Social Network Blog - r10

Kleinberg discusses web pages as if they were nodes in a social network. He looks at the organic growth of the World Wide Web and research that tries to understand if the concepts such as centrality, prestige, in-degree, and out-degree apply to the Internet network. Kleinberg discusses a recent study that found that “the Web contains a large, strongly connected core in which every page can reach every other by a path of hyperlinks” (p.1849). Of course, this notion mirrors Milgram’s notion of six degrees of separation. However, just as people have asked about hypothetical people ‘living on a deserted island’ and how they could possibly be connected to Milgram’s chain, I wonder if such interconnectivity is true of websites. I believe that this is a catch-22, in the sense that only websites that could be found could be used for the study, and the study can only report on websites that could be found.

Consider how human reproduction (which produces the nodes in a social network) can be thought of as the generation of sequential links (parent-child) and how humanity seems to exist in communities, even if some of these communities are isolated. Now consider the argument in this article that the growth of the World Wide Web was decentralized. What do you think is more likely: Milgram’s idea of 6 degrees of separation (a theory of human interconnectedness) or Kleinberg’s suggestion that any website can be reached via a chain of hyperlinks (a theory of webpage interconnectedness)?

Marks looks at the potential of social networking sites to become feeders into huge database sites maintained by agencies such as the National Security Agency. With somewhat of an alarmist approach, he outlines all the ways that the hordes of very personal, descriptive information people post about themselves on websites such as MySpace could be used to create a watchdog database.

Before I get into my analysis, I believe this funny video describes the issue perfectly. Please watch it, because it is really funny: This is so funny!!

Some of the things Marks believes that intelligence and national security agencies could use the databases for is to detect insider trading or highlight groups of terrorists. However, as Krebs’ article explained in the past, terrorists work to keep a low profile by having very dispersed social networks. Granted, such dispersion could be mapped on a social networking site. However, I find it hard to believe that a terrorist would want Friend A to see that he is connected to Friend B, who knows Friend C. The terrorist would not want Friend A to have such clear-cut access to Friend C, according to Krebs’ beliefs. Thus, I feel that social networking sites could not be used for these purposes.

To further bolster this viewpoint, I point to the part of the article where Marks points out someone who explains how “people have to wise up to how much information about themselves they should divulge on public websites” (p.3). However, of course the people with information to hide will realize this first and foremost. As soon as the first few college interns/graduates got fired from internships/jobs for information they posted on Facebook, many people who had lurid information and pictures changed their profiles. This example shows how people with “incriminating” information are the ones who will choose not to post it. I ask: Do you think that there is a direct negative linear correlation between an (a) increase in intelligence and national security agencies’ use of information on social networking sites and (b) how much information people put on those sites?

Ellison’s research project discusses Facebook, a topic of high interest to me and many other college students. It is interesting how Ellison notes that Facebook is often used to maintain offline relationships, especially compared to how often it is used to generate new friendships online. Ellison evaluates how Facebook can be used as a mechanism for creating various forms of social capital, and then she analyzes its effectiveness in those domains.

One comment that is particularly relevant is that, at the time this article was written, Facebook “distingushe[d] itself from other online social networks in that it primarily serves a geographically bound community (the campus)” (p.5). At this point, it has expanded to people who are not necessarily in college, high school, or an organization/company. I ask: If her study was done now, what findings do you feel would be different, regarding the geographic restriction previously characteristic of Facebook?

In addition, Ellison says that there “are some tendencies for Facebook members to report higher satisfaction with MSU life, bridging and bonding social capital” (p.20). (She does note that these findings cannot be statistically significant due to the small number of respondents who were not on Facebook.) I wonder: What do you think is the direction of causality? Specifically, do you think that (a) people who are on Facebook are more likely to have access to social capital and resources or that (b) people who self-select to be on Facebook are those that have higher self-esteem. In other words, does Facebook cause high social capital and satisfaction, or does high social capital and satisfaction cause one to sign up for Facebook?

Ellison also notes that Facebook is used for entertainment purposes more than informational purposes and notes that this fact “at first seemed at odds with [Facebook’s] role in forming and maintaining social capital” (p.28). Her statement implies that social networking and accessing social capital and its resources should be a planned, instrumental purpose. I determined this conclusion by analyzing her link between informational purposes and social capital and the lack of link between entertainment purposes and social capital. I believe that reading about friends’ lives gives you much more knowledge about and consequently access to their resources than a forced goal of “networking.”

