The most common form of collective intelligence found outside the organization is crowdsourcing

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Querying Indicators

With CIF intelligence at our fingertips, we need the ability to query this data. There are two ways that data can be queried; the CIF Perl client and the web interface. The Perl client is the default mechanism for interacting with CIF data and the most stable. Using the CIF command, we can query for any indicator type that might be found within the CIF database. For example, if we wanted to perform a query for an IP address that we suspect to be associated with malicious activity, the following command will accomplish this:

cif –q 112.125.124.165

The –q command specifies a basic query of all CIF data available. CIF also allows you to search for IP address ranges using CIDR notation, such as 112.125.124.0/24. The results of this command are shown in Figure 8.8.

In this output, we can see that the IP address in question appears in both the Zeus Tracker and Alientvault Reputation lists, classified as being part of a botnet. The output provides a URL for both of these reputation lists so that you can get more context from the indicator. The output also provides information on restrictions and confidence associated with the indicator. These values are all configurable within the CIF configuration, as some lists are given a default restriction and confidence value.

If you run this command a second time, you will notice that an additional entry appears in the list with “search” listed under the assessment heading. Whenever someone searches for a particular indicator with CIF, it logs the search and will output this data in the search results. This is useful for knowing if other analysts are searching for the same indicator. In some cases, you may find that a particular indicator you are concerned about doesn’t show up in any public reputation lists, but that multiple analysts within your group are searching repetitively for the same indicator. This probably means that activity associated with this indicator warrants further investigation if so many people suspect mischief. In the case of Figure 8.9, the output of the CIF query shows an indicator that has been searched for multiple times.

If you’d like to suppress the output of entries that are generated from user queries, you can use the –e flag. This flag will allow you to specify any assessment type you do not want included in the query results. In this case, you could suppress search entries by appending “-e search” to the query.

Taking advantage of collective intelligence

Collective intelligence is a sociological concept that describes how a group intellect begins to form when people work together. By interacting and even competing with one another, the group shares information and collectively solves problems, giving them a greater chance to find answers than they would have on their own. The reason this works is because the crowd achieves wisdom by finding a consensus in correct answers and dismissing or discarding incorrect or deviant ideas.

To make this a little easier to understand, let’s look at the 1947 Jimmy Stewart movie Magic Town. In the film, Stewart goes to the small town of Grandview to conduct opinion polls and finds that the opinions of its citizens exactly match national polls. Statistically, Grandview is a microcosm of the United States, which is why their collective viewpoint is a duplicate of the entire country. Although individually each person has diverse knowledge or differing beliefs, their collective intelligence provides accurate data.

Collective intelligence can be harnessed from social media through a variety of means and can be beneficial to your organization. Surveys and polls are available on sites like Facebook and LinkedIn, allowing you to identify trends and patterns in people’s opinions. By monitoring the Likes, shares, and comments on social networking sites, you will eventually see certain patterns arise in people’s viewpoints that show the popularity of one opinion over another. To give an example, let’s say that you owned a shoe company and wanted to identify which product line would sell the most. By uploading photos to a social bookmarking site like Pinterest (www.pinterest.com), people can click a link indicating they like the product or pin (i.e., repost) photos they really like to their personal page called a pinboard. These photos can be shared with others on other social networking sites like Facebook, increasing exposure to the product. By monitoring the reactions of people, you’ll see trends where a majority of people liked one shoe over another, and thereby predict that it will sell better than others. While the results might not have a guarantee, they would tend to be more accurate than the opinion of a single or small group of decision makers.

Crowdsourcing is another term related to these collective and collaborative efforts. Each person in a larger group (i.e., crowd) provides input and\or performs small tasks that together achieve an end result. When many people think of crowdsourcing, Wikipedia comes to mind as articles are submitted by individuals and reviewed by others with inaccurate information ultimately edited out. Because so many people have knowledge or expertise in so many areas, they are collectively able to establish the truth and eventually weed out any falsehoods from the article. Another example of crowdsourcing would be the International Bar Database (www.bardb.net), where you can add information on drinks, prices, and other facts about a bar you visited into the database. In doing so, a collaborative effort results in developing a final product that many others can use.

