Facebook and MySpace: Reflection of Perceived Norms and Socioeconomic Status

It's my individual project. :)

Research questions:
How does online inequality play out in the online social networking sites MySpace and Facebook?
What types of demographics appeal to MySpace vs. Facebook?
How does Facebook motivate "honors kids," white kids, rich kids, and kids of higher socioeconomic statuses to abandon MySpace and join Facebook?
How does danah boyd's observations about teenagers on these social networking sites translate into the actions of high-school graduates? In other words, what adults join Facebook over MySpace and why?

Sites used:
www.facebook.com
www.myspace.com
Both are global social networking sites that allow users to add friends, send messages, leave comments, upload and tag photos, post events, etc. The main differences between each website is the aesthetic of user profiles (MySpace can be altered in "creative" ways, whereas Facebook cannot) and the ability to post music to one's page (this enables many bands to have MySpace profiles, where Facebook is more likely to represent individuals or organizations).

Literature Review:
http://group-processes-social-change.blogspot.com/2009/09/digital-inequality.html
I used Elyse's post on our class blog to find the main article I used to illustrate the MySpace-Facebook divide.

http://causeglobal.blogspot.com/2009/07/white-flight-online.html
This is the article Elyse's post on our class blog led me to. The article from Cause Global: Social Media For Social Change's blog titled "'White Flight' Online?" summarizes Net researcher danah boyd's observations regarding the idea that "long-held social divisions of race, class, and income are beginning to play out online, particularly among teens now choosing which social network they prefer, MySpace or Facebook" which boyd presented at the Personal Democracy forum in New York last June.

http://www.danah.org/papers/essays/ClassDivisions.html
This is danah boyd's initial article, published in 2007, based on her observations about the class differences between teens switching to Facebook or sticking with MySpace. Boyd cautions that this is not an academic article but is rather her observations in the field; however, boyd had "analyzed over 10,000 MySpace profiles, clocked over 2000 hours surfing and observing what happens on MySpace, and formally interviewed 90 teens in 7 states with a variety of different backgrounds and demographics. But that's only the tip of the iceberg. I ride buses to observe teens; I hang out at fast food joints and malls. I talk to parents, teachers, marketers, politicians, pastors, and technology creators. I read, I observe, I document," according to boyd's description of her methodology. Also, the article published two years later on Cause Global clearly shows that boyd's research had progressed, and she had conducted interviews and gathered more data on the topic.

Data collection:
I used the articles above to flush out my own assumptions about the MySpace-Facebook divide and to present boyd's data to the class. I also observed my own friends on each networking site (610 on Facebook and 339 on MySpace) to make some general assumptions about who joins Facebook after high school and who remains active on MySpace.

Conclusions and connections:
Boyd's data concluded that teens who were wealthier, white, from the suburbs, and were likely to attend college after high school were most likely to move from MySpace to Facebook when Facebook opened its doors to non-college students. However, working-class teens, minorities, and alternative teens ("emo" kids, "wangstas") were more likely to stick with MySpace. Based on personal experience and my own observations using these sites, teens who are in high school or younger are more likely to have and actively contribute to their MySpace than high school grads. Also, adults with professional careers (professors, real estate agents, psychiatrists) seem more likely to join Facebook while working-class adults are active on MySpace. I find that friends of mine who went to or are attending universities are more likely to be on Facebook and to have abandoned MySpace altogether, whereas friends who still live at home with parents, attend community college or do not attend college at all, have children, or are extremely involved in music are more likely to still be active MySpace members (even if they also joined Facebook to keep in touch with other high school friends). I have noticed a significant number of my friends who are members of both sites share different information on each site as well. For example, friends from high school who now have children are more likely to post many photos of their children on MySpace than on Facebook.

Future Research:
I would be interested in coding and analyzing either a random sample or the first 100 of my friends alphabetically on each site to see if there is a significant socioeconomic and academic difference between MySpace and Facebook users. I would also be interested to observe my friends who are members of both networking sites to see which site they more actively participate on, and whether this corresponds with their socioeconomic and academic statuses, along with their number of children. I also got some great suggestions in class about ways to use Quantcast, ways to code different demographics, and other factors to consider when doing further research.

