The role of gratitude in creating communities

Recently, I listened to an interesting podcast interview about gratitude. Something that really resonated with me in terms of community-building was a discussion of how gratitude creates a sense of belonging – a key element in communities. The interviewee mentioned the classic example of a Buddhist giving thanks for an item of food and realising how many different people went into bringing that item from the field to the kitchen table. In a global, interconnected world none of us can say we’re truly independent – we belong to a complex network of many other people.

But aside from making you realise that you didn’t achieve or obtain a certain thing entirely on your own and that you’re part of a wider ecosystem, I think there are two other points relevant to community-building:

  1. Gratitude as a concept for me had gotten somewhat lost in the word “sharing” which perhaps gets overused when talking about online exchanges. Yet if we unpack the action of healthy sharing within a community, it’s both the offering of a skill, piece of information etc and the giving of thanks for it. And if you’re thanked for your offering, you’re more likely to feel appreciated and you’ll probably contribute again in the future. Gratitude also signals that your behaviour was appropriate for the community – which helps to clarify belonging.
  2. The exchange between the donor and the recipient builds a specific, two-way interaction between two community members. i.e. giving thanks makes the interaction bi-directional, and therefore stronger than if it had been a uni-directional gift-giving. You feel like you belong because you have an actual reciprocated tie to another person.

How does this translate into practical actions to take? It could be that you always acknowledge contributions to your community/group – even if that’s just a thank you for leaving a comment. It could be publicly thanking by name members who have contributed content of value in the past week. Or it could be specifically “rewarding” members who contribute more than most with badges, a Q&A with them about their role, or some other way of deliberately increasing their social capital.

What role(s) does gratitude play in the communities that you belong to?

7 fundamentals of design – and how they apply to online spaces

In “It’s complicated – the social lives of networked teens”, Danah Boyd considers the four affordances of online networks (the first of which I’ve discussed here). An affordance is a term that originates from the field of design, and recently I’ve been reading “The Design of Everyday Things” by Donald Norman, where he starts by explaining 7 fundamental design ideas. Here I consider how each can be applied to online spaces.

i) Discoverability

Usually when the word discoverability is used about the Internet it refers to how easy content is to find – either due to SEO or the intrinsic properties of the site hosting the content. Discoverability when referring to design is something different – it’s whether it’s possible to figure out how to use an object by interacting with it.

Online, many of the sites that we interact with use at least some features in common that guide us in how to use them. These might include navigation bars in the top and/or the sidebar of pages, dropdown menus and possibly even hover text with additional explanatory information. However, as we all know from experience, some sites are better than others at indicating how exactly to move between desired pages, or where particular features are located. For example, Facebook has been criticised many times over the years for not making its privacy settings easier to find and apply.

ii) Affordances

The affordance of an object is the possible use for that object once a user interacts with it. The key to the definition is that interaction is needed – an affordance depends on the qualities of the object and the capabilities of the person using it. A website might use a specific type of video player, but if users with certain browsers or smartphones cannot view the videos then the affordance of watching video is not available to them.

In terms of content shared in online spaces, Boyd lists persistence, visibility, searchability and spreadability as the four key affordances. However, if we go by the definition above, others such as the ability to participate in a conversation via a comment thread might also be included.

DOET

Drinking coffee, reading a book – but not with coffee poured from the pot on the book cover! Image credit: author’s own.

 

iii)  Signifiers

How do we discover what affordances are possible? Signifiers act as signs to indicate affordances. Online these could be a call-to-action button within an email, descriptive images in a carousel that invite you to read a particular news story, or a coloured section of a page indicating where you should look. They could also be a particular icon – such as the Facebook “thumbs-up” showing when you too can “like” a piece of content. Or they could be social sharing buttons, encouraging the reader to spread the content more widely.

Another good example is the “slide to unlock” text on the iPhone lock screen. Not only does it tell you what you need to do to operate the device, there is also an arrow indicating which direction you need to swipe in, and the text illuminates repeatedly letter by letter in that direction.