Public sites that allow people to work with others in collaborative efforts like wikis can provide a useful resource tool in finding information, but it is important to validate whether the information is legitimate. Within the business itself, there are tools that allow you to benefit from the same methods without having it accessible to the public. As we’ll discuss further in Chapter 4, collaborative features on platforms like Microsoft SharePoint or sites like Yammer (www.yammer.com) can also be incredibly useful for your company. Microsoft SharePoint has social networking features and is installed on your corporate network servers or in the cloud (i.e., hosted on the Internet), while Yammer is an Internet social networking site for businesses that were purchased by Microsoft in 2012. Both allow you to share files and collaborate on projects within your organization without having to email documents, so you avoid multiple versions of the same document being worked on by different people. Also, by creating new sites in SharePoint, you can have internal wikis that allow members of your organization to create knowledgebases of information. Because people can collaborate on the same documents, communicate with others within the organization and extended groups like partners and vendors, it provides the ability for a group to collectively solve problems and complete projects.

As you can see by this, the act of sharing information and working with others provides tangible results. Interaction, networking, and collaboration allow you to be part of a larger, more complex entity, where the combined knowledge, experience, and efforts of the group help to find answers, complete projects, and predict trends. Through crowdsourcing, individual actions collectively shape the project and help to complete a final goal.

4.4.2 Student Trustworthiness Profile

In an e-Learning system based on collective intelligence, trustworthiness propagation is needed in order to support both e-Assessment and collaborative learning activities, such as creating student groups. As stated by the authors in [147], trust is considered as the crucial factor for agents in decision making to select partners during their interaction in open distributed systems. To this end, the author presented a computational model that enables agents to calculate the degree of trustworthiness of partners as well as enabling agents to judge the trustworthiness of the referee when basing trust in a partner on a referral from its referees, thus preventing agents from giving referrals to the reputation of liar agents .

In our context of online collaborative learning, a first relevant activity requires the creation of learning groups. Trustworthiness can support this crucial process. Most current trust models are the combination of experience trust and reference trust, and make use of some propagation mechanism to enable agents to share students’ trust with their partners. These models are based on the assumption that all agents are reliable when they share their trust with others [147]. Therefore among these mechanisms, student profiles can be a suitable approach with the aim of supporting trustworthiness propagation in the e-Learning system.

Since we propose the design of a collective intelligence application (CIA) (ie, student profile application), we first review the main principles in designing CIA . In [148] the authors presented the seven principles [149] that were adapted to the CIA requirements. This approach can be summarized as follows:

Task-specific representations: CIA should support views of the task. CIA is data centric (ie, data is key) and should be designed to collect and share data among users.

User-added value: CIA should provide mechanisms for users to add, to modify, etc. with the aim of improving its usefulness.

Facilitate data aggregation: CIA should be designed such that data aggregation occurs naturally through regular use.

Facilitate data access: Data in CIAs can be used beyond the boundaries of the application. CIA should offer Web services interfaces and other mechanisms to facilitate the re-use of data.

Facilitate access for all devices: CIA needs to be designed to integrate services across hand-held devices and Internet servers.

The perpetual beta: CIA is an ongoing service provided to its users, thus new features should be added on a regular basis based on the changing needs of the user community.

Some LMSs include a service intended to support the management of student profiles , however, these services are not designed with the aim of managing either trustworthiness or collective intelligence data gathering. For instance, Moodle [52] as a representative LMS system, which is being extensively adopted by educational organizations to help educators create effective e-Learning communities, supports the management of student profiles as follows [150]:

Students can see their peer and tutor profiles in the course.

Course managers and administrators can access and edit student profiles.

Users can view and manage their own full profile.

User profiling in Moodle offers a basic set of functions that does not reach CIA requirements presented in this section. Even those related to collaborative learning activities cannot be developed using Moodle student profiles. Despite these limitations, Moodle offers additional modules devoted to the enhancement of collaborative activities, for instance, Moodle badges are a suitable way of showing achievement and progress as they are based on a variety of criteria. Moodle badges are fully compatible with other systems and can be displayed on a user profile [150].

