The Impact of Social Models
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As Richard Farson’s truism “no one smokes in church no matter how addicted” points out, context informs almost everything that happens in an environment. Online social experiences are no exception. How a product’s social model is set up can impact not only who contributes, but how much, and why. From permission-based subscriptions to one-click follows, Luke will discuss the attributes and implications of several popular social models by looking at data and behavior in the Web’s most popular social applications.
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The Impact of Social Models
- 1. THE IMPACT OF SOCIAL MODELS LUKE WROBLEWSKI IDEA2009 TORONTO, CANADA 1
- 3. 3 Flickr photo by by laurat
- 4. “Behavior is a function of social context.” 4 Flickr photo by Renata Deim
- 5. “Nobody smokes in church –no matter how addicted.” –Richard Farson 5 Flickr photo by stevecadman
- 6. The Power of Context Small features of context can produce huge differences in behavior. So what is the impact of specific social models on people’s online behavior? 6
- 7. 7 © Idiots of Ants
- 8. Social Relationships (modeled) Online No Relationship Community Groups • Public, Semi-public, Private Symmetrical/2-way Asymmetrical/1-way • Permissioned, Blocked 8
- 9. No Relationship 9
- 10. THE WEB 10
- 11. No Relationship (but both on the Web) Location • GPS: 10m (outdoors only) • WiFi: 50m (now with geolocation api) • Cell tower: 100-400m (triangulated) Technology • Device: palm, lap, desk, wall • Operating System • Browser: capabilities available • Settings Browser History 11
- 12. THE WEB A COMMUNITY 12
- 13. A Community 13
- 14. A Community (1 to many relationship) • Are users on the same site • Can interact with each other • Messaging and/or collaboration • Leave visible traces of behavior • Can manage identity (profile) 14
- 15. A Community (1 to many relationship) • Yahoo! Answers user base interacts with each other as one large community • In a graph of 700k askers and 550K answerers… • Found one connected component of 1.2M nodes • And 1.6K small components of 2-3 nodes • This indicates most users are connected through some questions & answers • Holds even for geographic categories (Local business) Source: Questioning Yahoo! Answers, 2007 ACM 15
- 16. A Community (1 to many relationship) • 1.8% of all users write more than 70% of all Wikipedia articles • .003% of digg’s users are responsible for 56% of the stories on the site’s home page • .o64% creator to consumer • 1% Creators ration on YouTube • 10% Curators • 90% of eBay’s users are not • 100% Consumers being monetized according to estimates Source: http://www.lukew.com/ff/entry.asp?448 16
- 17. THE WEB A COMMUNITY A GROUP 17
- 18. A Group 18
- 19. A Group (1 to many relationship) • A set of people within a community • Clearly defined relationship • Topic, relationship, or collaboration based • Can be long-lived or temporary • Convenient communication: one to many 19
- 20. A Group (1 to many relationship) Relationship-based • Support existing offline & personal relationships • Communication is more personal in nature • Coordinating events, meetings, etc. • Lighter posting volume but more interest Topical • Provides information about specific topics • Allows members to stay aware/up to date • Archives information • Higher posting volume, but high noise to signal ratio 20
- 21. A Group (1 to many relationship) Listed & Unlisted • Listed: can be found through search or browse • Unlisted: discovered through an invitation or posted URL Open, Restricted, or Closed • Open: non- members may read and post messages • Restricted: only members can view/post, but membership is automatically granted • Closed: only members can view/post, moderator must approve membership Source: Preferential Behavior in Online Groups, 2007 ACM 21
- 22. A Group (1 to many relationship) Public • Anyone can view & post Semi-public • Anyone can join, only members can view & post Private • Only members can join & post Source: Preferential Behavior in Online Groups, 2007 ACM 22
- 23. THE WEB A COMMUNITY A GROUP A PERSONAL RELATIONSHIP 23
- 24. A Personal Relationship 24
