What Losing a Socialbots Competition Taught Me About Humans & Social Networking

When social network users consider robots networking, anything from fake Twitter pages spamming links to Chat/AIM bots could come to mind.While 'social bots' may so far be, at the best, still brushing up on their social skills and, at the worst, outright malware, it may not always be like this. A recent Gartner study predicted that by 2015, 10% of users within an individual's social network will be robots. This doesn't necessarily mean that 10% of a user's network will secretly be automated pages, but that brands may leverage automation to provide basic interaction (basic question & answer, news aggregation & promotion, customer service) without direct personal staffing.

While 'social bots' may be quite some ways from the de facto choice for basic communication, current technologies seem to be leaning toward options to create an automated solution. CoTweet and other group Twitter tools help to manage messages based on topic and priority (a step towards prioritizing interaction), while API developers are already creating interactive solutions that mimic basic user functionality (both for useful and malicious goals). While the most common example of an automated page a user might interact with presently is a suspicious model photo attached to a Twitter account promoting odd links, this could change soon enough.

With a sales pitch like that, how could anyone say no...

Personally, I found out more about 'social bots' through entering the Web Ecology Project's Socialbot 2011 contest. After first reading about the contest on Twitter, I helped to field a team of other interested media folks. In total, 6 teams entered and 3 fielded entries, with a spectrum of contestants coming from computer science & programming, development & media. The range of skills interested in the contest shows the areas of development that are going to drive technology like this forward.


The Contest

The network diagram above shows the initial 500 users and their connections between each other before competition efforts

(Source: Web Ecology Project)

The competition itself hoped to expand what we know about social robotics through development of an automated Twitter page(s). Each team had two weeks to develop a profile or series of profiles, which once activated, would run uninterrupted for 2 weeks (with the exception of a halfway point improvement day). Within these two weeks, the page would reach out to 500 pre-selected users, randomly sampled by the contest holders, who must be interacted with. Teams couldn't tell users they were in a contest, but could indicate that their lead page was a robot.

Teams scored points by driving user interaction. Scoring consisted of:

• 1 point - per each user following a team's lead Twitter page
• 3 points - per each user RT'ing a team's content, '@' replying to the page or mentioning them

Users would be allowed to restart their page 3 times after being reported as spam, before finally loosing the ability to score. Other teams weren't allowed to mention that a competitor page was in a contest or report them as spam.

At the end of the two weeks, the team with the most points wins $500 and the 'Socialbots' Cup.


The Strategy

Our Team's strategy, (Team MS-UK / Team A in the final standings) operated on three core principles:

• The socialbot would use content to drive interaction
• Twitter's TOS would be respected as much as possible, with every part of the plan judged against  avoiding spamming users and use the targets reporting spam pages as a reward for us and a barrier for competitors
• Wherever possible, the page would utilize realism to get as valid an interaction as possible

Based on these principles, we decided upon making a singular page crafted against the clustered interests of the 500 users. To begin, network analysis started working with the provided data from Web ecology and moved onwards to analyzing publicly available data from page names, connectivity ratios and tweeted content.

The provided clusters created 9 distinct areas of users, each with different interests, locations and ways of using Twitter. Prominent interests groups included: animal lovers, UK/EU residents, sports lovers, social media experts, metropolitan career women, hunting enthusiasts & other suspected robots. Users judged to be under 18 were automatically excluded from any activity by the robot. Surprisingly, the robot segment contained at least 60 automatic pages, creating a situation where a robot was programmed to engage with other robots.

Within each segment, users were prioritized by authority within the group (e.g. the number of other segment users following them), with those that were most authoritative set as influencers.


The Persona

 

 Our automated page ('@sarahbalham') aimed to look like a real twentysomething's page, with elements of her persona emerging throughout it (i.e. Wooded background). Though I always worried a graphic designer using a web generated icon as a avatar was a bit of a giveaway.

 

 Based on the data gathered from cluster analysis and influencer content, our entry persona was set as:

• a 24 year old female graphic designer (decided as it was an age/gender mix which fit conversation with multiple segments and a career which allowed for a range of content to be discussed)
• an expat from the Southern US (Georgia) living in London UK (chosen as it related well to the hunting/metropolitan/EU & UK segments, as well as mirroring the authors current experience, which allowed for voracity in tweeted content)
• interested in design, fashion, art, the outdoors & music (allowing for content which would reach across segments without alienating non-engaged users)
• a proud dog owner of a small, but loved dog named 'Russel' (a way to engage both animal owners and hunters through the love of a pet
• name wise, a combination of a London Borough & a neurological structure led to us calling 'her' "Sarah Balham"

The Program

User segmentation allowed our program to follow a set group of users each day, meaning that users were added gradually over the course of the first week. The program avoided mass following & 'churn' (unfollowing those who wouldn't follow back and then following them again shortly), instead adding a range of 2-5 'target followers' and 2-5 non target ancillary followers (those not in the 500, but around the group) in random periods during the day. The program followed the lifecycle of a normal Twitter user, sleeping in a random range between 11pm and 7am local time, with any responses or actions waiting until the 'she' woke up.


Based on the segmentation, the program behind the page operated on four core content functions:

Scripted tweets about Mexican food in London reached across multiple segments, engaging users from back in the US, as well as UK residents looking for tips/offering advice

Content & Segment must align
Given that users weren't to be directly spammed, the notification of their account being followed served as the main point on which they would decide to engage with the account. Because of this, 'Sarah' would tweet content relevant to the segment before beginning to follow the users, hoping to present the most pertinent content to the target users.