Therefore most representative LMSs, such as Moodle, can offer collective intelligence tools and services based on student profile, which can be taken as a starting point. However, they do not reach CIA requirements and cannot offer an overall technological solution to support a student’s security profile model for e-Assessment based on trustworthiness and collective intelligence.

In addition, contributions from literature on profiling students include an adaptive computer assisted assessment system that automatically scores students and gives feedback to them based on their responses and the questions chosen according to their student profile and previous answers [151]. Another interesting approach determines student academic failure through building student profiles with data mining methods [152]. This profile approach is based on information extracted from online surveys filled out by the students and the data analysis is conducted by classification methods. Although both studies address a specific goal related to e-Assessment applications (ie, student responses in surveys and previous answers in e-Assessments), and the proposal is based on student profiles, they are conducted by assessment components, which do not support collaboration and collective intelligence.

To the best of our knowledge, current student profiling approaches are specifically focused on concrete objectives, which cannot be extended to the scope of collective intelligence, trustworthiness, and security in e-Assessment. Therefore further on this chapter, we make a proposal on how to apply previous research presented in this section to student profiles with the aim of enhancing security in e-Assessment through trustworthiness and collective intelligence.

Open science data

As with the harnessing of collective intelligence, the recognition of the potential advantages of sharing large quantities of data preceded the web. The UK’s first data archive, the Social Science Research Council Data Bank (now known as the UK Data Archive), was first established in 1967 in response to data banks that had already been created in the US, Germany and Holland (UK Data Archive, 2007). While the web has had an obvious impact on the way people can access such data stores, in many ways the bigger impact has come from people embracing the Web 2.0 idea of having a platform for the sharing of data that has allowed the publishing of data on a huge scale. For example, the UK Data Archive not only offers a curated data service, but has also established the UKDA-store for researchers to deposit their own data, funded by the UK’s Economic and Social Research Council.32 While the data in this store may not conform to the standards in the main store, data nonetheless has far more potential value being made available in an impure form than not at all.

The UK Data Archive is just one of an increasing number of data repositories being made available online. As well as being created to support a specific set of researchers, they have also been created for specific publications,33 or specific types of data (e.g. the Protein Data Bank34 and the Biological Magnetic Resonance Data Bank35). As well as those designed specifically for the scientific community, there are also a range of other tools aimed at a more general audience, which may nonetheless be used by the scientific community: Many Eyes, for example, provides a simple interface for sharing and visualizing data online.36 The web not only allows the public sharing of data, but also the public sharing of visualizations. This not only potentially reduces duplication, but can also encourage an exchange of ideas as to how the data may be used: Google Fusion Tables,37 for example, not only enables the public sharing and visualizing of data, but also the joining of data from multiple different tables (Gonzalez et al., 2010). An important part of Web 2.0 has been in putting tools into the hands of users, such as encouraging employees to blog, rather than using official press releases as the only public face of an organization; and in this respect, freely available public tools have an important contribution to make to the creation of a more innovative environment.

Encouraging researchers to deposit large quantities of data is unlikely to be easy. Even when a community of users supports an idea in principle, as is the case with OA articles, it does not prevent the proportion of papers that are freely available online from being in the minority (Björk et al., 2010). As such, it is important that wherever possible, data should be captured and shared automatically: for example, my Experiment,38 a repository and social network for the sharing of bioinformatics workflows (Goble et al., 2010), also makes its data available as linked data.

It has been suggested that the large quantities of data now available have produced a new paradigm of science, where computers can be used to gain understanding from the vast quantities of data that are available (Bell, 2009); and it is important to recognize the value of the data that is being created through the use of Web 2.0 services, even when this is not explicitly scientific. The large quantities of data that are now available from social network sites such as Twitter and Facebook now form the basis of numerous studies. For example, statistically significant correlations have been found between the mood of Twitter users and the Dow Jones Industrial Average (Bollen et al., 2011), and the US military are investing millions of dollars in open-source intelligence based on Web 2.0 services (Weinberger, 2011). Whereas it once would have seemed surprising for people to share such large quantities of personal information, it is now being incorporated into numerous applications, as people make use of mobile apps that share everything from the routes they are jogging to how much they weigh. Data will not only increase in quantity as people start including ever increasing varieties of data over longer periods of time, but will potentially also, through the Internet of Things, extend the Internet to objects in the real world that each have their own unique identifier, such as a radio-frequency identification (RFID) tag (Gershenfeld et al., 2004).