- 25. Symmetrical/2-way/connection • Confirmed & controlled by both sides • Sharing enabled as part of handshake • When either side severs, relationship is gone • Often no notification when severed • Can be used to mirror real world relationships 25
- 26. Symmetrical/2-way/connection • 10% of users account for 30% of production • 12% of Facebook users update their status daily • 40% of Facebook users have updated status in past 7 days • 1.89% of page views are contribution (photos, content, videos, events) Source: Facebook statistics: http://www.facebook.com/press/info.php?statistics Comscore Page View data July, 2009 26 Facebook app data for friend updates (300+ users)
- 27. Asymetrical/1-way/fan/follow • Declared by one-side • Easy to establish (no reciprocal action required) • Can maintain privacy control through permissions • Supports multiple relationship structures • Often more public than 2-way relationships 27
- 28. Asymetrical/1-way/fan/follow 1-way follow • A follows B (one direction) • B follows A (other direction) • A follows B, B follows A (mutual) • A nor B follow each other Blocking • A blocks B (can’t follow) Private accounts • A has private account, allows B (permissioned follow) 28
- 29. Asymetrical/1-way/fan/follow Categorized 1-way • Flickr example • A can follow B • A can optionally mark B as friend, family, or both • Friend and family categories enable permissions (restricted photo sharing) • B does not have to reciprocate relationship to see permissioned content (unlike a 2-way) 29
- 30. Asymetrical/1-way/fan/follow Permissioned 1-way • Y! Messenger example • A sends B a request • B accepts, then A has to accept • Once permission is given, there is no way to revoke it (unlike 2-way) • Can only appear offline or ignore/ block them • The two 1-way relationships are independent • But may be perceived by users as 2- way 30
- 31. Asymetrical/1-way/fan/follow Is Following Has a Private Has Blocked Marked as Marked as Account Family Friend User A Yes Yes Yes Yes User B Yes Yes User C Yes User D Yes 31
- 32. Asymetrical/1-way/fan/follow Twitter Users • 10% of users account for 90% of production • 50% have not updated status in past 7 days • 55% are not following anyone • 52% have no followers Source: Inside Twitter study, Sysomos June 2009 32 State of the Twittersphere, Hubspot, June 2009
- 33. Social Relationships (modeled) Online No Relationship Community Groups • Public, Semi-public, Private Symmetrical/2-way Asymmetrical/1-way • Permissioned, Blocked 33
- 34. Do Social Models Affect Contribution? 2-way vs. 1-way • 12% of all Facebook users update their status at least once a day (2-way model) • 40.5% of Facebook users have updated status in past 7 days (2-way model) • 14.7% of all Twitter users post an update at least once a day (1-way model) • 49.6% of all Twitter users posted an update in past 7 days (1-way model) Source: Facebook statistics: http://www.facebook.com/press/info.php?statistics Inside Twitter study, Sysomos June 2009 34 Facebook app data for friend updates (300+ users)
- 35. Do Social Models Affect Contribution? 2-way vs. 1-way • 30% of production comes from 10% of users on a typical (2-way model) social network • 90% of production comes from 10% of users on Twitter (1-way model) Source: Harvard Business Review research by Bill Heil and Mikolaj Piskorski, June 2009 35
- 36. Do Social Models Affect Contribution? Community vs. 2-way • .0032% page views vs. video uploads on YouTube worldwide • 1.89% page views vs. content contribution (not counting status updates & comments) on Facebook worldwide • 58,000% more contribution? Source: Facebook & YouTube site statics and ComScore PVs August 2009 36
- 37. Do Social Models Affect Contribution? 100% % OF CONTRIBUTION 50% 0% 0% 50% 100% % OF USERS • Top 10% of Twitter users account for 90% of content • Top 15% or Wikipedia users account for 90% of content • Top 30% of typical social network users users account for 90% of content Source: Harvard Business Review research by Bill Heil and Mikolaj Piskorski, June 2009 37
- 38. Do Social Models Affect Contribution? Yes, but there’s more to it… 1. Relationship limits exist in all models 2. Tight knit circles flourish in all models 3. Communication activity can reveal tight knit circles that matter 4. The more attention you get, the more you contribute –to a point 5. 1-way relationships are optimized for broad reach 6. But real relationships drive more production 7. Creation can be encouraged in other ways 38