Finally, one problem with analyzing this study was that there was an “extremely low incidence of non-members in [their] sample” (p.31). However, I wonder what the actual percentage of college undergraduates who are registered for Facebook is. Specifically, this sample determined that number to be 94%, but what if the national population number were also 94%? If she found a school in which many students were not on Facebook, then that school would be an outlier and produce odd, unrepresentative data. I ask: Do you think that 94% is the national undergraduate student population percentage of Facebook registrants? Why or why not?

Wellman’s comprehensive overview of the impact of online media on interpersonal relationships determines that proximity and in-person contact are not necessary for community-building. He explains how the communication node will be the person, regardless of where that person is located, rather than the “person in the place.” As computers and online access become more ubiquitous, social networks will become a placeless phenomenon. Contrary to other writers’ alarm, Wellman thinks that this is just dandy.

One question he forebodingly asks is, “Does the switch to person-to-person connectivity mean that even stably-married husbands and wives will be in separate communities?” (p.239). I think that to answer this question, it is critical to define ‘community.’ Even though Wellman makes it clear that ‘community’ is not inextricably tied to place, does that mean that people who live in extremely close proximity (i.e. the same household) do not necessarily have to be in the same community? Is ‘community’ only defined by one’s personal interests, hobbies, and favorite topics? I ask: If two people are living together but have different interests and different people with whom they explore these interests, can they still be part of the same community?

Another interesting quote that Wellman mentions is Andrew Odlyzko’s comment that “Our barber and our babysitters will continue to come from places not far away” (p.247). Although it is clear that there are some professions that need in-person communication and contact, it is also true that there are some professions (i.e. telephone operator) that do not need in-person communication and thus can be outsourced. What are some professions that are currently place-based that you could potentially see being performed virtually?

Tepperman’s article discusses deviance and how deviant actors often turn to their social networks for both (a) instruments of deviance, such as marijuana and (b) facilitators of deviance, such as a hit man. Tepperman presents deviance as a spectrum, running from the search type, in which effort must be consciously expended to find social support for deviant acts, to the contagion type, where people do not consciously intent to find support for their deviant acts. This dichotomy reminded me of homophily, in which birds of a feather flock together, and assimilation, in which people who are already friends adopt the same (sometimes deviant) behaviors. I ask: Do you think that the “search type” sounds more like homophily, whereas the “contagion type” sounds more like assimilation? Or do you think that these theories do not have any similarity?

Tepperman presents an idea that seems like an application of Wellman: Deviant communities are not confined to place. Tepperman supports this idea by referring to classified advertising for wife swapping and extramarital relations. These deviant behaviors, Tepperman shows, are created by communities consisting of dispersed people. However, I think that it is interesting to note that all of these activities do require in-person communication. Thus, although the deviant community is formed without regard to proximity, it is maintained via face-to-face contact.

Tepperman also demonstrates that trust is a necessary quality for deviant social networks. Killworth’s article had demonstrated that individuals are unable to macroscopically and accurately create a social network chain. However, an individual looking to participate in an illegal activity needs to tell as few people as possible about his or her intentions. Thus, it is even more critical that the deviant passes his or her message via the most direct route. How do you think most deviant actors determine the most optimal, clandestine chain along which to pass their messages? Do you think that this “secret” would benefit other actors, looking to pass along a message but who, according to Killworth, do not determine the best path?

Rogers’s chapter explains opinion leadership, communication networks, and critical mass; he relates these concepts to theories and examples of the diffusion of innovation throughout networks.

When discussing opinion leadership, Rogers brings up the differences between polymorphism and monomorphism. Whereas polymorphic opinion leaders serve as opinion leaders on a variety of topics, monomorphic opinion leaders serve as opinion leaders for only a single topic. This difference made me consider online friendships. Specifically, the literature on online relationships emphasizes how these friendships are usually very specified and built around a common topic. In class, we mentioned how a “bottle cap collector” is more likely to meet other people who are enthusiastic about this hobby over the Internet than he or she is to meet other enthusiasts in person. This relationship appears to be built around a single interest. Thus, I ask: Are opinion leaders online more likely to exhibit polymorphic characteristics or monomorphic characteristics?