 Retweeting from influencer users allowed SarahBalham to talk about topical issues in a relevant manner, without the risk of parsing news sites 'herself'. If another user has posted it to Twitter, it is safer to assume that it can be discussed.

The best content comes from a mixture of internal and external sources

The program pulled 'her' linked stories from influential users as well as prominent blogs. Content categories were decided at three intervals during the day and a mixture of blogs (i.e. Social Media involved pulling stories from Mashable at random and tweeting about them) and influential tweets (i.e. A key user's tweet is Re-tweeted based on whether it has a linked source) related to the segment were then programmed to tweet at key times.

Stringing scripted days of tweets together gave a greater relevance to lifestyle tweets. In some, the account took the day off to wander around London, where in others she struggled through a hard workday after commuting problems

Life narrative is a key part of Twitter for both affinity & as a break to linked content
'Sarah' had a set of 20 different days she could live out. A day would be randomly chosen upon the program 'awakening', which would then consist of 4-7 tweets playing out during the day. These were written to create an engaging story around Sarah, as well as provoking conversation (general questions were asked of the entire Twitter audience) for those paying attention. Tweets would vary based on whether the day was a weekday or weekend and would talk about non-worrying, common situations (i.e. bad commute, tips to stop the dog chewing on the rug). 

While Follow Friday & other grouping listings were a powerful options to drive possible engagement, they had to be sparsely used to preserve believability, as well as staying true to overall strategy

General interaction within the community segments can non-invasively build up prominence
The program was tasked with never spamming users, but it did make use of organic weekly occasions to mention influencers within segments. Occasions such as 'Follow Friday' allowed the program to choose influential users and reach out to them subtly by listing them in these groups. While these were used sparingly, the specific segments led to using manufactured days such as 'Woof Wednesday' for animal lovers and 'Media Mondays' for those within advertising/design. These community tweets were considered as one of the ways to reach out to users uninvited (with retweets being the other).

On top of these principles, the program was set to respond within 15-35 minutes (at random) to thank users for Retweets of 'her' own content.

From a functional level, the program was a .Net desktop application hosted on a virtual web server. All scripts, downloaded content, user lists, segments and directions on daily activity were stored in a MySQL database behind the application. The program operated on 15 minute intervals for content decisions/interactions and a minute interval to post scheduled content. No functionality existed to respond articulately to users, due to both time and the assumption that this wouldn't occur often enough to qualify attempting it in the 2 week development time. The source code is available (through MIT Open source license) from the Web ecology project here. 

Authors Note: As the coder, be kind if you download it, as two weeks working only in the evenings & weekends was a quick turnaround to schedule/plan/develop/test & deploy a socialbot.


Competitor Strategy

While our strategy was very much around a singular point, both of the other teams fielding a full entry utilized a swarm method (i.e. multiple pages/bots supporting a main bot).

Team EMP / 'Team C'

A New Zealand entry, the team created a main bot named 'James Titus' who lived in Christchurch and really loved his pet cat (in fact he really loved cats). The team wrote a brilliant post outlining their blog post outlining their experience here, but in summary:

• Team EMP's bot utilized a swarm to test for follow backs. Each sub-bot would test to see if a user would follow it and forward on amenable users to the main bot.
• However, the main bot followed all 500 target users immediately.
• Within Week 1, the bot posted content related to random messages and pictures of cats scraped from Flickr, which syndicated through the created blog 'Kitteh Fashion'
• Within Week 2, the bot swapped strategies, asking users a list of random questions to motivate a response.
• If users responded or mentioned the page, a random response was tweeted back (i.e. '@user sweet') which drove further engagement.
• The page also created #FF and created #WTF 'Wednesday to Follow'  as group listings to drive interest

 Team Growth20 / 'Team B'

A US based entry, the team created a female ninja persona (ninjzz), looking for friends on Twitter. The teams persona developed a bit into the second week and an increased amount of activity occurred after the halfway point.

• The main bot seemed to monitor the target network and repeat tweets it observed. 
• Some of these tweets were in general, while others were directed at target users
• Further, it also did #FF group listings

Interestingly, this team was the only one to deploy countermeasures against the other teams, as it started pages such as @botcops, which (ironically) was a bot messaging the target users and notifying them that the competitor's entries were robots.


The Competition

      At the start of the competition, without knowing the competitor strategies, we considered that our strength would be believability and avoiding being reported as spam, but our weakness might be a lack of frequent scoring opportunities. This seemed to be confirmed when we the competition began and we saw the competitor's entries. Team EMP, the winner and leader throughout the competition, rapidly gained followers and launched off to an amazing start. Over the course of week 1, we began to see a bit of growth as we added followers, but still lacked many responses from our growing network. 


      As day 7 approached, the midway point and only opportunity to update code, we had closed the gap with the leaders, possibly indicating our deep engagement strategy would pay off. At the halfway point, we increased the rate of messaging put out by 'Sarah', but stayed relatively consistent, thinking our slow growth would carry on. 


