What is virtual group psychology, and why should you be interested? Virtual group psychology is the same as group psychology, which is a subset of social psychology, but extended to the domain of electronic communities (the web). In most cases the principles that apply to traditional forms of group psychology are extensible to virtual group psychology.
One of the sets of principles that work well to explain how individuals behave on the web is the informational social influence model, which now has some representation in virtual group behavior, thanks to Chen (2011). In this paper we obtain a wealth of information explaining herding behavior as group behavior that is related to the interactions of individuals, especially as they may be classified as peers. This is most readily demonstrated in the online auction context, but we should be able to generalize Chen (2011) to explain other groups forms on the web.
Unlike the traditional vis-à-vis group where we can interact with each other on a personal level, the way we interact with each other on the web is necessarily constraint, even funneled through narrow channels of functionality, all made possible with Web 2.0 technology. When you want to leverage Web 2.0 technology to exercise herding and conformist controls, remember that there are only a few basics involved. we are not saying how much is possible, only that in observation, three major material operations reveal themselves.
First, we can see that individuals can express their sympathy or antipathy through mechanisms like the “Like” button, familiar to Facebook subscribers, and other buttons that work similarly. We can include the thumbs-up buttons, for example, and their functional equivalents. Through these very binary state mechanisms, how much information can we really derive about the particular comment being rated? I would say, at best, there are two things we can determine: Does an individual approve of the content, and what ratio of the population approves (or disapproves) the content. Some minor pieces of information may also be analyzable, such as the level of social conformity within the population that drives individuals to follow or be herded into decisions with respect to the content they are viewing. These approval mechanisms are found almost everywhere now, and are as ubiquitous as the next kind of mechanism, the comment.
The comment, and its functional equivalents the review and consumer feedback is of a higher order of information. Through them, we can get a more nuanced sense of sentiment. The longer and more eloquent the comment, the better we can understand the emotional involvement of the individual to the subject under discussion. There is much untapped wealth at present on many dimensions of the commentary form. Understandably, content analysis is fraught with difficulty. There are languages and dialects and grammar rules, there are word counts and phrases and figuring out whether the commenter, on the balance, likes this piece of content or hates it, or is just lukewarm on it. Furthermore, we may also analyze the cumulative effect of multiples of comments and reviews. There may be ways to simplify this process, such as by rating each comment on simple dimensions of “approve” / “disapprove”, for instance, and simply counting the numbers of pro-comments versus against-comments. More elaborate content analysis schemes might reveal who our reviewer base is, whether they are educated or uneducated, for instance, or their nationality or socio-economic class.
A third mechanism of virtual informational social influence is a rating system. Rating systems are very similar in function to the approval / disapproval button, but they provide a more scalar level of information. The rating system is recognizable on most web sites as a star rating, where content consumers can vote for their relative like / dislike of the content by giving it a star rating on a scale of from 1 to 5, or sometimes 1 to 10. These ratings are not binary like the thumbs up or high five systems. We can get the consumer ratings of movies, for example. Interestingly, star ratings are usually displayed as means of the overall ratings, and all this information is usually presented somewhere in the visible vicinity of the overall rating. For example, we may see a bar of 10 stars, where seven stars are shaded, one star partially shaded, and the remaining two stars unshaded, and next to it in parentheses will be some set of numbers like “Ratings: 7.8/10 from 7,220 users” (see: Top Hat on IMDB).
These are the three fundamental mechanisms offered by almost all Web 2.0 enriched websites today: Approval gestures, comments and other compound textual units, and the rating. Their emotion type correspondences are approval, levels of sentiment, and levels of sympathy, in that respective order.
Chen, Y. (2011). Auction Fever: Exploring Informational Social Influences on Bidder Choices. Cyberpsychology, Behavior & Social Networking, 14(7/8), 411-416. doi:10.1089/cyber.2009.0355