Signifiers don’t necessarily have to be deliberate to give away information about their environment. For example, as Norman points out, a bookmark doesn’t just indicate where in a book to resume reading; it also indicates how far through the book you are. Similarly, online, the number of likes or votes that an item of content has received may indicate how enjoyable the content is likely to be, but can also act as a signifier of how likely the content is to have been shared – and therefore how often it might have been seen.

iv) Mapping

Mapping is used to indicate the relationship between two sets of things – such as switches and the corresponding lights that they control. It’s often used when designing controls and displays.

Natural mapping is particularly useful in online design because it takes advantage of actions that we’re already familiar with – such as swiping through something to delete it and swiping down to refresh a smartphone page and pull more content into the window.

Another example of mapping is social network timelines, which typically show older content at the bottom and more recent activity at the top. One of the complaints about Facebook tweaking newsfeed settings is not just that it changes which content appears in your newsfeed, but that Facebook’s algorithm violates their instinctive mapping. The content no longer follows a strictly chronological sequence – older, more popular content may sit above an update made 5 minutes ago by a different friend.

v) Constraints

Providing constrains on what is possible with an object can help to clarify how to use it and what it’s for. For example, the handles on scissors constrain the user to only be able to fit one digit into the top hole, while she can fit all the remaining fingers into the larger, lower hole.

Tweets are an obvious example of an online constraint – only 140 characters are permitted for each tweet – and this is clearly signified by a counter that displays how many characters are still available to the user. A tweet therefore affords the user the ability to share small pieces or text including links to other media.

vi) Feedback

Feedback is important to help us understand how to interact with objects and what effect our actions have had on the object or system. Too much or too noisy feedback, however, can be distracting and impair the ability to use the item smoothly. Online feedback might be in the form of additional information appearing on a page. Or it could be a pop up window indicating that an action has successfully occurred or that there has been an error that the user needs to know about.

One of the challenges with feedback online is that it can also be used to encourage engagement and that can make for a frequently interrupted experience. For example, a popup window may not only be used to tell you about something that you did, it might also be used to point you towards content that may be of interest, such as a promotion, a survey, or even an online chat bot to help you with your purchase.

Another interesting issue about feedback is that new users typically need more than experienced ones do – and what is initially helpful can quickly become annoying as a user becomes more comfortable with a product. A human teacher will tailor their feedback to the needs of a learner, but we need to make sites sensitive to how much a user already knows.

vii) Conceptual model

A conceptual model is how the user understands a system to work and is important for giving the user a sense of control (think how frustrated you get when you can’t figure out how to setup the new TV!). With a clear conceptual model, discoverability is enhanced – the user can figure out how to do new things or try variations on existing things. She can also evaluate the results of the actions that she takes.

An example of a conceptual model online is the idea that content spreads on Twitter by being re-tweeted and that this provides a good mechanism of attracting new followers. This model may suggest to the user that she should experiment with what time of day to tweet, how often to tweet and what types of content to share in order to be retweeted more frequently.

Better by design

Next time you’re struggling to figure out the settings on your favourite social network, or enjoying an app or website, consider how each of the above principles has been applied (or not). I’d love to hear in the comments about any great or ghastly experiences you might have had!

The four affordances of online networks. Part one: Persistence

Barely a month goes by without some change to the features and functioning of the major social networks that many of us now use on a regular basis. Just this week, Twitter announced that it’s indexed all tweets back to 2006, meaning that they’re now easily discovered via search. Twitter’s also been experimenting with inserting tweets in your newsfeed that were favourited by people you follow, as if they were retweets. And conversations were made more prominent via threading, which links together comments about an initial tweet. If we look to Facebook, two of the biggest changes have been the shift to the timeline profile, as well as constant tweaks to the newsfeed algorithm, including how often – and where – content from Pages are shown.

Each new change is met – somewhat predictably – with complaints from users. But what if the basis for those complaints is not simply that adapting to new ways of doing things requires effort?  What if these changes are actually fundamentally altering the “rules” of these networked spaces that many users now consider to be their online living rooms?