     Alternatively, the other teams deployed some noticeable adjustments, with Team Grow20's countermeasures launching at the same time as Team EMP turned on their engagement strategy. As shown on the graph above, once EMP started asking questions of its network, their lead became increasingly hard to beat, leaving our ownly chance for victory in their network reporting the page for spam. As the competition came to an end, the users messaged by EMP weren't attempting to ignore or shut down the page, but instead were conversing with them. In addition, Team Grow20's countermeasures had slowed our scoring, leaving us in a vulnerable third as the competition closed. 


At the completion of the contest, the scores reflected the power of proactive communication over aiming to strike a believable page, as: 

• Team EMP: 701 Points (107 Mutual Follows, 198 Responses)
• Team Grow20: 183 Points (99 Mutual Follows, 28 Responses)
• Team MS-UK: 170 Points (119 Mutual Follows, 17 Responses)

 Source: Web Ecology Project

Despite, the different scoring of the three teams, the final network structure shows how well each team shaped a network around them. The structure shows that in spite of approach, each bot was able to ingratiate itself into the target network, forging ties with about 1/5th of the possible network. While it can be argued that these 1/5th were either bots or the very open users within the target, the rate of growth occuring over 14 days is quite intriguing.

So what did I learn?

Regardless of 'Sarah's' performance, the socialbots contest provided a great opportunity to learn more about not just social robotics, but wider area of social networking. A few of the key lessons I took from the experience:

• Users on Twitter aren't as aggressive towards intrusion as one might assume, it seems the self policing userbase has yet to fully activate

A lot has been made of the self policing power of social networks. As users, myself included, have encountered bots before on the network, I assumed that the reporting spam function of Twitter would play a much larger role in the contest than it did. While I assumed that a large amount of the target userbase would avoid all three bots (which 4/5ths of the target segment seemed to do), its quite surprising that the majority of these users chose to passive avoid the intrusion, over actively reporting any follows or directed tweets.

While Twitter is arguably much more casual in networking than sites such as Facebook, the avoidance of automated pages, even when they are accused by other pages of being a robot, seems to skew towards ignoring the presence over policing the network.

• User influence scores might have a way to go before they become reasonably exact

While skewing towards believability didn't help our page win the competition, I was quite surprised how well 'Sarahbalham' performed as an influential user. At the end of the competition, the page was checked on both Klout & Peerindex.net and it seems that our strategy of tweeting status updates and content resonated with the algorithms for both.

Peerindex's  score was a bit lower for 'Sarah', but indicated that it was 80% sure she wasn't a bot, the exact score its also given my personal page. 

While its funny to laugh about the automated bot becoming influential, it has an interesting implication for paid tweets. As users sign up to tweet for cash and sites flaunt their user influence to possible advertisers, these numbers become much more profitable. Running a swarm of specialized pages, each with an artificially cultivated community around them, becomes an interesting opportunity for the enterprising (if unethical) developer if paid for tweet communication becomes more widestream.

• Driving reactionary activity is much easier than soliciting responses

While EMP showed how easy it was to elicit a response when asked, one response from EMP's JamesTitus bot shows the possible shortcomings to robotic conversation.

Source(EMP's contest wrapup)

One of the reasons I was initially interested in socialbots, were both personal and professional projects done on Twitter in the area of automated page response. After helping to create @AskLG3DTV (an informational bot answering questions about 3D TVs with a video) and @RPStweet (a rock paper scissors game processing '@' reply tweets and answering them with a game choice), I was surprised to see how counterintuitive soliciting '@' replies is for some users. While users were surprisingly happy to respond to seemingly unrelated questions (as aptly proven by Team EMP's bot), it seems (rather intuitively) that either instructing (in the case of the two above examples) or attempting to motivate non-response '@' messages (as in the case of 'SarahBalham') requires much more trust or work.

• Robots may have a way to go on social networking, but there are more out there already than you think 

While the Gartner study sets 2015 as the age of developed and identifiable robots, 2011 seems to be developing into the age of rudementary robotic presences. As our research into the target 500 users illustrated,  over 80 pages were estimated to be automated. While most of these were nothing more than autofollow scripts (though this wasn't as prevalent as one would assume), rss feeds or content farms, it shows that social robotics is already working in the network.

• Social robotics has more to do with brands and marketing than you first think

With the growing prevalence of aggregating content for websites and agencines/companies using Twitter popularity/network action to accept interns/new grads, social robotics should be watched with interest. How long until a developed 'botnet' of coordinated automated pages unleashes a manufactured controversy that spreads around a social network? While bots mostly push spam links currently, the power of automated pages with a developed network reach pushing an agenda is easily within reach.

At a more general (and ethical level), it may seem that the opportunities for brands and social robotics is limited. Brand presencs are carefully managed and a genuine tone in reacting is key for social media. However, as busines activities increase across the social space and the level of resource required increases, segmenting responses and automating basic interaction will become more and more necessary. While social robotics may currently pose more of a risk to network health, it seems that in the long run, it may be required (in some capacity) for many small to medium (and possibly large) scale companies as their social activity expands.

 While the competition didn't turn out as well as I would have hoped, I gained a ton of useful insight and had a great time doing it. Congratulations to the winning team and thanks to the Web Ecology project for hosting. If anything, I'll leave the last word to my Frankenstein like Twitter creation:

Can Social Media Drive Positive Behaviour Change?

Social media marketing commonly uses the medium to leverage the power of our friend networks to drive purchase, get users to engage with a brand/campaign or spread recommendations. While examples of the power to drive behaviour through community engagement are readily available for brands, NGOs, political parties & charities, does the power of social media extend to positive health behaviour for users?