Social Identities on TV Tropes Wiki and Wikipedia

Research Questions: What social identities form on TV Tropes Wiki? How does identity formation on TV Tropes differ from that of Wikipedia?

Research Sites: tvtropes.org, and as a point of comparison, wikipedia.org.

TV Tropes is a wiki, the purpose of which is to catalogue the various conventions, or "tropes," used in various media, including but not limited to, TV, movies, anime, western animation, fanfic, manga, theater, and literature.

Data Collection:

I chose four trope pages from TV Tropes and four Wikipedia entries to examine discussion between users. I chose two from each site using each site’s “random” tool. The first entries that came up that 1) had at least two users interacting with each other on the talk page, and 2) for TV Tropes, were pages for tropes and not particular media items or indexes, were chosen. Two more were chosen from Wikipedia that had “Featured Article” status, based upon the front page area allocated to the daily featured articles, and two from TV Tropes that I evaluated as well-referenced and common were chosen, based upon the previous experience that I had with the site. The criteria for choosing these latter four were based upon finding tropes/entries that had a lot of discussion on the talk pages, so as to better evaluate interaction between users. My eight choices are as follows:

TV Tropes	Wikipedia
Magnificent Mustaches of Mexico	Italy-Yugoslavia Relations
Prophecies Rhyme All the Time	Peter Mogila
Most Common Superpower	I Don’t Remember (song)
Adaptation Distillation	Grim Fandango

For a quick look at user pages, I looked at the user pages of eight users from each site, chosen from the eight trope discussion pages. From Wikipedia, I chose PaxEquilibrium, Irpen, Randomblue, Sabre, Rjecina, Mzajac, Samuel Sol, and Masem. From TV Tropes, I chose Silent Hunter, Glenn Magus Harvey, Ununnilium, Cassius 335, Martello, Endlessnostalgia, Tabby, and Osh. I also looked deeply into various pages of both wikis that are specifically oriented towards the contributors. The pages that I was searching for had various purposes, such as setting out various rules and guidelines for editing, expressing social norms, and giving contributors an outlet to mitigate social strain and better establish community.

Theory

Burke and Reitzes- “Identity and Role Performance”

As a part of their study, Burke and Reitzes discuss the importance and weight of identities, indicating that they are social products, self-meanings, symbolic, and reflexive, but also act as catalysts for actions that result in the confirmation of the identity (242). Thus, a perceived identity can mean greater commitment for the wiki editor, allowing the wiki to thrive. They discuss cognitive and socioemotional bases for commitment (244), which are based on extrinsic praise and on social ties (e.g. being a part of a “posse” in which all participants act similarly), respectively.

Findings/Analysis

Trope/Entry Discussion

Discussion on the talk pages naturally followed the same sort of purpose- for editing the pages that they correspond to. However, while most discussions on both sites generally remained on-topic, TV Tropes users were more likely to respond with snarky comments. This was especially prevalent on the discussion page for “Most Common Superpower,” which is a trope relating to female superheroes with disproportionately large breasts. TV Tropes also has forums and an area called “Troper Tales (tropers can talk about tropes that appear in their everyday lives).” There are multiple places for users to ask for help, including “Ask the Tropers” and “You know that thing where…” There is individual language on each site, as demonstrated in Wikipedia's glossary and in the use of trope names as conversational items on TV Tropes.