The participation–collaboration pattern

The participation–collaboration pattern is also known as “harnessing collective intelligence” and “web of participation” – terms for intelligence gathered from the general public that makes a key contribution. When the number of participants in a web application reaches a critical mass, collectively those participants act as a filter for what is valuable and what is not. Web applications such as eBay, MySpace, YouTube, and Flickr are successful only because large numbers of people wish to contribute to them.

These are some of the ways to harness collective intelligence:

thinking of something that gathers new information:

people describe things to others – Wikipedia

people let others know what they want to buy or sell – eBay

people tell others of interesting news stories – Digg

people tell others of interesting websites – Delicious

what is your idea?

analyzing the information that is already on the web:

Google’s use of hyperlink analysis: if page A links to page B this suggests two things – A recommends B; and topics A and B are related

providing a facility for people to help those in need:

people giving information about missing persons, e.g. following hurricane Katrina in 2005 (Mutter, n.d.)

an individual trying to collect money to pay for university fees ("the Million Dollar Homepage"); the person constructed a collage comprising of a million pixels and sold each pixel for $1

including tagging in software so that a folksonomy can be built up:

let the masses filter the content so that only that which is valuable remains, e.g. memeorandum is a filter that presents popular political news.

Blogs

“Blog” is short for “weblog.” It is a personal log (diary) published on the web. Before it was possible to write blogs, individuals created personal websites about themselves and topics of interest, which were often static. Blogs are similar in some respects to personal websites that are updated regularly, but have a number of differences. For example, it is easier to create a blog than a website as hosting websites are available. Example blog-hosting websites are Blogger, WordPress, Drupal, and TypePad, and these blog-hosting websites have evolved. They now offer several features above and beyond simply allowing one to create and view a blog; for example, blog content has usually been text and static graphics, but is starting to include video. Example blogs include BuzzMachine, Rough Type, Infectious Greed, and UK Web Focus. At the end of 2011, the company NM Incite tracked over 181 million blogs (Neilsen, 2012).

The software My Blog (http://www.myblog.com) allows a blog owner to see who has been viewing a blog. Blogs have made it easier for anyone to have a voice on the web, and are evolving into new forms, for example, they are included in social networking websites such as MySpace and Facebook, and there are now microblogs.

Blog-hosting software allows a blog owner to create a “blogroll,” a list of blogs cited by a blog owner (see Figure 1.1). A blogroll is an example usage of the declarative living and tag gardening pattern – the declarative living pattern because the entries on a blogroll show something about the creator of the blogroll, and the tag gardening pattern because the entries on a blogroll are tags. John Lydon has an entry on technoracle.blogspot.com, which is an example of the declarative living pattern, as it links to a punk rocker. Perhaps the blogroll’s owner likes punk rock.

Figure 1.1. Blogroll of technoracle.blogspot.com

A “moblog” is a blog designed for viewing on a mobile device, such as a mobile phone.

Exercise:

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Go to the blog ldms.oum.edu.my/blog/ and display a recent entry.

Microblogs

“A microblog differs from a traditional blog in that its content is typically smaller in both actual and aggregate file size” (Wikipedia, n.d.). Microblogging platforms such as Twitter allow only short messages, which can be sent to one’s followers, who for some individuals number in the millions. When microblogging was first available it was viewed by many with derision, to be used to follow celebrities and for issuing thoughtless remarks of questionable worth. However, users soon found many uses for Twitter. As well as being used to carry out everyday conversations, it has been used to more significant effect, for example in stirring sentiment that has brought down a government. Twitter is banned in certain countries but there are other microblogging platforms that citizens in those countries can use.

Twitter is a blog-hosting website that allows users to send and receive other users’ messages, which are called “tweets” and restricted to 140 characters of text. It was launched in early 2006 by Jack Dorsey, an American software developer and entrepreneur. It is one of the most popular social networking sites and has been growing fast.