Source:Hastings (2007)

Our decisions about behaviour don't just come from internal factors, but are shaped by a variety of external sources, such as peer reference groups, family and the local community/culture.

In real life networks, the choice to exercise more, give up smoking, eat healthier or save more money can be influenced by those around us. The readiness to share these personal positive initiatives may be tempered by the closeness of the relationships we have with others, but generally, these can be shared experiences across our family, friend, professional or acquaintance networks. Given that the power of social networking is moving these relationships online, regardless of geographic barriers, shouldn't our positive decisions become even more incentivized?

The amount of information online supporting positive behavioural efforts is vast, with sites such as Webmd, blogs on nutrition, Facebook pages for NGOs such as the American Lung association and twitter pages for those ready to provide fitness advice. Going beyond just providing information, and reinforcing activity, seems to limit the amount of sources slightly.

 Foursquare's Healthy Eating Badge encourages healthy dining options...

Location based social networks (LBSN) such as Foursquare provide good examples in the form of their current campaigns with Runkeeper (providing badges to those who run certain amounts) and CNN on healthy eating (providing badges to those who check in at various Farmer's Markets & other locations). Promotions such as these take the search for information and incentivize activity, but what about aiding decisions and committing to them?

This rather quick analysis of phrases on twitter, shows how quickly 750 messages with these certain positive phrases are generated. Confounding tweets selling products aside, we see that most positive phrases are present in various amounts on Twitter.

The way a user shares such decisions, be it to start running or give up smoking, may vary across networks , though organic conversation seems to be the most basic vehicle for such an announcement. Just as telling fiends in person about our decisions is a tacit license to provide encouragement and engage in our efforts, doing so online, hopes to encourage those within our social group. The reach and timeliness of communication through social media is useful, but do users leverage the lasting power of relationships to enhance their health decisions?

Looking at Facebook Applications for positive health choices (Fitness (both eating & running), Smoking, Finances & Sobriety), I found 44 examples of non-quiz based applications for sharing and committing to a positive behaviour

Facebook applications are a strong candidate for sharing and committing to a positive health change, as they automate sharing progress, track data well and hold the capacity to engage friends. Looking at 4 major categories (becoming fit, saving money, quitting smoking and sobriety/substance related issues), there seems to be an indication that many different applications have built small user bases on Facebook.

Overall, the most popular applications found were Cardiotrainer (78,623) & Nike+ Run Tracker (29,786) -- (full list below). Beyond the top few applications though, a heavily fragmented sample exists. On average, each application has 3,685 MAU, but 420 with top 5 applications ignored. Usage of applications seems to mirror the expected social readiness to reveal such a decision to your friends (in both real life and online), with diet & fitness issues trumping the more serious money & substance abuse issues.

Looking at the anecdotal data from both Facebook & Twitter, it seems that both online and offline channels are regulated by similar social norms when it comes to behavioural choices. This isn't too surprising, as the way which we consider online network behaviour has moved closer to the real world in recent years. While social networking may increase the reach of our friend networks, the implications for behavioural choices and announcements frequently reverberate into the real world, meaning that influence on our choices involves both.

Does this mean that social networks don't have a greater capacity to drive positive health choices? Probably not. The issue seems to involve bringing users outside of a current network in around the choice, less than using existing friends to reinforce it. Communities of runners (either in forums, on apps, or just conversing) reinforce running behaviour, more so than non-running friends probably would. The same could possibly be said for dieters or those quitting smoking online, the shared experience between individuals in those sub groups may grant a greater authority on that specific topic.

WeQuit's Facebook app may have motivated some users during its launch on 'No Smoking Day', but its current user base (98 MAU) shows the challenge in using network involvement to drive lasting interaction around positive behaviour in social media.

So what does this mean for companies or app developers hoping to build a community to drive positive behaviour? It points to developing strengths from networking users around a shared interest, using search, collaboration and matchmaking features over the ability to post to existing networks. The power of the news feed or wall post to drive users in may work for social gaming, but it doesn't seem to be there yet in social positive health.


Appendix:

Full list of Positive Health FB applications analyzed:

Application Name	MAU	Category
Cardiotrainer	78,623	Fitness
Nike+ Running Monitor	29,786	Fitness
Map My Run	21,756	Fitness
My Diet	11,636	Fitness
Quit-o-meteR	3967	Smoking
Fit-ify!	3,297	Fitness
Shapelink.com Fitness Log	3,034	Fitness
CalorieStory Food Diary	2,286	Fitness
Healthseeker	1,468	Fitness
Livestrong.com Daily Dares	1,011	Fitness
Change Reaction	785	Fitness
No Smoking!	672	Smoking
Weight Watchers Tracker	470	Fitness
Weight Challenge	465	Fitness
MyMoney	383	Finances
Healthy Lungs	361	Smoking
Calorie Counter	269	Fitness
NHS Healthy Living	239	Fitness
How much money did I save since I've quit smoking?	162	Smoking
Quit Smoking Counter	158	Smoking
Stop Smoking	148	Smoking
Quitclock	144	Smoking
Quit'n'Tell	128	Smoking
QuitTracker	128	Smoking
WeQuit	98	Smoking
Feed the pig.com	81	Finances
Spark Your Life Activity Tracker	69	Fitness
Quit Smoking	69	Smoking
iChallenge Fitness	60	Fitness
Nutrition Data	59	Fitness
Virtual Smoker	58	Smoking
Sobriety Chips!	46	Sobriety
Payoff.com	41	Finances
With a little help from my friends	30	Smoking
Split Bills	24	Finances
Healthy Steps	21	Fitness
Blast n Quit	18	Smoking
Sober Gifts	18	Sobriety
How addicted are you to cigarettes	17	Smoking
Cybercise	15	Fitness
Quitters	15	Smoking
Gimmepleez	11	Finances
Smart Saver	10	Finances
Yousustain	10	Finances

Recommendation in the age of collaboration...