User Pages

An optimal site for any user to identify themselves is on their personal user page. On both sites, a user may use text, links, and pictures to personalize their place as a contributor, but Wikipedia goes a little further with the addition of the Userbox. Userboxes are small rectangles with links inside, which may indicate any number of things about the user, from the types of entries they like to edit, to the languages they speak, to their personal offline interests, to the search engines they favor, to their personal identification as WikiFauna (see below). Userboxes may also include a username for Skype or an email address, allowing users to communicate on a one-on-one basis. These allow users to categorize themselves into identities that extend beyond Wikipedia. Of the eight Wikipedia users I surveyed, six out of eight used user boxes (Randomblue did not personalize his user page, and Rjecina was banned). Wikipedia user Masem, in fact, had 33 userboxes on his page. Outside of the userbox, both sites give users the option of personalizing their pages to whatever extent they wish. On Wikipedia, most of the users I looked at listed userboxes, the pages that they contributed heavily to and the page statuses, and awards that they earned. However, on TV Tropes, user pages were a lot less detailed and a lot more casual. Four users had extensive lists of shows that they watched or tropes that they started or helped to name. Users tend to use more casual language on their pages, and user Ununnilium even created hypothetical “Magic: The Gathering” trading cards based on tropes and listed them on his user page. This would make it appear that describing one's identity on TV Tropes is less important.

Actual Roles

Both sites offer various ways to identify oneself within the wiki. Wikipedia has the concept of WikiFauna- various animals and creatures (e.g. WikiPig, WikiElf, WikiKraken, WikiWitch, WikiPlatypus) that indicate a user’s actions on the wiki (e.g. WikiFairies focus on style, color, and design on entries). While we briefly discussed roles and identities on Wikipedia in class, the detail here is tremendous. For example, there are six subsets of WikiElves. I believe that these act as a socioemotional basis for commitment, as the explicit name of the identity gives the user an idea of people who are like or unlike themselves, and whom they seek out (e.g. WikiKnights search for WikiDragons). TV Tropes doesn’t have such a direct and detailed listing of specially-named identities, but does contain common roles played on Wikis as tropes (e.g. Grammar Nazi, Hedge Trimmer). On their user pages, some users identify themselves with these tropes (Ununnilium: “by the way he (referring to self) is such a Grammar Nazi”), but because the identity is considered a trope for all wikis rather than specific to TV Tropes, the identity seems to be more of an afterthought.

Editing and Community Rules

While both wikis have deliberate structure and rules for editing, and both have a rule saying that the rules are loose, differences remain. Wikipedia has a category relating to the rules that is fairly easy to find, with each rule containing a separate detailed essay. TV Tropes does have rules under a category called Administrivia, for which I had to do a search to find. TV Tropes states on their front page, “We are not Wikipedia,” indicating a casual nature that Wikipedia lacks. TV Tropes, does, however, have in their list of Wiki Tropes, some actions that are looked down upon- more like social taboos, for instance, ”Complaining About Shows You Don’t Like (that is, on a trope page),” or being a “Bluenose Bowdlerizer (one that censors innocent text).” There are a few more widely-accepted guidelines. For example, a troper is to avoid Natter (discussion on non-discussion pages) and avoid saying “This Troper (that is, using main pages for personal stories and references).” However, Natter and usage of “This Troper” still appears often, which reflect the nature of the rules as guidelines. Naturally, the role of being a contributor or troper in general hinges on following these rules, and action that helps the wiki is often cause for reward.

Awards and Rewards

I was interested in the way that good and helpful participation on the sites incited rewards as a cognitive basis for commitment. Wikipedia users had barnstars, which a user may post on another user’s page. These are posted in userbox-type boxes, with text that the giver may add themselves. Also within Wikipedia are WikiLove templates, which are user-to-user "gifts" to be posted on user pages as a "thank you" for a good insight, a good edit, or just to spread good will. They usually take shape as various food and drink items. TV Tropes has the Made of Win award, along with sub-awards such as the Made of Forum Win. Made of Win is simply a Wiki-style page that anyone can edit, stating their nomination. If the nominated user takes notice or is notified on their personal talk page, they may post that they have won on their user page, but it is not immediately attached to the user’s personal page, unlike the barnstar. For being a good contributor, the personal nature of the barnstar and the wikilove item may have an effect on personal commitment on a cognitive basis. The Made of Win may have a similar effect, but only if the contributor notices or discovers that they even get the award.