- 39. Relationship limits exist • 120 average number of friends per user on Facebook in Feb 2009 • 144 average on Facebook 2004- March 2006 • 92.4% of people on Twitter follow less than 100 people • 148 size of stable social networks the human brain can manage at its current size (Robin Dunbar) Source: Inside Twitter study, Sysomos June 2009 Primates on Facebook, Economist, Feb 2009 39 Source: Rhythms of social interaction, HP Labs
- 40. Relationship limits exist 10M NUMBER OF USERS 1k 0 1k 5k 10k 0 10 100 1k 10k NUMBER FOLLOWING NUMBER FRIENDS • 100 people or less • 120 avg. number followed by 92.4% of users of friends per user Source: Rhythms of social interaction, HP Labs 40 State of the Twittersphere, Hubspot, June 2009
- 41. Tight knit circles flourish • 2-3 average “clique” size on Y! Answers before social relationships were added • 4 average people a man messages on Facebook • 6 average people a woman messages on Facebook • 7 average friends’s walls a man on Facebook posts to • 10 average friends’s walls a woman on Facebook posts to • 13 average “friends” for 92% of Twitter users Source: Questioning Yahoo! Answers, 2007 ACM Source: Social Networks that Matter: Twitter under the Microscope, January 2009 41 Primates on Facebook, Economist, Feb 2009
- 42. Tight knit circles flourish 120 RECIPORCAL FRIENDS 60 0 0 60 120 NUMBER OF FRIENDS • 90% of a user’s “friends” reciprocate attention by being friends as well. Source: Social Networks that Matter: Twitter under the Microscope, January 2009 42
- 43. Communication reveals relationships • 90% reciprocal relationships on Twitter when two sides exchanged at least two “@” messages • 15.1% of Facebook friends exchange direct messages • 95% accuracy for detecting real friends using mobile call logs & location Source: Social Networks that Matter: Twitter under the Microscope, January 2009 BBC News: Mobile data show friend networks 43 Rhythms of social interaction, HP Labs
- 45. Communication reveals relationships • 65 million active Facebook mobile users • 12 mobile platforms with Facebook applications • 23 minutes of use per day spent by Facebook mobile users Source: Facebook blog, Sept 2009 ComScore Mobile Metrix, July 2009 45
- 46. More attention, more contribution • 3 to 6 change in average daily Twitter updates when get 1,000 followers • 10 average daily Twitter updates with 1,750 followers • 480% increase in questions answered on Y! Answers when relationships increased by 20 Source: Inside Twitter study, Sysomos June 2009 46
- 47. Less attention, less contribution 3400 AVERAGE VIEWS 2400 1400 0 50 100 ITH LAST VIDEO UPLOADED • As contributors approach their last video upload, the average previous views exhibited a marked linear decrease Source: Crowdsourcing, Attention, and Productivity September 2008 47
- 48. More contribution to a point… 1200 NUMBER POSTS 600 0 0 500 1000 NUMBER OF FOLLOWERS • Though number of posts increases as followers increase, it eventually saturates Source: Social Networks that Matter: Twitter under the Microscope, January 2009 48
- 49. 1-way relationships optimize for reach • Like topic-based groups, 1-way allows people to stay connected with interests • Only a lightweight “subscribe” action is required • Having followers encourages contribution (see who likes you!) & builds audience • Though a broadcast format, still enables conversation • Allows for asymmetrical relationships (5,000 followers, 150 following) • Public updates allow information & messages to “amplify” • Better aligned with celebrities, brands, companies: quantity indicates popularity, or authority 49
- 50. 1-way relationships optimize for reach • 90% of a Facebook page’s fans can be a part of a single connected group • 15% of all fans arrived independently and started their own chain • These patterns hold for pages with few thousand fans and those with more than 50,000 Source: Gesundheit! Modeling Contagion through Facebook News Feed, May 2009 50