In the introduction to her recent book “It’s complicated – the social lives of networked teens”, Danah Boyd outlines the concept of “affordances” of online platforms. An affordance is a characteristic of an environment that makes certain types of behaviour possible. It doesn’t determine directly what will happen, but people using that space will need to work with – or around – that particular feature, and so their behaviour will be shaped by it.

Boyd describes 4 affordances that affect what we do in our online networks: persistence, visibility, spreadability and searchability. These affordances provide a useful way to think about our relationships to the content that we share online and, by extension, to how we interact with each other around that content.  In a series of posts, I’ll take a look at them in more detail, specifically with respect to how our social networks keep changing.

Persistence – how long your online content lasts

We’re coming to realise that pretty much everything online is persistent now – not just static pages, blog posts and so on, but also our activities on social networks. For example, we know that Facebook is storing not just our visible activities such as commenting on, and liking, our friends’ posts, but also the data about what we decide not to post, or delete. Tweets are all being automatically archived in the Library of Congress, and are also available to select research institutions working with Twitter to study the behaviour of users.

For me, our reaction to the issue of persistence seems to be closely related to two of the other affordances: searchability and visibility. Basically, pretty much everything you post online is going to stay there “forever” in some form, even if just on some server at Twitter or Facebook. But what really affects the user is how easy it is for anyone else to find it. And this can depend on search tools, and whether the audience for a post is somehow changed after it’s shared e.g. by sharing that data with unintended recipients such as researchers or even new friends.

Do you know what medium you're creating your digital footprint in? What happens when you thought it was sand and discover it was concrete? Image credit: Flickr user Dave Mathis: https://www.flickr.com/photos/eldave/1455743875/

Do you know what medium you’re creating your digital footprint in? What happens when you thought it was sand and discover it was concrete? Image credit: Flickr user Dave Mathis: https://www.flickr.com/photos/eldave/1455743875/

Travelling back in digital time

And this is where changes to features matter. With Twitter’s original search tool you couldn’t go back very far into “Twi-history” and so the content never really felt persistent. But now, in addition to third party tools such as Topsy that allow you to go back through the archives, and Storify that allow you to preserve conversations, Twitter has now indexed all tweets back to 2006 making them discoverable within Twitter itself. What may once have felt like a forum for throwaway chatter now becomes something more lasting and possibly with more consequences.

Ditto Facebook where the obligatory shift to the Timeline-based profiles suddenly meant that your friends could search back through your archives if they wanted to, viewing the you of several years ago, possibly before you were even friends. In relationships, we’re used to sharing information about ourselves gradually, incrementally revealing our vulnerabilities. The persistence of our digital pasts presents us with a new way of getting to know someone – and one that is further complicated because it may only be representative of our online life, not of our offline actions.

What’s wrong with forgetting?

The popularity of Snapchat, where content is destroyed after it’s viewed, suggests that there’s a demand for non-persistence online. We need places where we can engage in the kind of less consequential, more fleeting interactions that face-to-face meetings have often permitted. In “It’s complicated”, Boyd argues that being able to experiment with different identities without major consequences is a key part of being a teenager, and I’d argue that adults need this opportunity too.

We’re often doing things for the first time, or make mistakes that we’d rather be able to forget as part of a process of learning. Where actions are held only as memories, they fade and may even be completely forgotten. What are the consequences of persistent online records where events can be revisited and even revealed to those who weren’t part of the original audience? And what happens when what was once perceived as a safe, somewhat private space suddenly becomes a more public one?

From persistence to privacy and onto visibility

Changes to social networks, and also the creation of third party tools, are increasing the persistence of online content. As we build up a larger and more searchable online history, what are the consequences? Do we react by more carefully controlling the access to our past lives? For example, a friend of a friend’s daughter recently moved schools and created a brand new Facebook profile  so that none of her new friends could see her interactions with her old school friends. Or do we still take comfort in the notion of privacy through obscurity?

Which brings us to the next post in this series, where we’ll consider visibility – whose eyeballs you want on your content.