Conclusions

From this data, I would like to suggest that while Wikipedia does not have traditional hierarchical institutional structure, Wikipedia is gaining a more institutional flavor because of its structured nature. The various subtypes of WikiFauna delineate specific duties and roles to play, offering a well-structured in-site communal atmosphere. On TV Tropes, these roles are not prescribed. The casual and humorous language used on the user pages and discussion areas create a social atmosphere of social that is more user-mediated, rather than institutional.

I’d like to suggest that participating on these wikis allow users to demonstrate commitment to identities in the offline world (eg. As a scholar or person of some knowledge, as a fan). However, identities on TV Tropes are more often self-formed and sometimes pre-existing as part of offline society (e.g. I'm a fan of House offline, I'm a fan of House on TV Tropes). People can act more like their casual, everyday selves on TV Tropes, rather than having to take on a staunch, scholarly identity that Wikipedia’s rules might enforce. TV Tropes’ success thrives on the devotedness of the users to their specific media of choice or media in general (that is, if there is no media, there is no TV Tropes), whereas the structure of Wikipedia allows it to act as more of an institution.

Work Cited: Burke, P.J. and Reitzes, D.C. (1991). An identity theory approach to commitment. Social Psychology Quarterly, 54(3), 239 – 251.

Reviews as group-forming.

I chose to do my project on Urban Outfitters--specifically, their reviewing feature. I chose this mostly because I like Urban Outfitters and spend too much time looking at items anyway. I also chose it because I would never have thought of reviews as group-forming before taking this course, so it seemed to fit well as the last project for this course.

Urban Outfitters is a store (both online and tangibly across several countries) which sells women and men's apparel and accessories, as well as housewares. Several years ago, a review feature was implemented, so that people who purchased the item could give their response to potential buyers. A question-and-answer section was started within the past year as well, to combat users who were asking questions in the review section. A tagging section was also recently added, where users could describe an item as "cute" or "80's" or whatever else.

For my project, I coded 108 reviews that had received a vote for “Was this helpful to you? Yes/No.” These were all from the non-sale dresses section.

I coded the amount of up-votes versus overall votes, the person’s username, the number of words in their review, if they were a top contributor, the three separate ratings they gave the product (overall, fit, and look).

Then, I coded for 11 individual variables present in their review.I coded for the following within the review:

1. Tells a story, ex. “When I saw this...,”

• “As soon as I opened the box...”

• “I read a lot of positive reviews...”

2. Sizing, ex. “I’m 5’4” 110 pounds”

• “I’m curvy and...”

• Or mention of what size they purchased

3. Quality, ex. “The material felt cheap.”

• “The zipper broke as soon as I unzipped it.”

4. Price, ex. “This was worth every penny.”

• “Great buy.”

• “This was not worth what I paid for it.”

5. Wearability, ex. “This dress is too short.”

• “I didn’t have to wear a bra with this, yay!”

• “The dress rode up.”

• “The bottom is see-through.”

6. Fit, ex. “This hugged in all the right places.”

• “The medium was too loose.”

7. Comfort, ex. “This was tight across...”

• “This was comfortable.”

8. Appearance, ex. “Cute!”

• “This dress is so pretty.”

9. Direct recommendations, ex. “You should buy this!”

• “I would wear this with tights.”

10. Indirect recommendations, ex. “This was obviously made for skinny girls.”

• “I paired this with tights, and it was perfect.”

11. I also coded for responding to other reviewers, but I ended up not including this in the ten variables. However, examples were, “I agree with...,” “I didn’t find the material to be cheap.” I ended up not using this data, however.

After coding for all of this data, I then added up the number of conditions met by each reviewer. The possible number was 10. The highest reached by a reviewer was 9. The lowest was o (However, this was only one review--which was less of a review and more of a “UO, Get more in yellow!”). The average was about 4.79.

I then found the percentage of a review’s positive feedback, for simplicity reasons. The vast majority were 100% positive, but the average was 86.74%.

Findings:
(click for a larger image)



(Professor Welser suggested that I redo this chart so that you can see concentrations of points--which is a great idea! That would give me a better idea of what I'm actually seeing in my findings, because currently, it doesn't look like much.)



