Why do academics use academic social networking sites?

The original version of this post first appeared on the Trellis blog.

Earlier this week in a panel discussion about technology for academic collaboration at the Science of Team Science conference, I mentioned a recent paper, which asks “Why do academics use academic social networking sites?” The paper presents the results of a survey of 81 researchers at three Israeli institutes who were asked about their motivations for using ResearchGate and Academia.edu.

The survey draws upon the Uses and Gratifications theory from the field of media studies for its research questions – exploring whether the five broad motivations for media consumers may also apply to academics that use online professional networks. Here we outline that theory and then highlight some of the findings from the paper.

Considering Community: a brief history of academic studies of online communities

I’ve decided to start a new series of occasional posts focused on community management tips and related information. I’m tagging these Considering Community and you’ll be able to find all the posts in the series here.

Recently, I’ve been thinking about the stages of growth for online communities. I ended up reading a paper that compares the research literature on online communities to come up with a model for the lifecycle that communities follow. While I’m going to blog about that in a future post, the paper also gave a great introduction to how the literature on online communities has itself grown – and the different disciplines that have been involved.

Knowing first-hand that “community” doesn’t fit neatly into one category, this was particularly interesting to me, so I’m going to share the paper’s overview here in case it’s of interest to you too. All text below that is highlighted in italics is taken directly from the paper, which is a much recommended read.

The literature about online communities as 4 waves

The review (published in 2009) describes four waves in the community literature:

i) 1st wave – input from sociologists

During the first wave, which started in 1993 when Howard Rheingold coined the term virtual community, sociology took the lead focusing on online communities as a social phenomenon capable of modifying how people interact in society. Sociologists compared online communities to physical communities and explored the presence of various community-related concepts such as social aggregations, identity, social networks and ties, and social and collective action.

They also studied the impacts of Internet use on individuals and society, such as social isolation, social involvement, and well-being [Carver 1999; Jones and Rafaeli 2000; Cummings et al. 2002; Turkle 1995; Hampton 2003; Hampton and Wellman 1999; Katz and Rice 2002; Kraut et al. 2002, 1996]. For example, Wellman et al. [1996] and Wellman [2005] found that online communication can strengthen face-to-face communication in local communities, as opposed to producing social isolation. Moreover, they found that online interactions can facilitate accumulation of social capital which may enhance civil involvement.

Those interested in the impact of online communities on society found that by facilitating strong social relationships, trust, and reciprocity, an online community may gather enough social capital to engage in social action to achieve a collective goal [Blanchard and Horan 1998; Chaboudy and Jameson 2001; Hampton 2003; Iriberri 2005].

ii) 2nd wave – input from those studying management and business

A second wave in research on online communities started around 1996 with management researchers analyzing the value to business organizations of the content generated by online communities. Hagel and Armstrong [1997] studied online communities as viable business models capable of attracting customers who are searching for information on products or activities of interest to them, and who want to find and build relationships, conduct transactions, or live fantasies.

They suggest that if organizations provide mechanisms to identify and satisfy customer needs more accurately this can then turn into profit for vendors. When businesses provide the space for interaction, vendors can strengthen customer loyalty and also extract customer information to further improve marketing and customer service programs.

Wegner et al. [2002] focused on online communities that emerge in business organizations and are used by employees as repositories of organizational knowledge. In these communities of practice, the knowledge created and stored by members contributes to the organization’s ability to solve problems, create new products, innovate, and ultimately increase productivity [Millen et al. 2002]. This is evident in the widespread use of wikis, electronic boards, and electronic meeting rooms where team members in organizations add content and share online documents, thus reducing by one-half the time it takes them to complete projects [Conlin 2005; Goodnoe 2006].

Stuck on the shelf: How do you translate knowledge from the literature into practice? Image caption: Flickr user wy_jackrabbit https://www.flickr.com/photos/wy_jackrabbit/4294858160/

iii) 3rd wave – input from psychologists

In the third wave of online community research, psychology researchers focused on members’ relationships and attachments within online communities. Blanchard [2004] and Blanchard and Markus [2004] studied sense of community including feelings of belonging, safety, and attachment to the group. When these feelings are present, members develop lasting relationships with other members, feel attachment to the community, and perceive the online community as a source of social and emotional support.

In one online community of multisport athletes, Blanchard and Markus [2004] found that active participants develop personal friendships that in some cases move into private and face-to-face interactions.

iv) 4th wave – input from information systems researchers

Last, in the fourth wave, information systems researchers integrated previous perspectives, developed working definitions, and created research agendas to initiate a more focused and controlled empirical study of online communities [Gupta and Kim 2004; Lee et al. 2003; Li 2004]. The focus shifted to members’ needs and requirements, development of electronic tools to support online communities, adoption and implementation of these tools, online communities for new purposes such as teaching and, finally, outcome assessment [Arnold, et al. 2003; Kling and Courtright 2003; Stanoevska-Slabeva and Schmid 2000, 2001].

For example, Stanoevska-Slabeva and Schmid [2001] described the activities members conduct in online communities and matched those activities with the technology platform capable of supporting those activities; and Arnold et al. [2003] presented a model to translate member needs into technology requirements.

In the latter years of this fourth wave, the focus of the information systems discipline moved toward proposing conditions that would increase member participation and ensure online community success. For example, Preece [2000] recommended following a participatory design approach, which takes into consideration user needs, and establishing a clear purpose combined with policies of behavior to govern the interactions of members. She referred to the fostering of “tacit assumptions, rituals, protocols, rules, and laws” that define the community identity.

Similarly, Leimeister et al. [2005] proposed implementing mechanisms to encourage trust, such as discretionary levels of anonymity, which can help promote lasting relationships. Most recently, empirical studies have been carried out to test independent success factors such as presence of content quality, interaction support, organization of online and offline events, rewards for contributions, volunteerism, and posting of member pictures and profiles.

From communities on paper to communities in practice

I’ve been pondering a bit this past year how much professional roles that involve working directly with communities – whether that’s pure community management, or related activities such as marketing, customer service, and market research teams – apply what’s reported in the research literature in their day jobs. As a research scientist – whether in academia or industry – it’s a standard part of your job to keep up to date with current developments in your field of expertise. While some of that may be done alone, for example, by subscribing to updates from your favourite journals, some of it also takes place in communal activities such as lab meetings and journal clubs. How do you find and share knowledge if you’re a community professional?

There are some resources – from the #cmgrchat that takes place weekly on Twitter to the annual Community Leadership Summit founded by Jono Bacon, but it’s my sense that as a lone individual or very small team within a larger organisation, community managers can often end up feeling professionally isolated in terms of knowing how to develop new skills or where to turn to for peer-to-peer support.

Secondly, where there is useful information available  – such as websites that discuss updates to common tools that community managers use – the focus tends to be on technology updates, or on successful business reporting. Especially within organisations that haven’t fully grasped the benefit of community there can be an emphasis on the types of conversations described in wave ii) above – namely how to demonstrate the business value of online communities, whether those are internal employee communities or external engagement channels. By contrast, I don’t see many articles or other professional development resources that really focus on waves i) and iii) as described above – the sociology and psychology of communities.

Are you a community professional who’s found some resources that I’ve not yet seen, or who has successfully created your own peer network? Do you ever consult the academic literature for a different view on the daily activities that you’re involved with – or is this something that there’s not realistically enough time for in a job that already involves doing multiple different tasks in a given week?

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/

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.