Gone fishing – liking versus loving content online

In 2012, Robin Sloan created a very cute little app called “Fish“, a tap essay exploration of what it means to love something on the Internet (and well worth 10 minutes of your time to download and read it). Sloan argues that when you really love something, you pay it repeated attention; revisiting it, and noticing or re-appreciating more details with each visit. Loving a thing – as for loving another person – means that you want to keep returning to it and spending time with it. Whatever the thing might be – a poem, a song, a film, a brilliantly written essay – it’s had such a compelling effect on you that it’s more than just a glimmer of passing gold in the stream, it’s something you want to catch and keep.

And in the era of social media, it might also be something you want to share with others.

What is love?

So how does loving something work online? It seems to be both a question of behaviour – how we signify special affection for a particular item of content over another – but also a challenge for technology – how we save and revisit cherished digital content.

Like, like, like /= love

On Facebook, a like is relatively cheap currency. Yes, a like has social value – it can indicate agreement, shared celebration, a public social declaration of approval, even that you appreciate someone’s humour – but once you’ve liked something, it’s often quickly forgotten and you’ve moved onto the next thing. Likes on Facebook are so lightweight in terms of your relationship with the specific item that you’ve liked, that you don’t even receive notifications on that post if anyone else interacts with it. They’re a fleeting interaction, a passing fancy. Notifications only start once you’ve left a comment.

Furthermore, there’s no easy way of going back and revisiting what you’ve liked; these momentary connections are only enduring love in the eyes of the algorithms that try to predict what you might like next…

Likes gain weight when more people pile on the praise, shooting the liked item up the newsfeeds of friends, but this doesn’t mean any individual *loved* the post, just that the peer group as a whole thinks it has some social value (and we’ve heard plenty in recent weeks about how Facebook is influencing this).

Rather like the boyfriend who brings you flowers, charms your parents and didn’t forget your birthday, this type of “social” content might be ticking some of the right boxes, but that doesn’t guarantee it’s setting your heart alight and that you’ll care about it 6 months from now.

The read later button that Facebook recently introduced may help with the bookmarking aspect of saving content to read later, but it still doesn’t allow you to star something to indicate both that it is worth saving and that it’s good.

Maybe you have to take the content off the platform for that because, once found, loving something becomes less of a social phenomenon and more of a personal one?

Favourites on Twitter also don’t solve the love problem. It’s not that they don’t have complex social meanings, but, as per Facebook likes, the action alone is not meant to be an enduring bookmark for the user. I have over 11k favourites – it’s never going to be an effective way for me to find that blog post about that thing.

Likes on Instagram are less complex – you’re just showing that you appreciate a photo, but it’s still very difficult to go back through the list of photos that you’ve liked and find a specific one.

Google+ offered the prospect that when you gave something a +1, you would be able to go back and find everything that you’d given a +1 to. Except that Google+ hasn’t really caught on as a competitor to Facebook and Twitter – without the stream, there’s not much fishing to be done in the first place.

How do you "heart" content online?  Image from Flickr user UnaMamaSnob: https://www.flickr.com/photos/mammasnob/8332953919/

How do you “heart” content online?
Image from Flickr user UnaMamaSnob: https://www.flickr.com/photos/mammasnob/8332953919/

Curation for your infatuation

So what to do about both indicating superlative content and saving it for later? Is the answer to both the social challenge and the technical challenge to be found in curation?

Making desert island lists of favourite movies or albums or meals has long been a fun game to play – imagining shrinking your record collection down into a few choice samples that you’d be forced to listen to forever is certainly one way to test how much you really like something. And continuing the obsession with lists, there’s now a trend for bloggers to create round-ups of the best posts from the past week (See the URLs of wisdom, for one!). But these are mostly valuable when a) you share similar tastes as the person doing the curating and b) their reading habits allow them to continue to identify new and interesting content rather than relying only on the same sources every week. Even then, it’s debatable that there are going to be 20 things you really love in a given week – to the extent that you’d want to keep going back to those items.

A new network being developed by Atlantic Media, called This, is based on the premise that each user only shares one link each day to something that they think is really valuable. But how safe would you play it if you could only cast a single daily vote that was meant to indicate all of your tastes?

Still looking…?