Twitter allows both social networking and microblogging. The tweets are displayed on the user’s profile page and are publicly visible by default, although there is the option of restricting access to one’s page to one’s friends. Users have the ability to subscribe to other users’ pages to get updates from these users; this is called “following.” Subscribers are known as “followers.”

Users send and receive tweets via the Twitter website, or a mobile device (running a Twitter app), or a mobile phone (using SMS). Currently (spring 2013), the latter facility is only available in certain countries. Twitter is freely available; however, if you use it via SMS then it may not be free as the mobile service provider could levy a charge.

Twitter grew from 400,000 tweets per quarter in 2007 to about 300 million tweets per day in 2012 (Twitter, 2012d). According to Twopcharts (n.d.) in March 2013 there were over 637 million accounts. In December 2012, the site had more than 200 million monthly active users worldwide (Twitter, 2012c). Twitter allows users to perform a search in real time. Users can browse through conversations taking place on Twitter and other social networking sites using the application TweetDeck, which was developed by Twitter.

The usage of Twitter spikes during major events. For example, during the Euro 2012 soccer tournament the number of tweets per second peaked at 15,358 (Twitter, 2012a). According to Quantcast (2013b), twitter.com is ranked the fifth most popular website in the USA.

Quantcast (2013a) gives demographics of Twitter users in the USA. For each category, it compares the proportion of Twitter users with the proportion of overall web users in the USA, which enables one to ascertain the type of person that Twitter appeals to. Tables 1.2 to 1.5 show the situation in February 2013. Table 1.2 shows usage by age. The proportion of 18–24 year olds that use Twitter (22 percent) is significantly larger than the proportion of web users generally in this age group (12 percent). The proportion of households with an income in excess of $150,000 (30 percent) is slightly larger than the proportion of web users in this category generally (28 percent) (Table 1.3). Nearly half (45 percent) of Twitter users are male while just over half (55 percent) are female, and the proportion of females using Twitter is slightly larger than the proportion for web users generally. The proportion of Twitter users with children (55 percent) is slightly larger than those without children in the household (45 percent), and the proportion of Twitter user households with children is slightly larger than the proportion for web users generally.

Table 1.2. Twitter usage by age in the USA, February 2013

Age range	Proportion (%)
< 18	17
18–24	22
25–34	23
35–44	17
45–54	13
55–64	6
65 +	3

Source: Quantcast (2013a)

Table 1.3. Twitter usage by household income in the USA, February 2013

Income range	Proportion (%)
US$0–50,000	17
US$50,000–100,000	25
US$100,000–150,000	28
US$150,000 +	30

Source: Quantcast (2013a)

Table 1.4. Twitter usage by level of education in the USA, February 2013

Level	Proportion (%)
Not attended college	49
Attended college but not studied at graduate level	38
Studied at graduate level	13

Source: Quantcast (2013a)

Table 1.5. Twitter usage by ethnicity in the USA, February 2013

Ethnicity	Proportion (%)
Caucasian	67
African American	17
Asian	3
Hispanic	12
Other	1

Source: Quantcast (2013a)

Table 1.4 shows that 49 percent of Twitter users have never attended college, a slightly larger proportion than that for web users generally (45 percent). Nearly one in five (17 percent) of Twitter users are African American, a significantly larger proportion than the number of African American web users generally (9 percent) (Table 1.5).

Twitter is a short messaging service that requires access via a web browser. (Many social media sites offer the facility to send short messages.) The generic name for a short message is a “status update.” It is possible to cite another Twitter user in a tweet, in what is referred to as a “mention.” Twitter allows users to send a private message to one of their followers in a “direct message.”

Anyone can be a follower of a Twitter account; no permission is required from the account owner. The user’s home page displays all the tweets that have been posted by the people the user “follows"; an example is shown in Figure 1.2. A reply to a tweet is referred to as an “@reply.”

Figure 1.2. A Twitter home page

Twitter has a comprehensive “Help center.”