When discussing innovativeness and opinion leaders, Rogers mentions how neither opinion leaders nor their followers are innovative in traditional communities but that both opinion leaders and their followers tend to be innovative in modern communities. When reading about this difference, I imagined the structure of traditional and modern communities, envisioning a tribal structure and a busy city structure. I thought that traditional communities are likely to rely more on a leader, such as a religious or spiritual leader, while modern communities are more likely to have different leaders for different activities (a CEO at work, a local mayor, a national president, etc.) due to their massive size. Thus, I wonder: Do you think that traditional communities or modern communities rely more on an opinion leader for guidance and influence?

Another point Rogers brought up is that “[a]n individual is more likely to adopt an innovation if more of the other individuals in his or her personal network have adopted previously” (p.359). Previous articles such as those by Wellman have emphasized that one’s personal network is not necessarily geographically based. However, some innovations that require a critical mass might be constrained, at least in part, by geography. For example, it costs more to make a long-distance phone call than it does to make a local phone call. Do you think that the original adopters of telephone technology were influenced more by others in their geographic network (area code) adopting this technology or by their (perhaps closer) larger social network?

Burt’s report discusses the network structure of interpersonal contagion and research on the network structure of social capital. Interpersonal contagion is understood via two concepts: cohesion (strength of the relationship between two people) and structural equivalence (similarity of network position for two people). Network structure of social capital is discussed in reference to opinion leaders and brokers of information between social group clusters.

One of Burt’s points is that strong relations between weakly equivalent people significantly decrease the amount of time it takes for someone to learn something new (contagion and/or adoption). It is interesting that strongly equivalent people appear to be competitive – and thus lose the ability to share information amongst each other – whereas weakly equivalent people seem to be more cooperative and allow benefits to accrue to each other. This situation reminds me of university applications. I noticed that there was more competition in high school, when everyone was applying to college, than in college, during which everyone’s post-graduation plans are different. Do you think that the level of “equivalence” (weak or strong) between two people has a direct bearing on how much “college/graduate school application help” they offer each other?

In addition, Burt notes that opinion leaders bring new information to a group via cohesion (with people outside the group) and then that the information spreads amongst the group via equivalence (of people within the group). Thus, Burt believes that an opinion leader is a product of network position more than superior authority and attractiveness. However, not everyone within a group is in a structural position to serve as an opinion leader, since many people do not have any types of “bridges” to other social clusters. Therefore, an opinion leader does display many unique characteristics in regards to his or her network position. Do you think that opinion leaders have higher levels of centrality and/or prestige, too? Why or why not?

1)

People with whom I interacted the most often:
(1) Evan F. (48 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(2) Susan S. (23 interactions) – Mother, close tie, lives in New Jersey
(3) Gregg G. (21 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 21-years-old
(4) Dan A. (20 interactions) – Friend and Group Project member, close tie, attends University of Pennsylvania, male, 21-years-old
(5) Max C. (tie, 19 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 20-year-old
(6) Rachel S. (tie, 19 interactions) – Sister, close tie, lives in New Jersey, 17-year-old

People with whom I interacted the most often via AIM (AOL Instant Messenger):
(1) Polly S. (8 interactions) – Friend, close tie, attends University of Pennsylvania, female, 20-year-old
(2) Jackie W. (3 interactions) – Friend, close tie, attends University of Pennsylvania, female, 21-year-old
(3) Maria L. (2 interactions) – Friend, moderate tie, attends University of Pennsylvania, female, 21-year-old
(Note that I interacted with 10 other people via AIM 1 time each.)

People with whom I interacted the most often via Cell Phone:
(1) Evan F. (26 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(2) Susan S. (14 interactions) – Mother, close tie, lives in New Jersey
(3) Rachel S. (9 interactions) – Sister, close tie, lives in New Jersey
(4) Sydney K. (6 interactions) – Grandfather, close tie, lives in New Jersey
(5) Emily G. (tie, 4 interactions) – Friend, close tie, attends University of Pennsylvania, female, 21-year-old
(6) Janet K. (tie, 4 interactions) – Grandmother, close tie, lives in New Jersey