In conclusion, although there seemed to be a small shift towards more up-votes if the reviewer met more conditions (ie offered a wider range of information), this was pretty miniscule (a difference of only one condition).

So, there is not enough evidence to suggest that offering a more complete range of information makes readers more likely to up-vote you. It was also difficult to collect this information because the majority of reviews received only one vote. And, for about every 5 reviews, only one would be rated at all. The reviews that received 6 or more votes were very out-of-the-ordinary.

In the future...

• I’d love to play around with the data I’ve collected so far--perhaps find the “necessary conditions” that, on average, must be met to receive up-votes.

• I’d also like to explore the reviews that received more than 1 vote, because they didn’t seem to follow any sort of pattern.

• I was able to qualitatively observe a lot of interactions during this process--for example, although there were “top contributors,” their reviews were frequently useless, like, “This dress in white is so cute!” Urban Outfitters encouraged these kinds of reviews by offering top reviewers occasional discounts, but being a “top contributor” depended solely on how many reviews a person added, not on the quality of them. Instead of looking to these reviewers as helpful experts, I think that many readers saw them as annoyances.

So, what do you think? Comments? Suggestions?

A Tunable Network Simulation (Thusly Tuned)

The continuation of my project has been the creation of a tunable social network simulation. The first addition to the project is the addition of more means of control including the size of the population and the structure of the functions that serve to create the networks. The more dominant addition has been the inclusion of an increased number of metrics for evaluating the properties of the simulated graph against the properties of a real social network graph.
The first measured property, frequently referred to as the property being of scale free, is the distribution of degrees in the network. Degree distribution in social networks tends to follow a long tailed distribution with most individuals having a relatively small number of edges to other nodes and a select few having a large number of edges. The quality of being scale free can either be evaluated as a direct comparison of the distribution to the expected distribution or as a Power Law exponent indicating the overall distribution. Measuring the distribution of degrees also lends itself to the inclusion average number of degrees as a part of the desired creation of a scale free network.
I also added measurements for clustering and grouping. I measured the clustering coefficient, and the number of bicomponent clusters. The clustering coefficient measures the degree to which nodes are tightly connected, where as the number of bicomponent clusters provides a rough estimation of the number of distinct groups present in the graph structure. Also examined, though not formalized in a metric examined for each generated graph, was the distribution of clustering coefficients which, similar to the distribution of degrees, should be non-normally distributed and tend towards a long tailed distribution.
So what do all these new inputs and outputs mean? We can adjust the inputs of the simulation and see if we get better outputs! In an unprecedented feat of interactivity I supplied the class with a set of simulation inputs and suggested that the output of the clustering coefficient was higher than expected. An arbitrary audience member (Read: Ted) made a suggestion and when we adjusted the inputs we got a graph that more closely resembled what we might expect from an arbitrary social network. Below is our co-constructed graph as well as the distribution of edges:

High vs. Low Risk Transactions on ebay.com

 Sellers on Ebay exploit a wide range of low risk transactions (books for example) as well as a variety of high risk transactions (selling electronics). Is it risky for consumers to resort to purchasing expensive, more in depth products over simpler, cheaper ones through the internet? Or, is there trust established because of the quality of the “high risk” product assuming if the seller puts something of such expense out there it is reliable?

Books presented on Ebay range from about $7.00-$35.00. They only have so many reviews, when in a large range of reviews on such a small scale of 5/5 it is hard to establish how reliable that product is. The “product details” of each book is pretty lengthy, advertising low risk products in a descriptive way. Review scales for electronics differ from books, measure by “positive feedback” percentage scale. Most electronic sellers offer free shipping. Example of attempting buyer/seller trust. (“what will you get for me?”). The sellers information directly right of product picture and information for someone to access questions. However impersonal, exploiting the seller through a screen name rather than their actual name. Location of the item itself is available. The description of each product is short.