A tweet can contain a URL that links to an image, video, or website. Users can forward a tweet they have received to their followers in a “retweet,” but Twitter does not have the facility for users to edit a tweet before retweeting it.

It is possible to search Twitter for tweets about a topic (identified by a hashtag followed by a keyword), user, person, or account.

A message sent out from one social media site can easily be distributed to other sites. For example, Facebook and blog-hosting sites allow users to post status updates to Twitter automatically. Similarly, Twitter allows users to post tweets to Facebook automatically.

Twitter allows a tweet to be embedded in a blog or website. The Twitter buttons shown in Figure 1.3 can be added to a website.

Figure 1.3. Twitter buttons on websites

The Twitter web interface was built using the Ruby on Rails framework. The website maintains application programming interfaces (APIs) to allow developers to build applications that integrate with Twitter and make use of its services and data (see Appendix 2).

Tumblr is another social networking and microblogging site.

Exercise:

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If you do not have a Twitter account, try creating one.

2.4.9 Idea Generation

Organizations are increasingly looking for ways to benefit from the collective intelligence of the masses. Several social media sites use “idea generation” tools to help solicit and evaluate new ideas. Companies like IdeaConnection allow organizations to post proprietary challenges to a community of problem solvers. If someone solves the problem, that person is awarded a specified dollar amount. If nobody solves the problem, no money is exchanged. Other tools by companies like Chaordix and IdeaScale allow users to post ideas and vote on others' ideas, helping the best ones bubble to the top. These services create networks that connect people based on who voted on whose ideas. They also create networks that connect ideas to other ideas based on the number of people who liked both ideas.

2.4.9 Idea generation

Organizations are increasingly looking for ways to benefit from the collective intelligence of the masses. Several social media sites use “idea generation” tools to help solicit and evaluate new ideas. Companies like IdeaConnection allow organizations to post proprietary challenges to a community of problem solvers. If someone solves the problem, that person is awarded a specified dollar amount. More domain-specific examples include Kaggle and TopCoder where users compete against each other for prizes in data analysis tasks or coding tasks. These sites create networks that connect people based on shared projects and challenges. If nobody solves the problem, no money is exchanged. Other tools by companies like Chaordix and IdeaScale allow users to post ideas and vote on others’ ideas, helping the best ones bubble to the top. These services create networks that connect people based on who voted on whose ideas. They also create networks that connect ideas to other ideas based on the number of people who liked both ideas.

22.1 Introduction, Problem Statement, and Context

In the past decade, the field of swarm intelligence has become a hot topic in the areas of computer science, collective intelligence, and robotics. ACO is a general term used to describe the subset of swarm intelligence algorithms that are inspired by the behaviors exhibited by colonies of real ants in nature. Current literature has shown that ACO algorithms are viable methods for tackling a wide range of hard optimization problems including the traveling salesman, quadratic assignment, and network routing problems [1–3].

In nature, ants are simple organisms. Individually, each has very limited perceptual capabilities and intelligence. However, ants are social insects, and it has been observed that groups of ants can exhibit highly intelligent collective behaviors that surpass the capabilities of any given individual. It is these emergent behaviors of ant colonies that ACO algorithms reproduce with colonies of virtual “ant agents” and use for solving computational problems.

In general, ACO algorithms are characterized by a repeated process of probabilistic solution generation, evaluation, and reinforcement. Over time, the solutions generated by ant agents converge to a (near) optimal solution. By generating a larger number of solutions each iteration (which requires a similarly larger population of ant agents), a more complete exploration of solution space can be achieved and thus better solutions can be found in less time. However, the population of ant agents in sequential ACO algorithms is a large factor in overall running time and therefore makes very large ant populations infeasible.

Because ant colonies, both real and virtual, are a type of distributed and self-organizing system, there is a large amount of implicit parallelism. In this chapter we investigate how the GPGPU computing model can take advantage of this parallelism to achieve large populations of ant agents while also reducing overall running time. It is our hope to show that with GPU-based implementations, ACO algorithms will be seen as competitive with traditional methods for a range of problems. As a case study, we present the GPU-based AntMinerGPU algorithm, an ACO algorithm for rule-based classification, and show how the GPGPU computing model can be leveraged to improve overall performance.