At least for the major social networks that I’ve considered here, finding content that you really love doesn’t seem to be the point. Social networks are more about promoting repeat visits to the site rather than to individual items of content, and so relationships with the content are encouraged to be fleeting and news-like. Tech-wise, most attention is paid to the upfront features that determine the visibility of that content, such as newsfeed algorithms or retweets, rather than encouraging archiving and making chosen items of content accessible later.

So does this mean the options to develop a more in-depth relationship to online content are skewed against us? Or just that we need to think more deliberately about how we separate out our likes from our loves, acknowledging that perhaps the things we love require us to create a separate place away from those we merely like?

 

 

The comfort of strangers – finding safety by putting eyes on the digital street

I recently read Danah Boyd’s “It’s complicated” which I enthusiastically recommend as a great dissection of many common concerns about the Internet. Boyd devotes chapters to topics such as identity, privacy, danger and addiction, discussing them in relation to the behaviour of teens online. In addressing the fears that adults have – many of which are seeded by media scare stories, she argues – Boyd is clearly flying a flag that proclaims “The kids are alright”. However, many of the ideas that she presents apply well beyond teens, and provide great starting points for conversations about our collective online behaviour and the assumptions we may have made about it.

One of the sections of the book that particularly caught my attention was the conclusion of the chapter on danger. The majority of the chapter refutes the media-inflated idea that kids who use the internet are at risk from strangers lurking in chat rooms or on social networks.  Boyd argues the dangers to teens on the internet are not as scary as news stories might have us believe.

But she also acknowledges that teens do occasionally end up in destructive situations online. She explains that this is usually the product of troubles in their offline lives, ones that give them reasons to engage in risky or damaging behaviour online. In one example, a teenage girl detailed her struggles with abuse and suicidal thoughts via a public YouTube video, which tragically failed to get her the support that she clearly needed.

Why are cries for help and other warning signs of underlying misery often ignored online, or fail to attract the attention of adults who could intervene to help? One of the reasons Boyd provides is that adults are turning a blind eye to the struggles of others in an effort to protect their own children. By banning their kids from hanging out in online spaces (where they fear their kids may encounter others with problems) adults remove the eyes – and the support networks they could attract – that those in trouble really need.

Community – the digital eyes have it

In concluding the chapter, Boyd urges us to stop turning our backs so that we face away from the “scary” interactions we might find online. And to stop hoping that turning inwards will mean that the bad things will no longer be a threat to us or our loved ones. As she explains,  “When parents create cocoons to protect their children from potential harms, their decision to separate themselves and their children from what’s happening outside their household can have serious consequences for other youth, especially those who lack strong support systems. Communities aren’t safe when everyone turns inward; they are only safe when people work collectively to help one another and those around them.

Just as on a street, removing our adult eyes from the digital street leaves it an unwatched space, one where neither we (nor anyone else) will notice when it’s important to intervene.  “People may appear to ignore a child biking down the street,” says Boyd, “but in a healthy community, if the child falls off the bike, concerned individuals will come out to help because they are all paying attention. Young people need the freedom to explore and express themselves, but we all benefit from living in an environment in which there’s a social safety net where people come together to make sure that everyone’s doing ok.

Boyd notes that her call for eyes on the digital street to create a safe atmosphere is an extension of an idea that urban theorist, Jane Jacobs presents in her book “The death and life of Great American Cities”. Intrigued, I decided to take a closer look.

The digital street - stairway to heaven or highway to hell? Image credit: Flickr user Magdalena Roeseler: https://www.flickr.com/photos/magdalenaroeseler/10103244315/

The digital street – stairway to heaven or highway to hell?
Image credit: Flickr user Magdalena Roeseler: https://www.flickr.com/photos/magdalenaroeseler/10103244315/

Urban planning theory – and the digital street

Jacobs’ book is a strong criticism of urban planning in large cities in the US. It’s also surprisingly readable, and a fascinating consideration of the social dynamics of how and why people use spaces in cities. The more I read, the more I saw parallels with online interactions – not just among teens, but more generally among anyone using shared online spaces.