- 51. Real relationships drive production • 0-1-2 effect: probability of joining an activity when two friends have done it is significantly more than twice the probability of doing it when only one has done so Source: Convergence of Social & Technical networks, Communications of the ACM Nov 2008 51
- 52. Real relationships drive production 3200 NUMBER OF POSTS 1600 0 0 175 250 NUMBER OF FRIENDS • Total number of posts increases with friends without saturating up to 3,200 posts Source: Social Networks that Matter: Twitter under the Microscope, January 2009 52
- 53. Tight knit circles flourish 3200 FRIENDS NUMBER OF POSTS 1600 FOLLOWERS 0 0 500 1000 NUMBER OF RELATIONSHIPS • Contribution as friends increase does not saturate like it does for followers Source: Social Networks that Matter: Twitter under the Microscope, January 2009 53
- 54. It’s not just relationships… 54
- 55. It’s not just relationships… “Most user-created content is crappy. As we create better tools, we’ll increase the value of the output of those tools.” -Will Wright 55
- 56. The Impact of Social Models 1. Context shapes behavior 2. How we model social relationships in software creates context 1. No relationship, Communities, Groups, 2-way & 1-way personal relationships 3. Social models do affect contribution 4. But core behaviors exist across all models 1. Attention limits 2. Tight knit circles 3. Activity signals 4. Contribution drivers 5. Social relationships alone do not drive contribution 56
Editor's Notes
- Thanks for having me. Appreciate you all getting up early as I saw there was some pub crawling going on last night…
- In 1992, there were 2,154 murders in New York City and 626,182 serious crimes. Within 5 years, those numbers shifted dramatically. murders dropped 63% to 770 and total crimes 50% dropped to 355,883 . Those of you who have read Malcom Gladwells’ Tipping Point may recall why…
- At the time, George Kelling was tasked with cleaning up crime in NYC He b elieved that crime is the inevitable result of disorder. So he brought in a subway director called David Gunn to oversee a multi-billion dollar rebuilding of subway system. Gunn especially cared about graffiti. –”the graffiti was the symbolic collapse of the system” “ without winning the battle against graffiti, reforms and physical changes were not going to be possible”
- Gunn let no car go back on the track with any graffiti on it. Once a car was “reclaimed” it should never be allowed to be vandalized again –”we were religious about it” His belief in the Broken Windows theory – visible disorder grew crime- helped him drive down crime tremendously over a 5 year period. Gladwell generalized this theory as: The power of context: people’s actions are products of the conditions and circumstances of times & places where they occur. People are prompted to act based on their perception of the world around them The power of context says that what really matters is the little things.
- Put another way, the power of context can be neatly summarized in a quote by Richard Farson.
- So if People are prompted to act based on their perception of the world around them What is the impact of the online social models we put in place on people’s behaviors? How do people act based on the relationships they can set up with others? Let’s take a look..
- So there’s our context. This is how we are shaping human behavior through the way we model social relationships in software Ha ha Sorry about the poor quality of video.
- To get a deeper answer on how the way we model social relationships in online software impacts behavior. I went to FB, LinkedIn, Answers, Groups, Flickr, and asked the smart people there. Everyone’s response was “wow that would be really interesting to know” Which was a bit of a shocker. Lots of interest but not a lot of analysis being done. So I originally thought this paper would write itself based on the peopIe was going to talk to. Then I moved on to research –got some stuff there Caveat: going to show numbers, not all equal but hopefully they help shed some light on the situation. There’s a lot of data in here, so I am including cute animals to keep you awake! Since context matters need to look at “flat sites” Twitter -1way Facebook 2-way Y! Groups –groups
- Perhaps the easiest relationship to model in software is no relationship.