Electronic culture: collective intelligence and knowledge communities

Mass media scholars Jenkins (2006), and Kahn and Kellner (2005) contribute to our understanding of how technology and media can foster and facilitate online culture and knowledge communities, which are especially pertinent when teaching and learning online. Jenkins (2006) mentions two interesting concepts, collective intelligence and knowledge communities. Collective intelligence is described by stating, ‘None of us can know everything; each of us knows something; and we can put the pieces together if we pool our resources and combine our skills. Collective intelligence can be seen as an alternative source of media power’ (Jenkins, 2006: 4).

I would argue that collective intelligence can also be seen as an alternative source of educational power. For example, in online LIS education, bulletin boards are often used to supplement and/or replace traditional face-to-face conversations. In this way, each learner has the opportunity to contribute their opinions, experiences, and interpretations to a common area, thereby shaping the learning experience and overall understanding. In effect, collective intelligence contributes to the formation of knowledge communities.

Referring more specifically to social software applications, Boulos and Wheelert (2007) feel that these technologies foster collective intelligence and decrease isolation, and have the ‘potential to promote active and engaged learning, where participants themselves construct their own knowledge through social interaction and exploration. Learning becomes an active process in which peers collaborate equally so none might dominate the interaction’ (Boulos and Wheelert, 2007: 18).

About knowledge communities, Jenkins says that ‘knowledge communities form around mutual intellectual interests; their members work together to forge new knowledge often in realms where no traditional expertise exists; the pursuit of and assessment of knowledge is at once communal and adversarial’ (Jenkins, 2006: 20).

Kahn and Kellner discuss blogs and wikis, which are enormously popular, powerful, and excellent examples of social networking and knowledge communities. If the WWW was about forming a global network of interlocking, informative websites, blogs make the idea of a dynamic network of ongoing debate, dialogue, and commentary come alive and so emphasize the interpretation and dissemination of alternative information to a heightened degree (Kahn and Kellner 2005: 88). While specifically discussing blogs and wikis in a political environment, there are many examples of blogs and wikis being used to facilitate communities in all types of specialized communities (ibid.: 91). These tools can be extended to include online LIS CPE communities, several examples of which are briefly presented below. While beneficial, care must be taken not to allow online knowledge communities to completely substitute for, or supersede other methods of communication and interaction.

Scepticism about the promise of Science 2.0

Even where there is little concern about Science 2.0 undermining traditional publishing mechanisms, there may still be legitimate scepticism regarding the promise of Science 2.0. O’Reilly and Battelle (2009), in their revisiting of the subject, described Web 2.0 as ‘all about harnessing collective intelligence’. Harnessing collective intelligence is not simple, however, especially when it is used in the narrowest sense of crowdsourcing people to work on a specific project; and even when there is widespread interest in the outcomes from a study, it may be difficult to sustain the momentum. For example, when the Guardian newspaper created a platform for investigating MPs’ expenses by publishing the 458 832 pages of documents online, it gained a lot of initial interest; but by October 2011, almost two years later, there were still 234 428 pages that had not been classified: the flood of interest had become a trickle, long before the project was finished.

Whenever a collection of data is placed online in the hope that a community of people will solve a specific problem or make use of the data in a novel way, the crowd in many cases will not emerge to support such a project. While works such as Clay Shirky’s Cognitive Surplus (2010) are important for emphasizing the huge potential of harnessing collective intelligence, even more important for capturing people’s interest are the success stories of what has actually be achieved by this method, and having clear and realistic expectations of the timescales that may be necessary. While some individuals and organizations will have crowds of people swarming around a project as soon as it starts to be discussed online, many others will need to play the long game: most Twitter accounts will not have a thousand followers overnight; no one may comment on a researcher’s blog for six months; and a wiki may end up being used to combine the contributions of a group of one. Harnessing collective intelligence will take time, and unless the necessity for realistic expectations is promoted as much as the potential of Web 2.0, researchers will be unlikely to continue working on something that provides little immediate response. It could also be the case that people are unwilling to start contributing until they find something significant to contribute to.