking at your data through the segments that Google Analytics defines as defaults. While all visitors, new visitors, and returning visitors are certainly interesting, you should try defining some of your own. I have created a segment for looking at the behavior of frequent visitors, meaning visitors who have come to the site 5 or more times in particular time frame. It’s possible to compare such a custom segment with other segments, so I can see if frequent visitors spend more or less time on the site, and visit fewer or more pages than new visitors, or other returning visitors.

You can create some very detailed segments, too. For example, I created two test segments: both are from Western Europe, but one segment is Firefox users, and the other is Chrome users. I compared the two, and found that people from Western Europe using Firefox spent more time on my site and visited more pages. You can even use these advanced segments on the custom reports that I mentioned in my previous post.

You can define a custom segment by going to the “My Customizations” box and selecting “Advanced Segments.” Once you have an advanced segment defined, an “Advanced Segments” drop-down menu will appear in the gray bar at the top of your reports.

2. Navigation Summary

The Navigation Summary lets you look at any page on your website, to find out what page they came from, and what page they went to next. This provides interesting information about whether key pages are fulfilling their purpose.

For example, my blog has a “Starting Point” page that was designed to help people find articles that I’ve written. The Navigation Summary shows that from my Starting Point page, most people go to my page on Yahoo Pipes and RSS Hacks. I suspect this is because of the way the navigation on the page is designed; I can use the analytics data to reorganize the navigation to make my content easier to find.

You can get to the Navigation Summary by visiting the “Content Overview” page and clicking on “Navigation Summary” in the right column, underneath the graph. On the Navigation Summary page, select the “Content” drop-down box to see navigation data for any page. Dennis Graham has written an in-depth explanation of some other interesting ways to use this data.

3. Views

Detailed data in Google Analytics is shown as a table by default, but you can also see the data as a percentage pie chart, performance bar chart, comparison chart, or even a pivot table with all kinds of interesting data about your visitors. For example, a pivot table could be used to compare visitors from Google in Asia who are new or returning.

Looking at a pivot table of my top content, it was interesting to see that people who land on certain pages from Twitter spend much less time on the page than people who arrived from some of the other sources.

To access the various views, go to any page with a table and look under the right side of the top graph or just above a table to find a “Views” section with five icons representing your different views.

What are your favorite Google Analytics tips?

Related content from GigaOM Pro (sub. req.):

• Enabling the Web Work Revolution
• Report: The Real-Time Enterprise
• Social Media in the Enterprise

Administrators of domains that use Google Apps can now enable a technology known as DomainKeys Identified Mail (DKIM). This technology is intended to prevent “spoofing” of messages, the widely-used spam technique of sending emails with a bogus “from” address, or one that’s real, but not sent by that person. DKIM uses a pair of keys — one in the domain’s DNS records, and one in sent messages — supposed to prevent address spoofing.

This new option is added to SPF, another spam-fighting technology that Google Apps has supported for some time. And users of the paid Google Apps for Business suite can refine their spam filtering using Postini, which Google bought several years ago.

If these spam-fighting tools aren’t enough, Google has also announced that it’s now possible to entirely turn off the ability for some users to send or receive emails from outside one’s own domain. While this feature was originally intended for Google Apps for Education used in schools, it’s also available in the Business and Government editions.

I’m not sure how many businesses would find internal-only email addresses useful, since there are many other options for communicating privately within organizations. But internal email would have some similarities to the “not-really-email, only-receive-messages-from-your-friends” Facebook Messaging system.

How many spam messages are arriving in your inbox? What do you use to control spam?

Image by sxc.hu user kveselyte

So what does that actually mean? The site presents a montage of images, video, animations and other visual resources culled from search results, and overlays it with real-time narration, using text-to-speech technology. The result is information as a watchable experience.

For me, the actual experience of watching the content is eerie and unsettling. I’ve never liked the way text-to-speech technology sounds; it’s strange and unnerving to my ears and brain, sounding almost, but not quite, human in a way that could mean trouble, like HAL in 2001: A Space Odyssey.

My first search was for “Tok,” the rural Alaskan community where I live and work. The text-to-speech voice sounded nearly human, but with mispronunciations and odd inflections that are inherent in the technology. Qwiki pronounced my community as “Tawk” rather than the correct pronunciation, “Toke.” Right away, this interfered with my ability to appreciate the visual montage.