Assume buying electronics on Ebay hold more reviews, more reliable scale, giving other indications (seller’s personal information, location of product, benefits to the buyer such as free shipping) that they might be more trustworthy. Because High Risk sellers have so much at stake, they are almost forced to remain dependable. One bad review could cause others not to buy. Purchase based more on the picture, quality, and trustworthiness rather than a general description. Low risk purchases are more opinionated. Consumers may purchase item according to their liking, and the detailed description over the small scale review system. “Normal” people can’t really sell electronics on the internet (would have to be for a lower price). Suggestions about other comparisons??? Product types of seller characteristics??

Reputation system differ online because they perceive social differentiation. New ways to transfer information about people’s reputation. Organize, quantify information. Barriers to trying to get online feedback. Getting people to contribute? Negative feedback. Honest feedback. Reliability trust. Decision trust. Personal trust.

Higher transactions more trustworthy to the extent of how valuable products are. When given positive feedback/reviews + seller information + product information à enough evidence to build trust. However, Ballot stuffing- people can skew reputation with ratings. So…study of the reviews, people’s extent they go through before actual purchase, biased? Rigged?

How QuantCast Works

Quαantcast was originally designed to assess the demographics of website traffic so businesses could be aware of the website’s audience and advertise accordingly. This is evident as many of us have noticed how advertisements on these sites have changed to clearly target certain populations. So is Quαantcast some omnipotent being that magically generates fairly accurate demographic data? Close, but no. Instead Quαantcast uses what they call in their methodology, “the inference approach.” Simply stating, “The key to this approach is to compare directly measured data with reference points that provide known truths (for example web destinations that require registration) and to use this to calibrate models.” Not only does Quαantcast generalize demographics based on previously conducted studies, but in a more Big Brother fashion, collects cookie information from users as they visit sites, especially those which require profiles with demographic data. Also known as our digital footprints. We discussed something similar to this with the Lazer et al. article. Though Quαantcast is only publishing aggregated data without identifiers, it certainly raises some privacy issues.

What does this say about the power of businesses and advertising agencies?

How have social networking sites changed especially in terms of advertisements? For example, consider the 2008 Presidential campaign advertisements on Facebook.

Demographics of Five Top Social Networking Sites Using QuantCast

For my individual project, I looked at the demographics of five top social networking sites using QuantCast.com. Included in this analysis were: Facebook, Myspace, Bebo, Friendster, and Hi5. These sites were chosen based on their rank on http://social-networking-websites-review.toptenreviews.com/. Despite having similar benefits such as: photos, comments, friends, applications, and privacy options, different sites clearly appealed to different populations. Facebook uniquely appears to have a more affluent, educated, audience. Facebook also has the largest percentage of visitors (13%) who are age 50+. Myspace, though mostly Caucasian, has an above average number of Hispanic visitors, but also appeals to a young adult audience with no college and children who are under 18. Bebo largely appeals to African American teens, while Friendster’s audience are mostly Asian young adults and Hi5 visitors are mostly Hispanic young adult males with no college who make 0-30k per year.

What are some possible reasons for these demographic differences? Specifically, why are most of the differences related to socioeconomic status and ethnicity? Facebook, for example, may attract a crowd with a higher number college students and college graduates, because, until recently, Facebook required a university e-mail address to join. Also, as Backstrom et al. posited in their theory of diffusion of innovation, that people are more likely to join groups in which they have friends who are already members. This also relates to our previous discussions of trust and recommendations from friends who are already members of a group. What other possible reasons can you come up with for the demographic differences? You can view the charts here: http://docs.google.com/present/edit?id=0AX3vFFf18ROAZGc5NGRzZ3RfMTlnc3Bmc3Noag&hl=en. QuαntCast uses estimated data of 220million internet users in the US

An index of 100 means the measurement is on par with the population estimate.

What does this imply about the changing face of the internet? Indeed in the demographic post, we see that people ages 50+ are visiting social networking sites. Is this evident of McAdams idea of “An Esteem Theory of Norms”? Has it become a norm for people to have a Facebook, Myspace, etc.?

p.s. Forgive my poor typing and grammar. I only have the use of one hand due to surgery.