- But online it is not entirely true that people have no relationship. They are both users of the Web. So that might tell us something
- Specifically… We can infer some kind of relationship by detecting their location Which is getting easier all the time. Devices with GPS, WiFi, etc. We could also align them based on their technology stack: browser, operating system, settings. Or even browsing history These are not declared relationships But they can still be interesting from a social perspective. …
- Where we begin to see declared social relationships is in communities
- So let’s drill down into a community…
- Two people in a community are both members of the same site & can interact For the sake of simplicity, not including sites where members cannot message each other or do not leave visible traces of behavior Need communication & presence to count as a community site Increasingly sites are being set up this way Just Any site does not count for social relationships It needs to be a community –what does that mean?
- You might ask –is a community a real thing? Or just some fancy marketing Web 2.0 term Just because people are participating on a site are they doing it as a cohesive whole? Or as individuals? Or as groups? Answers study done at Stanford tackled that question (among others) Cohesion of the user base even in diverse category sets It is possible to reach other users following an alternating sequence of Q &A pairs 996,887 users interacted through the questions, answers, and votes in our data set
- So we know a community is an actual entity and is connected as such. We also know a little bit about how a community’s participation works There’s a model from my friends at Yahoo! on how communities are structured Yahoo! Groups Example Creators 1% of the user population might start a group (or a thread within a group) Curators 10% of the user population might participate actively, and actually author content whether starting a thread or responding to a thread-in-progress Consumers 100% of the user population benefits from the activities of the above groups YouTube: 640,000 videos uploaded a day. 1 billion videos viewed per day. That’s 0.064% creator/consumer ratio
- Now within a site that allows people to interact with each other can exists groups Communities can have groups and personal relationships in them but they do not need to. Answers is an example – relationships were added later.
- Let’s zoom in on a group online
- How do we define a group? Set of people within a community site (again defining community as a site with presence & communication so not too formal) Groups were some of the earliest communities on the Web. What keeps people engaged in groups Abundant and fresh content High quality of other group members Especially if real life friends are also members Alerts of new group activity Games Especially those that require participation of other group members Temporary groups of Facebook game players for example
- Groups span a range from being relationship focused to topic/information focused Groups with a high # of messages are more relationship (people) focused Groups with a high # of members and page views are topical/information focused Very few members of large informational (member / page view) groups know each other offline Informational groups with the largest membership tend to have more relevant content Groups with the most messages (relationship focused) have the broadest level of participation. At least ½ the group’s members participate regularly in over 70% of these (relationship focused) groups. Overall (across all groups), participation is limited to a select few members That should not be a surprise to any of you that have ever been on a mailing list.
- Another important aspect of groups is how they are set up and operate Groups are also defined by their findability & moderation/membership rules Groups not listed in the directory which employ message moderation produce significantly more relevant content In addition to these key factors, # of members is a predictor of good content Privacy is a key determining factor for relationship groups use
- These can be simplified to the following three categories Public groups tend to suffer a range of abusive behavior ranging from spam to trolling to personal attacks to self-aggrandizing users who post “too often”. Effective moderation is the key to keeping this abusive behavior in check Private groups are definitely the best behaved; members generally know each other in real life which tends to keep people well behaved.