The audio was accompanied by a flowing stream of imagery that seemed at first to correspond with the narration. But on closer examination, many images had nothing to do with what was being said, other than being from Tok. For example, when the narration mentioned Tok School, the images that appeared were of a gift shop and an RV park. Another image of a burned-out old gas station showed up, and I immediately wondered how I could remove such a photo, and replace it with something more representative of the community. Ditto for an image of a coffee shack that isn’t even in Tok, but is over 200 miles away, near the community of Glennallen.

My second search was for “karaoke.” I found the experience of this information less off-putting. However, the images in the presentation, while colorful and interesting, seemed less familiar. Many turned out to be of displays and equipment common in Japan.

My third search was for “social media.” The narration was a bit convoluted and the visual presentation consisted of a single static screenshot of Flickr.

My final test search was for…me. Here’s what I found (login required). And here’s what it looked like–apparently I have one of the dirtiest minds in business–or at least that’s what was displayed during the entire presentation!

Qwiki may give us a new phrase to replace “Googling ourselves.” We can now “get a Qwiki” and “give a Qwiki.”

Currently, Qwiki covers over two million reference terms, which feels sufficient for pretty good results when searching for a not-too-uncommon term. You can also search for people and places.

For me, the visual and audio dissonance of Qwiki was initially disturbing, but I tried to look past that and appreciate that I was “experiencing information.” Presentations are short–about 30 seconds–which is just enough time for narrative content from a paragraph or two from a Wikipedia entry.

Conceptually, Qwiki is a fascinating step forward in the presentation and consumption of search results. Since the site is in its alpha phase, it’s available only by invitation, although you can request one at Qwiki’s website. You’ll probably start itching to fix what isn’t working, but Qwiki doesn’t yet have a Wikipedia-like system for collaborating on editing information. However, the company is very open to input on ways to improve the experience.

Give Qwiki a try, and let me know what you think of it. What implications do you think it will have for the future of search?

Related content from GigaOM Pro (sub. req.):

• Social Media in the Enterprise
• Email: The Reports of My Death are Greatly Exaggerated
• HTML5’s a Game-Changer for Web Apps

ReSearch.ly’s premise is that “search is a social act which relies on trust and community.” Developed by the folks behind PeopleBrowsr, ReSearch.ly creates “instant communities” around the things you publish, respond to, or search while you use Twitter. Your searches become “sharable objects” and the site purports to add context to your searches by providing additional related information–all from tweets on Twitter.

The site provides what they refer to as “degrees” of access to, and filtering of, information:

1. Geo Search. This is a local or regional search of the Twitter community that can increase relevant results, such as places to go for an evening out.
2. Local Trending Retweets. By seeing what others are retweeting, you can see what’s popular, and immediately participate in conversations of interest.
3. Your Community Search. You can search your Twitter stream for specific niche communities, and filter out less relevant conversations.
4. Search Within Search: You can drill deeper within a particular search by, for example, adding geo-searching to a topic-based community search.
5. Degrees of Separation. This is an analysis of your web of networks: who is connected to you and to other Twitter users.
6. Related Search. This allows you to enhance your search by providing other relevant information such as related hashtags, links or @ references.
7. Share Your Search. You can share your search, and make the way you are compiling and experiencing information into a social activity.

The premise of ReSearch.ly seems reasonable and potentially useful: When you search for tweets, you’ll be able to get more context to the content of interest. For a first-time user, the reality may be a bit confusing.

I tested the site by searching for “Napa” because I’m going to Napa Valley soon. By viewing search results through the Global filter, I quickly learned that “napa” is a word in Indonesian. But when I narrowed down results to my Twitter community (my followers or people I’m following), I begin to see much more relevance: reviews of Napa wines, mentions of Napa hotels, and tweets about Napa Valley events. I was able to narrow results to only male or only female Twitterers, only positive or only negative tweets, or only retweets. I could also click on the United States tab to get a broader view of who was tweeting “Napa.”

When I hovered over a person’s tweet, a “Degrees of Separation” link appeared. Clicking on that link showed the path between me and that particular Twitterer–how I might be connected to that user and, by extrapolation, how much I might be able to trust him or her. Based on the information I discovered, I could opt to follow individual Twitterers.