Cities, unlike small villages, are places where we expect that the majority of residents will be complete strangers. And yet these strangers can create dynamics that ensure that there’s an environment that feels safe. Jacobs argues that one of the functions of streets is to create that safety, which is achieved via the following 3 conditions (quoted directly from Jacobs’ book):

  1. There must be a clear demarcation between what is public space and what is private space.
  2. There must be eyes upon the street, eyes belonging to those we might call the natural proprietors of the street. The buildings on the street equipped to handle strangers and to insure the safety of both the residents and strangers, must be oriented to the street. They cannot turn their backs or blank sides on it and leave it blind.
  3.  The sidewalk must have users on it fairly continuously, both to add to the number of effective eyes on the street and to induce the people in buildings along the street to watch the sidewalks in sufficient numbers. Large numbers of people entertain themselves off and on by watching street activity.

Sharing our online sidewalk stories

Doesn’t this also describe the way things might work in some places online? Many of us are strangers to each other on the Internet. Our feeds on social networks such as Twitter are full of people we haven’t necessarily met in person, despite reading their musings for years. And yet Twitter probably fulfils the three conditions Jacobs defines:

  1. There is a clear demarcation between public and private – we share what we want people to know, and there is an unspoken etiquettes that usually results in everyone respecting the context in which the information is shared. So, for example, you might live tweet the talks at a public event, but the private conversations you have in the bar afterwards don’t end up online
  2. There are plenty of eyes on the Twitter stream (although what those eyes are engaged with i.e. who they choose to follow, is a matter of personal choice much as you might choose which neighbourhood you live in)
  3.  There are “people on the street” at all times of day as users around the world interact with each others.

So in light of this, how many of us feel that the eyes that we put on our digital streets are not just looking for information or entertainment, but are actually looking out for each other? How feasible is this and does it differ from street to street, platform to platform?

In considering some of the factors that might affect a willingness to look out for one another, how big is your digital street, your community? And what determines whether you’d offer to help if you saw someone in trouble, either because they were being publicly harassed or because a change in their behaviour indicated that they might need a hand? Perhaps you feel more comfortable reaching out via a back channel, rather than in public. Or do the ties to a semi-stranger feel too weak so that it would be awkward to get involved at all?

Does this kind of helpfulness also extend to keeping your digital street in good order in other ways such as reporting spam, blocking bots and encouraging good behaviour by upvoting liking or resharing content that you’d like to see more of?

I’d love to hear your feedback and ideas in the comments!

Closing time! Predicting when users will leave an online community based on their language use

Back in 2011, in his book “Everything is Obvious”, Duncan Watts was musing on the tremendous potential to be tapped in the volumes of data that the internet is now giving us about human interactions. Comparing it to the discovery of the telescope, which revolutionised astronomy, he enthused that big data from our collective online activities gives us a powerful new lens to understand how we behave.

This theme was echoed by all three of the speakers in the session on Learning about People and Society via Analysis of Large-Scale Data on Human Activities at the AAAS meeting that I attended in February.  Here I’ll focus solely on some of the work of Jure Leskovec, Assistant Professor at Stanford University. Of particular interest to me was what Leskovec’s studies have taught us about the lifecycle of an individual within an online community – including predicting how long they’re likely to remain a member of it. From a community management perspective, this work could prompt more data-driven ways of monitoring and facilitating engagement online – something I’ll return to later.

Mmmmm – beer!

In their research, Leskovec and colleagues decided to focus on language use. Language is crucial for self-expression, and for creating and reinforcing a sense of group identity. Sociologists and psychologists had already learned plenty about how language use evolves in the offline environment – but what happens online?

Leskovec’s study focused on two large online communities, RateBeer and BeerAdvocate, which are both over a decade old and therefore provide the ability to study multiple generations of users of the sites. Futhermore, the sites have many active users – almost 5000 have contributed at least 50 posts each – giving a large amount of data to analyse for variations in language use.

The first things the researchers looked at were the trends in language use amongst individual community members and by the community as a whole.  Individual users each follow a linguistic pattern whereby they initially use more personal pronouns and their own word preferences when submitting reviews, but over time they drop the self-referencing and adopt more beer-specific vocabulary.