- Communities can have explicit personal relationships in them as well Relationships that are modeled in software –not that exist offline only. but they do not need to
- As we saw the data from Y! answers earlier Personal relationships encourage conversations and sharing This grows the site/community In Y! Answers, the number of connections drove more answers. With 1 connection, answered 10 questions, with 22, answered 58. -480% increase in median number of answers
- Perhaps the most common form of personal relationships in social networks and private groups is the two way connection With a two way relationship, you get everything at once –then need to tune to adjust. Problem is few people tune… Challenge of Facebook’s new design: introduced a different consumption model For getting updates –not what you signed up forw/ 2-way connection Personal relationships not attention management Don’t have to follow back if you are not interested (FB has added this on invite flow now)
- Some interesting stats about 2-way connections And contribution/behavior We’ll look at this info in more depth soon Again using FB cause it is pretty flat and context free
- Another form of personal relationships is the 1-way Found in Twitter, Flickr, and Yahoo! Messenger among other places Stay connected with interests Asymmetrical = can have audience bigger than following count Privacy/stalking –small % (who have turned off) Increases risk of being targeted for spam/trolls –anyone with a twitter account should know this
- 1-way follows though they seem simple on the surface can get quite complex. Let’s see how Don’t have to follow back if you are not interested (FB has added this on invite flow now)
- But there’s more to it than that 1-way follows can be categorized and associated with content /sharing permissions Like they are on Flickr You can have only one side permissioned –not both
- They can also be permissions-based by default like on Y! Messenger This is kind of like being private by defualt
- This enables lots of different social relationships Which means it is powerful but also complex. Yet -in the real world relationships are complex Unaided most participants categorized their connections according to the context in which they know them Friends from school Co-workers Family Etc. Permissions connection model was confusing to most users, and even after explanation, many still did not understand it
- Hopefully, you all are still with me on the 1-way model. Some other numbers 32% of all content form most actives users is machine generate. 24% of all content comes from bots
- So those are the social models that I pulled data on. Hopefully we are on the same page about how I am defining them Going to show numbers, not all equal but hopefully they help shed some light on the situation. There’s a lot of data in here, so I am including cute animals to keep you awake! Since context matters need to look at “flat sites” Twitter -1way Facebook 2-way Y! Groups –groups
- Despite the differences in social models, Some contribution behaviors remain pretty similar. 1-way seems to lead a bit in production of status updates but… its probably a result Of the perceived purpose of Twitter and it is not by much.
- Where things start to differ though is where production comes from How does contribution differ? (1 to all contribution)
- And even bigger contribution differences between communities and 1-ways FB: 112.04B page views worldwide, with (1b photos, 1b content, 10m videos, 2.5m events), you get: 1.89% of page views are contribution page views. That’s 58,000% bigger than YouTube’s! Looking at Youtube PVs, that is .0032% (59.7B pageviews a month worldwide). Not counting status (just UGC)
- Graphing this out, you can see the difference between contribution in 1-way, community, and 2-way relationships I’m working on getting data to model groups here as well So there are in fact differences in contribution between social models
- Then to answer my original question, Contribution seems to change as relationships get tighter But there is more to it. Specifically –there’s some interesting findings that emerge when comparing behavior within these different social models.
- Point 1: Relationship limits exist in all models Facebook today, when was college tool, twitter, and dunbar There is a cap on how much people can tune in to. An attention cap if you will. Before relationships are not social but game-like or…? Several years ago, therefore, Robin Dunbar, an anthropologist who now works at Oxford University, concluded that the cognitive power of the brain limits the size of the social network that an individual of any given species can develop. Extrapolating from the brain sizes and social networks of apes, Dr Dunbar suggested that the size of the human brain allows stable networks of about 148. Rounded to 150, this has become famous as “the Dunbar number”.
- You can see this limit graphed out The graph on the right expands the front part of the data 1 10 100, etc. so the two look a bit different but are essentially saying the same thing
- Point number two: Tight knit circles flourish in all models In each model, there’s a few people that get the bulk of people’s attention (from a contribution standpoint) In Yahoo! answers, the vast majority of users are connected across a single large community while a very small fraction belongs to cliques of 2-3. (first 10 months study). .13% are small cliques (this was before the introduction of peeps) Scary but the number of users that matter for real production (conversation & sharing) –is around 10. What are online social systems doing about that?