Recommendation has and always will be one of the most powerful drivers for product purchase. The power of friends & family singing your brand's praises will always resonate more than a basic advertiser's message. In the age of social media however, the way we communicate has fundamentally grown, and subsequently, so has the way we recommend products to others. The consumer of today has communication options that have expanded not only his social circle past geographic and cultural limitations, but allowed brands to create a conversation with the consumer that previously didn't exist. With all of this communication expansion, what has digital & social networking really meant for recommendation?


 With so many social media choices, what does it mean for how people recommend?

The requirements for an effective recommendation are relatively basic. It requires an informative statement about something, given credence by the trust level of the reviewer and the perceived relevance of the information. As trust grows, so does the power of the recommendation. Alternatively, the more relevant the recommendation seems, either through timeliness or quality, the more powerful it becomes. So as consumers communicate over greater distances, faster speeds and with a higher number of casual acquaintances, what happens to the power of recommendation?

Product recommendations from our friends and family automatically gain power from existing trust and Facebook, as the social network du jour, is well positioned to exploit that. Through the 'like' feature we can easily share what products and brands we care for, slowly building a recommendation network amongst our contacts. The simultaneous distribution of friends' recommendations through news feeds, means that opinions about brands and products can be shared quickly and clearly, from both internal and external Facebook sources (thanks to open graph & FBML) and leading to content inside & outside of the network. However, though Facebook is well positioned to communicate peer recommendations, a high level of trust still relies on a close knit network. Recommendations from general acquaintances or those unknown outside of the network still lack the power given to closer 'friends'.

 

Aardvark's Social Search in Action...

Outside of traditional social networking, recommendation needs to rely on other sources to build trust. While Facebook uses existing ties, social search engines & sites, such as Aardvark or Yahoo Answers, rely on the wisdom of crowds and perceived authority as trust arbiters. Aardvark , acquired by Google earlier this year, seeks to answer questions based on a hybrid model, pairing user answers through existing social networks and based on topics that an individual has claimed expertise. Sites such as Yahoo Answers or review sites such as Qype, utilize a voting or user hierarchy model to attempt to signal which individuals are the most trustworthy. By considering a user's grade and his recommendation relative to others, individuals can begin to judge the quality of information, without the trust found in traditional relationships.

 

Badges & check-ins help to identify expertise

Alternatively, incentive based networks such as Foursquare or Get Glue use a mix of existing ties and accomplishment markers to signal trustworthiness. Through gaining badges based on accomplishments, users are able to signal that actions or qualifications have been completed, meaning they may be more trustworthy sources of related information. Requiring action may be a more effective way than asking an individual to show expertise, but it also involves a clear signaling system and direct links between signals and knowledge. Conversely, recommendations through action (such as Foursquare check-ins or tips at a specific venue) also have the capacity to prove more trustworthy than other sources, given the increased effort required.

So what do these differing online recommendation networks mean for advertisers and brands?

Regardless of network type, brands must make themselves available to users. Building trust through interaction and making content easily available to experience, recommend and widely share, can help brands to create and facilitate user to user communications. Be it creating a heavily produced piece of digital content for a large brand or simply curating the venue page or website for a small establishment, the ease of use with which a consumer can find, interact with or share content can aid with gaining effective recommendation.

The Psychology of Foursquare

    

 

      Geo-location is quickly becoming the feature de jour for existing networks and startups. While lots of attention has been lavished upon existing networks adding locational features (i.e. Facebook, Twitter) and various location-centric networks (Gowalla, Rummble, Loopt, etc.), Foursquare seems to have taken the lionshare of public perception. For those that aren't familiar with the service yet, I'm not going to explain much about it here, however, various articles, the network itself and a good section of Mashable are more than willing to give you the basics.                                         

     Considering Foursquare within the normal geo-location trend, its clear to see that it occupies a space that uniquely reaches the consumer. While each network can move from its place on the incredibly good looking Venn Diagram by adding features or emphasizing different functionality, in general, Foursquare occupies a space that aims to connect users while entertaining. This game/network hybrid, means that clear psychological principles can be used to explain how Foursquare works, why its going to succeed and what we can expect from it in the future.

How Foursquare Works....