The community’s linguistic norms evolve over time too. For example, if you’re a beer drinker, in 2003 you’d be referring to a beer’s aroma whereas by 2005 you’d be calling it smell. Another noticeable trend was that reviewers on the sites were found to use more “fruit” terms when describing their beverages as the years passed.

Linguistics – and last orders

Is there any relationship between an individual’s choice of language and that favoured by the community as a whole? And if so, does this relationship change over time?

By taking snapshots of language use across the different months in the study, the researchers were able to calculate how distinctive the language used by an individual was compared to the language used by the rest of the community at that point. They could then compare this to the stage the user was at in her lifecycle – as determined from the total number of posts she had made before leaving the community.

If you imagine the effects of alcohol on your social interactions on a Friday night in the pub, they’re not all that dissimilar to the changes in language use by individual community members of the review sites. You start off shy and a bit distant from the people you’re with, then warm and adapt to your companions and finally, if you’ve had a bit too much to drink, become less coherent before parting ways at the end of the evening.  So, too, in the online environment, a user will become increasingly receptive to the linguistic norms of the community for the first third of her time within it, but after a point of being maximally “in sync”, the gap between her language use and that of the community will widen again until she eventually leaves.

Tick tock - when's beer o'clock? Image credit: Flickr user ChrisWaits https://www.flickr.com/photos/chriswaits/5620435602/

Tick tock – when’s beer o’clock?
Image credit: Flickr user ChrisWaits https://www.flickr.com/photos/chriswaits/5620435602/

It’s not you – it’s me

So, what’s the reason for the growing gap in language between an individual and other users of the site towards the end? Given that the researchers had already observed that the community’s use of language continually evolved, the key question to address was whether a reviewer moved away from the core community by starting to use a language that was foreign to it, or because she fails to adapt to the changing word use within the community.

This can be determined by comparing a user’s current language use to their previous language use. For these sites, users’ “last orders” came because they had stopped adapting their language at later stages in their lifecycle – they’d got stuck in their linguistic ways while the community continued to evolve away from them.

Calling time at the bar

The user lifecycle of coming into sync with the community and then becoming linguistically distant from it follows the same pattern for everyone – and it correlates with how far through their own user lifecycle they are, rather than occurring after a fixed number of posts. Furthermore, the lifecycle analysis showed that users quickly learned to pick up new words once they’d been introduced into the community – lexical innovation – but that this decreased over time. Those users that were most adaptive initially, were also the one that were likely to contribute more posts.

These observations raised the intriguing possibility that a user’s exit from the site could be predicted based on their initial few posts.  Leskovec and colleagues combined the various measures developed in the study – including first person pronoun use, lexical innovation, and consistency of language use – to show that it was possible to estimate whether a user would leave the site within a certain number of posts after their first 20 or 40. This gave a significant improvement over existing methods used to estimate churn rate based on activity levels alone.

Implications for community management

Imagine if, as a community manager of an online community, you could track the type of linguistic changes described in this paper. Perhaps you could identify when your current contributors are likely to lose interest in the community and whether you could take steps to compensate in advance.

Maybe further studies could identify what happens to users once they leave one site – do they start completely afresh on another and repeat the same lifecycle there? Or does their behaviour somehow evolve as they move through a number of online communities? Does being a member of multiple online communities at the same time affect the rate at which your language use evolves or whether there’s cross-fertilisation of terminology between online communities? Is it possible to identify users as being culturally bi- or even multi-lingual online?

I find it heartening to see an example of a data-driven approach to understanding online communities, and it underlines once again that community managers need to have a range of tools in their toolkit. It’s important to make new analytics methods and an awareness of relevant research in the social sciences part of this toolkit— doing so makes a lot of sense for a role that depends so much on good communication between people.

References

No Country for Old Members:  User Lifecycle and Linguistic Changes in Online Communities – Danescu-Niculescu-Mizil et al., Proceedings of the WWW, 2013. Proceedings of WWW, 2013.