- These tight knit circles are mutual. Ina study that defined “friends” as sending two @ messages to someone, On average, 90 percent of a user’s friends reciprocate attention by being friends of the user as well. This shows that reciprocity of attention plays an important role in defining the “hidden network.” This is interesting because it shows how a 2-way network can exist in a1-way network . If you just apply a layer of permssioning –it can be very powerful
- Point 3: Communication activity can reveal tight knit circles that matter You can discover and then utilize these tight knit circles by looking at messaging behavior between people Across both 2-way and 1-way models –people who message each other are likely to be connections Really interesting to see the intersection of messaging & mobile use
- Which why mobile phone OS is a growing area for social. People are all over it. And it makes sense Any of you seen the HTC Sense UI on the new HTC Android phones? “ Make it Mine” –watch the video
- Another exmaple is the new Motorola Motoblur UI Which integrates messaging and social behavior into one UI These integrations also make sense because engagement on mobile is high! Apple's iPhone and Nokia's N97 and 5800. Facebook applications from INQ, HTC, LG Electronics, Motorola, Palm, RIM, Samsung and Sony Ericsson. There are also Facebook applications for the T-Mobile Sidekick and phones powered by Microsoft's Windows Mobile.
- Point 4: The more attention you get, the more you contribute –to a point Having followers, encourages contribution (Twitter numbers)
- And conversely, less attention means less production. As modeled here in community site behaviors YoutTube As can be seen, as contributors approach their last video upload at the origin, the average number of previous views of their videos exhibited a marked linear decrease. This confirms our conjecture that decreasing attention leads to a lack of productivity, in this case to the point of making contributors stop uploading any videos. Our dataset contained 9,896,816 videos submitted by 579,471 users by April 30, 2008
- And the number of total posts eventually saturates as a function of the number of followers. On Twitter
- Point 5: 1-way relationships are optimized for broad reach Getting the attention you need to drive contribution is better achieved through 1-way follow relationships Because of lightwieght follow actions, incentives of follower counts Asymmetrical support Help listeners/followers to “manage attention” not “manage relationships” –bokardo Don’t have to follow back if you are not interested (FB has added this on invite flow now) More flexible relationship options: follow each other, 1 person follows, none Do not know if people are listening back –shouting into a void When content is more important than relationships Subscription to content & activities
- What’s interesting is the connectedness of these 1-way follows This sequence of connections is like a domino effect created by News Feed, enabling fan actions to evolve into ultra-long chains. What’s even more striking is how a flurry of fanning or short chains, all started by many people acting independently, often merges together into one gigantic group of friends and acquaintances. Typically, each of these large, close-knit communities contains thousands of separate starting points— individuals who independently decide to fan a particular Page. No single person is accountable for the popularity of a Page; it appears that the most explosively popular Pages catch on as closely connected groups of like-minded people contact one another. Individual influencers aren’t nearly as crucial to a Page’s success: Pages grow if people are easily engaged by the content, not because of the actions of a couple trendsetters. That may also be true in other areas--such as the ways that new Platform Applications catch on--or a different dynamic might apply. We’ll be analyzing word-of-mouth (or should we say, word-of-mouse) referrals to find out.
- Point 6: though 1-way relationships a re good for broadcasting and encouraging contribution Real relationships drive content creation even more 0-1-2 says if your friends are doing it –you will do it
- We saw a slide ago how the number of posts began to increase as followers increased But when compared to friends As defined by messaging behavior That saturation does not happen.
- Compare the two graphs
- Point 7: Creation can be encouraged in other ways It is not just relationships that drive production. Don’t want to leave you with that impression Tools matter too. But most importantly is I HARD on myspace to do good design. Its is HARD on Craigs list to do design.
- Will Wright at SXSW commented on this In spore, the tools for creating great content are very robust. Some people like henry jenkins think the content from open space peer production systems is golden. Others believe fan fiction was relagated to basements for a reason.
- Summary! Interesting to see how underneath these different elements similar structures emerge as: Attention limits, tight knit circles, activity circles, contribution drivers Relationship limits exist in all models Tight knit circles flourish in all models Communication activity can reveal tight knit circles that matter The more attention you get, the more you contribute –to a point 1-way relationships are optimized for broad reach But real relationships drive more production Creation can be encouraged in other ways
- Ways to contact me. Thanks for your time.