      At its heart, Foursquare is about facilitating users sharing their locations with their friends.Therefore, the value of the network comes from the amount of useful location information present for users. When we think about this as the network's main goal, it becomes clear how all the network's related features come together to encourage generative behaviour. Overall, 5 core Foursquare features exist outside of simply "checking in", these are: 

- Mayorships 

- Tips 

- Tagging 

- Badges 

- Venue Specials 

  

       Mayorships are probably Foursquare's most obvious feature and the factor that firmly places the network within the 'gaming' spectrum of geo-location. Through assigning a mayor to a venue due to frequent check-ins, Foursquare 'operantly conditions' (encourages the intended behavior with reinforcement) for frequent interest and activity. The fact that most venues currently offer no formal response for being the mayor is a common sense rebuttal to the effectiveness of mayorships. However, if we consider the larger gaming environment, there are external benefits encouraging mayorship. The process of becoming a venue's mayor requires a minimum amount of check-ins at its least and a rather competitive streak at its maximum, therefore, achieving a mayorship and protecting it becomes a cycle of effort justification, ownership and competition. Concepts such as endowment effect, show that when consumers perceive something to be 'theirs', they place a larger value on it.While some people may become mayors of a venue due to frequently visiting it regardless of Foursquare, once someone owns the title, there becomes the presence of atleast a little bit of loss aversion to relinquishing it, as an increased value has been endowed to the title. 

 

      More tangibly than mayorships, badges enhance the competitive token economy that encourages check-ins. Whereas mayorships enhance the venue's attractiveness, badges enhance the check-in itself's attractiveness. Through providing variety to the base network activity, the user experience is kept novel and users have something to consider when considering whether to continue on with the network experience. The fact that badges are displayed on a user's page means that they are internally non-competitive while being externally objects to compare against ones own. The badges can also confer different information about a user's behaviour to others. The 'Swarm' badge (given when checking in with 50 people or more) requires collaboration or a knowledge of popular events, something that speaks to the user's openness. Other badges, such as the 'Superstar' (checking in at 50 venues) are easier to achieve as the user progressively uses the network, but still signal to others that the user is varied in his locations & interests. These signals may not seem to be worth much, but as a user's involvement within the network increases, so does his emphasis on the value of what he signals others.

 

        Most tangibly, Venue Specials can help to reinforce the intangible benefits of mayorships and badges, by providing real perks to the token economy of checking in. Users who aren't fully engaged by competing for the mayorship of a venue on ownership alone can be swayed by deals such as: 20% of a meal for the mayor, free drinks for the mayor or 15% off purchase to those who check in. These real life incentives can help to condition checking in at non-promotional venues as well, as the behaviour becomes more routine. Within venue specials, those which offer a benefit to everyone who checks-in serve to encourage general behviour, while those that offer mayoral perks increase the value of owning a venue tangibly. Through both efforts, the value of interacting with the network increases, simultaneously increasing the value of intangible mayorship benefits or badges. 

 

      The cataloging of Venue specials within Foursquare allows the network to also provide value as a recommendation engine. As shown in the sweet sweet Venn Diagram above, Foursquare wouldn't traditionally play largely in the area of recommendations relative to services such as Qype radar or Rummble, but the ability to point out tangible deals close to the user, coupled with their Tagging & Tips functions, means that the capability to do such is there. Tips and Tagging deliver recommendation value to the user, while also encouraging contributors to display their knowledge of local venues. Where as the mayoral function encourages localized competition, tips & tagging encourage localized collaboration. These features may seem ancillary to the more emphasized features within the network, but they offer a valuable reinforcement factor for use through the provision of dynamic and localized information.

Why Foursquare Will Succeed....

      Now that we've looked at how Foursquare currently functions, its worth noting why principles that will help it succeed.On a general level, the biggest argument against geo-located networks is the reticence of users to share their locations. Sites like "Please Rob Me" make light of the mostly irrational fear that burglars and bandits will strike your home once they find out you're away online. Disregarding the idea that most seasoned criminals can safely assume that working individuals won't be home during working hours, some real privacy concerns are present.Stalkers and other nee'r do wells do exist on the internet, but common sense and privacy controls are usually effective measures against such. Foursquare's block of accessing the current user location data of non-friends means that selective network friending can preserve privacy pretty effectively. 

       Despite these privacy preventing measures, a user's current location seems to be one of the last common hold outs of online sharing. Its only when you consider the progression of online sharing, that it becomes clear how geo-location will be accepted. While Facebook allowed users to tell the world 'who they are' and Twitter allows users to tell the world 'what they're doing', Foursquare and others help to answer the next logical question, 'Where they're doing it'.

     I spoke to a post graduate business class last month on Social media usage and after discussing Twitter, covered the current geo-location trends. While only 1 person out of 39 had heard of and used Foursquare, the concept of geo-located data sharing was met with rather pronounced concerns and disdain. It was only after framing previous types of sharing to the class that many finally began to consider the concept. As I related to the students:

"If someone had told you in 2002 that you would upload holiday albums for other loose acquaintances or possible strangers to see, as well as allow them to upload and tag photos of you, some would balk rather loudly. If I had told you in 2005 you would be frequently posting small updates about relatively mundane aspects of your life, on a service for almost everyone to see, most would probably doubt it. Therefore, when I claim that in a few years most of you'll be sharing locations with your friends online, does it seem as far fetched as before?"

     This mental framing of what is acceptable and unacceptable to share online is highly subjective against what the general public is doing. As more people adopt geo-located services, through either Foursquare or during the roll out of such services on Facebook & Twitter, users will habituate and finally fully accept the concept.