Instant replay: how a study of online communities helps to re-run scenarios in order to understand popularity

In the last post, we looked at how groups behave when binary decisions are involved. There, it’s assumed that each individual in the group has their own threshold that needs to be exceeded before they’ll take action. Granovetter’s riot model provided a useful place to start thinking about why the interactions amongst individuals in a group are important, but the simple threshold model doesn’t take into account more complex dynamics that are involved in social relationships or how these might affect decision making.

One noted social phenomenon  is cumulative advantage (also known as preferential attachment). Here, once a few people express their liking for something, it will become more popular still, and any differences between the popular choice and any less popular alternatives will be amplified.  Cumulative advantage tells us that it’s the number of people that like something that’s important to its success – not necessarily any intrinsic qualities of the object itself.  As Duncan J. Watts argues in his book Everything is Obvious, this goes against our common sense feeling that it must be that a popular item has some special, distinguishing features.

If we had the chance to repeat life multiple times, we could test which of the two ideas was actually true. If it’s the intrinsic features that are important, we’d expect that every time we replayed history, the same item would emerge on top. But if cumulative advantage is at work, then different items might emerge as favourites each time.

But of course, we only get to play history once. And this is where the internet becomes an incredibly useful tool for studying network effects – the large numbers of users and ability to create different online environments can allow hypothesis to be tested in multiple parallel situations, as if history were being allowed to play out several times. This is nicely demonstrated by Watts and his collaborators in a fascinating 2006 study of an online community of people interested in listening to music (no, not YouTube!).

Do you like what you hear? Image credit: Photo by Flickr user Mark JP http://www.flickr.com/photos/pyth0ns/6757854133/

Do you like what you hear?
Image credit: Photo by Flickr user Mark JP http://www.flickr.com/photos/pyth0ns/6757854133/

In the experiment, participants from a teen social network were recruited to a site called Music Lab, specifically created for the study. Each visitor was assigned to one of two types of condition – “independent” or “social influence”. In both cases they were asked to listen to and rate songs and given the opportunity to download them.  In the social influence condition participants could also see how many times others had downloaded the songs – the “social” aspect.

Over 14, 000 people took part in the experiment. The researchers divided them into 1 of 9 different “worlds”  – 8 of which had the social feedback about downloads displayed to members. All worlds featured the same 48 songs and started with download counts at zero. As songs were downloaded, this social data contributed only to the specific world where the song was accessed so that each world provided an independent repetition of the study. The additional group that wasn’t shown any social feedback provided a control for quality – given that participants couldn’t see what anyone else had downloaded, it was assumed that songs that became popular there might be the ones that were intrinsically better.

So what happened?

Where downloads were shown, the social input did influence what other users downloaded, and popular songs became more popular than anything in the independent, non-social conditions. What proved to catch on in one social world was also quite different to what was popular in another. So social influence increases not just inequality in decision making (“the rich get richer”), but also adds an element of unpredictability.

Interestingly, for those interested in online marketing, the whole experiment was also repeated to compare the layout of the songs on the website – displaying the songs as a ranked list in one scenario but as a random grid in another. The ranked list provides a clearer signal about the preferences of other users, and unsurprisingly, resulted in even more inequality and unpredictability about which songs would end up topping the ratings.

The results as a whole were even more dramatic because the experiment as a whole was likely to represent a toned down version of the social signals than might be observed in the real world, where marketing tactics and even discussion amongst users might be taking place. Finally, just in case you’re wondering if the experiment merely revealed some quirks about teenagers’ music tastes, the study was also repeated with adult participants with similar results.

So, after considering riots and music preferences we’re starting to get a feel for the importance of capturing the relationships between individuals if you want to understand group behaviour. Next, we’ll move onto thinking about the role of influencers in prompting changes in behaviour.

References/further reading

Everything is Obvious – once you know the answer – Duncan J. Watts – Chapter 3 – The Wisdom (and Madness) of Crowds

“Experimental Study of Inequality and Unpredictibility in an Artiifical Cultural Market” – Salganik, Dodds and Watts (2006). Science Vol 31. 854-856.

Preferential attachment (cumulative advantage) – this has been used to explain links to pages on the Internet and differences in citations of scholarly articles.