People with whom I interacted the most often via E-Mail:
(1) Gregg G. (21 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 21-year-old
(2) Max C. (19 interactions) – Co-Director of SPEC Connaissance, moderate tie, attends University of Pennsylvania, male, 20-year-old
(3) Evan F. (18 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(4) Dan A. (17 interactions) – Friend and Group Project member, close tie, attends University of Pennsylvania, male, 21-year-old
(5) Michelle C. (tie, 15 interactions) – Group Project member, moderate tie, attends University of Pennsylvania, female, 21-year-old
(6) Christina K. (tie, 15 interactions) – Group Project member, moderate tie, attends University of Pennsylvania, female, 20-year-old
(7) Alex A. (tie, 15 interactions) – Group Project member, not close tie, attends University of Pennsylvania, male, 21-year-old

People with whom I interacted the most often via Text Messaging:
(1) Evan F. (tie, 3 interactions) – Boyfriend, close tie, attends University of Pennsylvania
(2) Rachel S. (tie, 3 interactions) – Sister, close tie, lives in New Jersey
(3) Craig S. (1 interaction) – Father, close tie, lives in New Jersey
(Note that I only interacted with 3 people via Text Messaging during this week.)

2a) Relationship between “medium of communication” and “strength of the tie”

AIM and cell phones are mediums which allow for synchronous interactions. Since people are probably more comfortable directly connecting to a close tie, these two mediums are used most often to interact with close ties (AIM, 78%; Cell phone, 83%).

2b) Relationship between “medium of communication” and “type of support exchanged”

I use e-mail to both to correspond with the leadership of SPEC Connaissance and to manage e-mails to this organization’s listserve. Since various Directors, Advisors, Chairs, and Committee members all need to “be on the same page” regarding this organization’s activities, I often send out mass e-mails to exchange such information. In fact, of the 106 e-mails offering “Information (Extracurricular),” 63% were sent to multiple recipients. Likewise, when coordinating with multiple people about a Group Project, I use e-mail as well. Such uses are consistent with both (a) Hampton’s statement that the Internet’s has the “ability to be used as an asynchronous form of communication that can engage others not only one-on-one, but as a broadcast of one-to-many” (p.225-226, 2004) and (b) Baym, Zhang, and Lin’s statement that the Internet was rated as better than other forms of interaction for “getting schoolwork done and exchanging information” (p.304, 2004). Thus, I postulate that e-mail undermines Burt’s theories on structural holes and elimination of redundant ties, since I am not expending extra resources when communicating with multiple people through e-mail.

I noticed that Wellman and Wortley’s (p.562-563, 1990) categories for “type of support” are not fully transferable to “new media” interactions. For example, I doubt that it is too often that people provide financial aid via new media (Do they often transfer money online from their own bank accounts to the others’ bank accounts of others?). In addition, it is impossible to use “new media” to actually ‘do things together,’ a sub-category of Wellman and Wortley’s “companionship” category.

Nevertheless, my results do correspond with their assertion that “[s]trong ties provide broader support than weaker active ties,” (p.566) since 92% of my interactions with “close” ties provided companionship or small services, compared to 13% of my “moderate” ties and 3% of my “not close” ties. (see below)

2c) Relationship between “medium of communication” and “type of relationship”

For everyone with whom I communicate besides my boyfriend, my mother, and my sister, I used e-mail more than any other medium. Because of the high number of daily interactions for which I use “new media,” I value e-mail’s asynchronous and direct nature.

In addition, due to its informal nature, I used AIM more with friends than with professionals. In addition, because of its personal nature, I used AIM with friends rather than with people with whom I have a more instrumental relationship (ie. classmates, delivery people, professionals).

I postulate that I would talk to fewer people if I did not have media such as e-mail and a cell phone. I wonder how McPherson would explain the shrinkage in the size of people’s core discussion networks with the results from my new media diary, in which I talk to my boyfriend and family members more than anyone else. Perhaps she would not classify the discussions that I have with most people as “discussing important matters.”

2d) Relationship between “medium of communication” and “duration of relationship”

I tend to communicate most often with people who I have met within the last year (duration = 0 years) via e-mail. However, I often use this method reactively, since people who do not know me very well (ie. classmates with whom I am working on a group project, Connaissance committee members, professionals) are more likely to contact me via e-mail; then I respond.

In addition, people who have known me my whole life (family) are most likely to speak with me via cell phone (my only telephone) due to their age and the closeness of our tie. However, e-mail is a close second, because I often send pictures to these relatives via e-mail.