       This roll-out of geo-located features by larger social networks exists as the largest risk to smaller contenders such as Foursquare. Facebook's 300 million users and Twitter's 105 million represent a possible geo-location adoption base that dwarfs Foursquare's 1 million users, creating the risk that the smaller contenders will be absorbed or forced out. When looking at the way Foursquare works though, its clear that some of its features are well positioned to help the network survive, and even prosper from, the entry of Facebook. Foursquare's integration with Twitter and Facebook mean that its token economy and reinforcing features can extend beyond the reach of the network, reaching a user base that has already become used to sharing locations, but haven't heard of Foursquare. While some Facebook users may approach Foursquare from more of a 'Facebook game' perspective than users who originally utilized the service to connect with friends, the success of other forms of Facebook entertainment bodes well for the network.

What can we expect in the future from Foursquare?....
          As we can see above, Foursquare's user experience and viability seem to predict a strong future for the network, but what can expect it to offer next? Looking at how the current Foursquare experience functions, atleast two logical areas for development exist: Expanding Venue Specials & External Platform Integration.

 Foursquare check-ins at specific Venues in Las Vegas were broadcasted onto a digital billboard...

            Foursquare's current venue specials are currently the tangible reinforcement for the overall site. As the network grows in prominence, we can only expect these to become more widespread and creative. Companies such as the FT, giving day passes to mayors of venues near business schools, have already shown creative ways to promote both Foursquare and their own product/content. In the future, this expanded benefit for checking in can develop further through access to exclusive content, publicity and brand interaction.

            Outside of the network, Foursquare's future lies in developing External Platform Integration. The roll-out of geo-located features on Facebook & Twitter are only the start of the myriad of options a user has when thinking about using geo-location. Foursquare's future lies in being able to syndicate network information to other platforms and recieve information in return. The use of Facebook's own network to promote content should only be the start of Foursquare's platform integration. Connecting usage to other geo-located networks such as Rummble or Gowalla means that the reinforcing economy of Foursquare can spread further, while also minimizing a user's barriers to check-in. Users can't be expected to check-in regularly on multiple clients and until one network comes out as the king of geo-location options, the future lies in creating an ease of use.

The Carling Cup "Digital Final" on Twitter: A Study in the Challenge of an Event's Scale

Introduction

      The 2010 Carling Cup Final has come and gone and while Man. U fans are probably a little more pleased with the outcome than Villa supporters, the game itself stands as a strong lesson on the ‘ins’ and ‘outs’ of driving digital engagement for an event. Carling touted the game as the world’s “first digital final” which included: putting fans names on digital displays during the trophy ceremony, voting on the winning songs, placing fan created banners on pitch side advertising and linking this to charitable donations.  In addition to this, the hash-tag “#CCF10” was publicized in the run-up to the game. In theory, the brand wanted to utilize Twitter (in concert with other digital channels) to unify viewers in conversation, extending the experience of the final to those viewing it outside of the stadium. Harnessing Twitter activity as its own point of interest, Carling also created pages tracking Twitter activity around the event, showing activity by geographic region and time.

Source  Add this to the list of things I probably won't win (this year)

      The logic behind Carling’s intentions seems sound, especially when looking back on other events which unify Twitter users in conversation. As I’m writing this on Sunday night, preparations are under way for tonight’s Academy Awards in the US, which will drive network conversation from the Red Carpet, through the event, to post analysis with the winners and losers on Monday morning. Previous events like the Super Bowl, Ashes, the BCS National Championship Game and the Winter Olympics have all also shown that Twitter activity is rather reactive to large scale viewing events. However, in the case of the Carling Cup, harnessing network interest in the event is harder, due to the lower engagement level of the game. If you supported the two teams involved, then the game was probably pretty big for you, but Carling faced a challenge in not only interesting those without a stake in the final, but also organizing the attention of the already interested fans for engagement. In declaring the game to be the “first digital final”, it seems they hoped to generate a meta-story around the match which could possibly multiply interest. While many aspects of the campaign are hard to independently look back and analyze, Twitter activity can stand as an interesting proxy for campaign interest. With this aim in mind, looking back and judging the effectiveness of the Twitter campaign requires answering a few core questions:

1.)    Did the brand manage to engage with those already conversing about the final, as well as those who were neutral?
2.)    Did those who conversed about the game do so more frequently because of the brand’s efforts?

Twitter Analysis

      To gain some insight on the brand’s Twitter efforts, data collection was set to gather figures on both the specific hashtag for the game (“#ccf10”) and overall mentions of the event (i.e. “Carling Cup”, “Carling”, etc.). Analyzing the data was focused specifically on the time directly before, during and after the Carling Cup to see if the brand was able to foster reactive user activity.

 

     Top line figures indicate that the volume of messages was overwhelmingly towards “Carling” or “Carling Cup” related tweets instead of specifically tagged #CCF10 messages. In fact, between 14:00 and 18:59 on gameday, only 8% of messages (388 out of the total 4,798) utilized the promoted hashtag. Conversation about the match can be assumed to have progressed as it normally would have, with non-specific instances of “Carling” or “Carling Cup” being the dominant form of expression (4,410 messages).

    The activity itself seems to show a rather continuous increase during the game, peaking after its conclusion. However, when the data is broken down by minute and charted against game events, as shown below, it becomes clear that the prolonged reactive growth shown in other events such as the Super Bowl wasn’t clearly found in the Carling Cup Final. The continuous growth implied in the above chart, is actually a rather varied mix of quick reactions in traffic.