Search results also included charts showing global use of “napa” in the past 7 and 30 days. I could view the “Sentiment” for the word, and its popularity. I could also see words surrounding the original search term, such as “valley” and “wine,” but also “napas” and other words in Indonesian. Below that, I could see the most tweeted links where Napa was mentioned, related hashtags including #wine and #loveindonesia, and @ names referenced in tweets mentioning “napa.”

After that, there were images of pictures and videos where “napa” was mentioned, including wine-related images, plus random images where I couldn’t discern the context. They could have shown up because “napa” has other meanings internationally. A quick tweet to my followers revealed it’s short for “kanapa,” meaning “why.” So I learned something, but not directly from ReSearch.ly.

I’m all for slicing and dicing search results in new ways. At first blush, I found the results from ReSearch.ly to be curious. But ReSearch.ly is billed as a social search platform for “online marketers, brand managers and social media experts” that provides demographic data, psychographics, and instant viral analytics, along with location, gender, and retweets. I saw the location, gender and retweets data, but I didn’t immediately see how the information would help me as an online marketer, brand manager or social media expert.

I did another search for “SXSW” and the results were more relevant, most likely because SXSW is a much more distinctive term. The breakdown of the search results, however, didn’t seem particularly useful. Then again, I was searching not as a marketer or social media expert, just as someone thinking about attending the SXSW 2011 Interactive conference. I did discover when adding “Interactive” to the search that there are SXSW Interactive events in other cities happening in January. Now that’s interesting.

In conclusion, the service is complex enough that users may need more help from the company. You’ll need to decide whether the value of ReSearch.ly’s “social search” goes beyond mere curiosity by adding social pathways and context to search results.

What are some of your experiences with “social search” so far?

Related content from GigaOM Pro (sub. req.):

• Can Enterprise Privacy Survive Social Networking?
• Social Media in the Enterprise
• Enabling the Web Work Revolution

In the movie Minority Report, the government tried to predict future crimes by using a trio of “pre-cogs” who had visions of what was going to happen while lying in some kind of chemical bath. Stock traders don’t have that kind of setup yet, but they are doing everything they can to predict the future — and that involves crunching every bit of data they can get their hands on, up to and including that tweet you just posted. The New York Times recently described this phenomenon, and a hedge fund says it plans to launch a new fund that will trade stocks based in part on an analysis of market sentiment as defined by Twitter.

Stock traders have always looked at market-sentiment indicators, including some who study fashion trends or popular music to try to determine which way stock indexes are likely to go. It’s a little like science and a little like voodoo, as even some traders will admit. But the key is information — as much information as possible — and investment banks and hedge funds now have more of it than they could ever want, and the computing power to take advantage of it by crunching and analyzing it.

In a sense, they are doing the same thing Google does, but they are doing it in order to figure out which stocks to buy, not because they want to serve up related advertising. This is just part of the future of what we call “big data.”

The Times story describes how information services such as Dow Jones, Thomson Reuters and Bloomberg are trying to cater to this demand for more data by adding features to the terminals and software they sell to banks and investment houses, which tries to parse the sentiment of news stories, blog posts and even Twitter messages based on the use of common words and emoticons like the “smiley.” One portfolio manager at an equity fund says he feeds that data into his trading systems, and that such features give him “the ability to assimilate more information.”

Meanwhile, a hedge fund called Derwent Capital Markets says it will launch a new fund in February that will trade based in part on analysis of Twitter sentiment. This approach is built on research from the University of Manchester and Indiana University (PDF link) that showed how the number of emotional words on Twitter could be used to predict moves in the Dow Jones index. Researchers said they found that a change in emotions as expressed on Twitter would be followed by a move in the index between two and six days later, and that this method had greater than 87-percent accuracy.

The fund is apparently going to use other data in its analysis as well, but since the fund company has signed an exclusive deal with the researchers who published the sentiment paper, it sounds like tweets will play a major role in the trading. Will it work? Some are skeptical — including Reuters blogger Felix Salmon. My only question is: How long until someone starts setting up spam or bot accounts to try to game specific stocks? I’d give it about a week — if they don’t exist already. Welcome to the future.

Related GigaOM Pro content (sub req’d):

• Why Google Should Fear the Social Web
• Lessons From Twitter: How to Play Nice With Ecosystem Partners
• What We Can Learn From the Guardian’s Open Platform

Post and thumbnail photo courtesy of DVDActive