Nevertheless, I believe that the “type of relationship” and “type of support” are confounding variables for the relationship between “medium of communication” and “duration of relationship.”

2e) Relationship between “medium of communication” and “distance to the person”

Because I am most likely to communicate via e-mail with other students on the University of Pennsylvania campus, e-mails to people who are less than 1 mile away are the most common method of communication. There is about an equal split between using my cell phone and using e-mail to communicate with people 75-100 miles away (family members in New Jersey and professionals in New York). However, the relationship between “medium of communication” and “distance to the person” is most likely confounded by other variables like “type of relationship” and “type of support.”

2f) Relationship between “medium of communication” and “person’s age or gender”

(Note that “20s,” “30s,” and “40s” refer to people in this decade for whom I did not know an exact age. “N/A” refers to group listserves and various professionals whose ages could not be specified.)

One may expect that older people are more likely to use telephone media to communicate. This seemingly intuitive hypothesis would make sense, since landlines (which have been around for longer than the “new media,” cell phones) can receive telephone calls, whereas to receive e-mail, the sender and receive must both use “new media.” All of my friends, parents, grandfathers, and professionals with whom I communicated besides my two grandmothers could be accessed via both cell phones and e-mail.

However, my grandparents’ are much less likely to use AIM and completely unable to send text messages. Thus, I only used these two media with my friends, sister, and parents.

I communicate most frequently through all media with 21-year-olds and other college-aged students. Because my group projects, extracurricular activities, and immediate friendship networks are comprised of other students of the same age, I am experiencing what McPherson calls baseline homophily (p.419, 2001) regarding age.

There appears to be no relation between “gender” and “medium of communication” besides that I use AIM with more females than males. Since I am female and may communicate on more personal matters with people of my gender, I would use this form of computer-to-computer communication for same-sex conversations. Nevertheless, my overall media use is consistent with the McPherson’s finding that people like me should have “friendship and confidant networks that are relatively sex-integrated” (p.423, 2001).

2g) Relationship between “medium of communication” and “similarity of age and gender to your own”

I appear to use e-mail more frequently than other media of communication to interact with other 21-year-olds. However, this finding may be because I use e-mail most frequently. In fact, of the females with whom I interacted across these various media, 45% of AIM interactions were with 21-year-olds females, 28% of cell phone interactions were with 21-year-old females, and 41% of e-mail interactions were with 21-year-old females. Upon noting that only 38% of my overall interactions with females were with 21-year-old females, I determined that I use AIM and e-mail more than the “expected frequency” and cell phones and text messaging less than the “expected frequency.”

2h) What if anything does this say about the role of new media in our social networks?

New media serve the same purposes as “old media” such as telephone calls and in-person interaction. Although Wellman and Gulia (1999) noted that online relationship development might take longer due to the asynchronous nature of e-mail exchange, their report references Walther, who determined that undergraduates’ “online interactions are as sociable or intimate as in-person interactions[, demonstrating that]…the Net does not preclude intimacy” (p.346). Consistent with Baym, Zhang, and Lin’s comment, my use of the Internet as a social tool is “influenced by [my] relationships, including [my] geographical distance and type, and [my] pre-existing sociability” (p.302, 2004). Finally, my usage patterns correspond to Ellison, Steinfield, and Lampe’s comment that students use “online channel[s] less to meet new people than to intensify and solidify relationships that started offline” (p.32, 2006).

E-mail is unique, because it both (a) directly connects you to a person but (b) has social mores surrounding it that make it appropriate to use e-mail contact “not close” ties. In fact, 85% of the interactions I had with “not close” ties were through e-mail. Thus, the popularity of e-mail could perhaps serve to increase the number of interactions people have with weak ties. Applying the proposed popularity of e-mail to Granovetter’s “strength of weak ties” theory, one may determine that people will have more access to resources to provide them job information than before.

3a) Relationship between “tie characteristics” and “Mindy’s location during interaction”

I appear to communicate most often to close ties and most often in a private (dorm) location.

The reason that most interactions took place in private locations was because of the media to which I had access in these locations. As demonstrated before, I often use “new media” to send and receive information. Such information comes in the form of computer files that I need to either save to or access from my computer. Since I leave my computer in my dorm room, such interactions must therefore occur in my dorm room.