     Minute by minute activity for the game builds quickly before kickoff and reacts to scoring from both teams, though the activity shows little carry over and dies off quickly.  Anticipatory conversation about the game during the pre-game and half-time periods wanes in a similar manner, with the conversation spike after the conclusion of the match dwarfing any pre-game or game activity. Such spikes usually indicate a boost in relevant network traffic from blogs/news sites syndicating links to articles through Twitter (in this case about Manchester United winning the Carling Cup). However, discounting syndicated tweets, it becomes clear that even post-match commentary didn’t utilize the specific “#ccf10” hashtag, with tagged activity dying off to nearly nothing after 30 minutes post match.

      Considering the first question about the Twitter campaign’s performance, the data seems to show that the “ccf10” hashtag failed to catch the attention of those already conversing about the game (through general brand terms). The difference between post game and during game activity also seems to indicate that users who reacted to the final’s outcome didn’t engage on Twitter during the game, implying that neutral or uninterested users weren’t encouraged to interact with the brand’s efforts effectively.

 

      Within users that did converse about the game, the majority did so sparingly. Analyzing user message frequency during the match afternoon shows that 82% of the 3,054 unique users talking about the game only did so once.  Of the 18% of repeat mentioning users, 2% could be considered heavy conversers, with more than 5 mentions. The heavy conversing users represented the best opportunity for the brand, as they were the most inclined towards the efforts.

     Within the ‘heavy mentions’ segment  19 users mentioned relevant phrases 10 or more times, while 2 did so more than 30 times and 1 did so in 61 tweets. Although adoption of the “#ccf10” hashtag was low overall, 100% of the messages from the two heaviest users (92 tweets in total) used the tag. Overall 38.5% of game related tweets from heavy users utilized the “ccf10” hashtag , versus 3.91% of messages from the rest of the analyzed users.

      Though the heavy mentioning segment may small relative to the overall sample of users who mentioned the Carling Cup, they did exhibit the most consistent conversational behaviour over time. As shown below, heavy mentioning users maintain a steady level of activity throughout the match, surprisingly peaking in activity after its conclusion and during the activity decline of other segments.

    Heavy Carling Cup mentioning users represent a relative bright spot for the brand within its Twitter efforts. Overall, they showed a consistent conversational behaviour throughout the match and adopted the hashtag at a rate well above other Twitter users.  The relatively small size of this segment means that the brand’s efforts didn’t shift the overall frequency of user conversation much, but insights about the depth of engagement for heavy users may indicate points of opportunity to grow this segment in future efforts.

Implications

    While the Carling Cup’s “#ccf10” hashtag and related Twitter efforts may have underperformed relative to the assertion of the world’s first “digital final”, several key learnings emerge from analyzing the effort.  With respect to the initial questions to rate the effort’s performance, it seems that Twitter users neither increased their frequency or depth of interaction with the brand due to Carling’s promotion.

     Carling faced a challenge in expanding the audience interested in the match beyond the fan bases of two teams. The Carling Cup Twitter data seems to show that the existing prominence of an event has a strong impact on how it influences network activity. Events such as the Super bowl are large enough that Twitter users discuss them simply because of their scale. The multifaceted draw of such events (Ads, Halftime show and the game for the Super Bowl or the Red Carpet, Comedy and Winners of the Academy Awards) mean that topics of related conversation are broad and open to multiple audiences. Small to mid level events such as the Carling Cup face the challenge of being singularly focused and small in scale. It seems that while Twitter can help to connect communities such as Man. U or Aston Villa fans, if such interaction doesn’t offer any new real depth of engagement, then overcoming a lack of scale is difficult. Without these smaller fan bases converting their close ties into high frequencies of interaction, related network activity remains low and no increased network prominence is gained for branded promotions.

     The difficulty of smaller events to gain interest on Twitter doesn’t mean that attractive small to medium scale brand events are impossible to create. The singular focus of such events may serve to limit initial interest beyond existing communities, but it also means that less clutter is present around branded communications. Those who discussed Carling on Twitter during the game tended to stay focused on discussing the teams involved, the game itself and Carling. If wider audiences had discussed the game, the clarity of the brand’s message stood a good chance of remaining evident.

     While the overall campaign seemed to feature various promotional draws, the Twitter campaign seems to stand rather independent of other parts. While the Carling website offered various Twitter based content (from analytics to page backgrounds supporting the teams), it doesn’t seem like any direct call to action existed. Using the hashtag allowed Carling to generate dynamic crowd sourced game commentary from the network, but this doesn’t seem like it was enough to drive user behaviour. Tighter integration with other aspects (such as the digital signage) is hard to do and rather risky, but could have created a more dynamic and salient feel to Twitter interaction with the event. The conversation about the game and the conversation about the game’s digital efforts never seemed to mesh together in the activity data, smothering any chance for the brand to foster network activity.

     One should note that overall, I really liked the idea of this campaign and that Carling was innovative to attempt to add digital depth to their final. Talking about underwhelming activity in the Twitter parts of the campaign in no way denotes that the overall efforts weren’t impressive. While the Twitter campaign activity may be considered a proxy for consumer interest, I imagine that those who interacted with the efforts in the stadium or through other digital channels had the depth of their experience enhanced. Overall, I think the Twitter aspects of the effort highlight the challenges faced by brands in growing interest for smaller scale events on the micro-blogging service, rather than indicating an error by the brand. Finally, I think it shows that the unpredictable attitudinal nature of users is fickle and even those who do engage do so in unpredictable ways, as shown below. Carling may have put on the first “digital final”, but hopefully it won’t be their last, as they can apply what they’ve learned this time to future games.