My choice of dorm room for interactions is consistent with Wellman’s statement that “[r]ather than being accessible to others in public places, people now overcome their isolation by getting together in each other’s homes or by telephone and electronic mail” (p.29, 1999). In fact, Baym, Zhang, and Lin’s study found that “online interaction was conducted less frequently in public places than telephone calls and face-to-face interaction” (p.311, 2004).

Thus, location of communication is more related to the medium rather than the type of tie. As can be seen below, the most common type of medium for me to use in a public place is a cell phone, since I can walk and talk simultaneously.

3b) Relationship between “person’s characteristics” and “Mindy’s location during interaction”

For each category of relationship, I am more likely to talk to the person in private than in public. The most common types of people with whom I spoke in public were my boyfriend (with whom I interact frequently throughout the day, when I am away from my dorm), friends (who I am likely to call to make plans or discuss homework), and my sister (who prefers to talk on the phone between 2 PM and 8 PM, when I am going to and from classes and meetings).

3c) What if anything does this say about how new media may change the composition of our social networks?

New media are used to communicate with people while both/all parties to the interaction are not together. Thus, “new media” would appear to be used regardless of context. Nevertheless, it appears that these new media are often used in private, to both (a) communicate with others who the communicator cannot see and (b) communicate away from the presence of a public who is not involved in the conversation.

Consequently, new media are used to seek out certain strong ties rather than speak with ties of all strengths into whom someone might run in the course of a day’s natural activities (ie. on the street). This allows the new media to reinforce stronger ties with specific individuals, an increases privatization.

Nevertheless, although new media could serve as an isolating factor, it did not seem to do so in my case. I was able to speak with many people, even though those individuals were not physically surrounding me. Thus I see new media more as a uniting factor, determined because many interactions did occur between myself and others. Wellman determined that “Cyberspace fights against physical space less than it complements it. Cyberspace is the medium by which people arrange things and fill in the gaps between meetings” (p.247, 2001). Thus, perhaps new media interactions actually supplement our relationships and allow us to have more interactions within our social networks.

Link to Assignment 2, Part 1 Website: Link

Originating Tie Strength

Question #1: What is the strength of the tie between the originating alter and the 2nd alter?

Of the 6 class members for which we have data, no one passed on their folders to extreme ties (either very weak or very strong). Both of the 2nd alters who had strong ties to the originating passed on the folder. Most moderate ties passed on the folder. No weak ties passed on the folder. Thus, “tie strength” appears to positively influence whether or not the folder is passed along. Nevertheless, it is important to recognize that more data points would further illuminate this hypothesis.

Granovetter explained that weak ties are often the sources of information, a type of bridge to groups to whom an individual might otherwise not be connected and from whom an individual might not otherwise get information. It is not possible to determine whether weak ties or strong ties helped move the Polley folders closer to the target, since that data is missing. However, based on the very limited data from the Yoon group, it appears that weak ties between the originating and 2nd alters were less effective than strong ties (since of the chains that were completed, 67% began with weak ties and 80% began with strong ties). Such a finding is consistent with Wellman’s argument that people “appear to get most of their social support – of all kinds – through their small number of strong ties” (p.566). In this case, the social support is coming in the form of “small services,” since passing on the folder was often seen as a favor.

Size of “Small World”

Question #2: What is the average number of links for both (a) completed and (b) incomplete chains?

Completed Chains: 4 1/2
Incomplete Chains: 2 1/3

Both these numbers are lower than the “6 degrees of separation” found by Milgram and Milgram/Korte. Nevertheless, our class’s findings are consistent with Stevenson et al.’s finding that “Small world studies in organizations have shown, given the relatively clear boundaries in organizations, the number of intermediaries between a starter and target is smaller.” Clearly, the University of Pennsylvania contains fewer people than the United States, and this comparison is reflected in the degrees of separation.

An interesting point is that our results demonstrate that, somehow, everyone in the class is connected to Polley. Thus, the theory mentioned in Milgram’s study that perhaps there are clusters of people in the world who are not connected to each other does not apply here. To explain, 2 folders did reach Polley, demonstrating that any of the folders that did not reach him could have originally been given to either Classmate r45 or Classmate g10, allowing the folder to reach Polley along either of the chains emanating from these two people.

Question #3: What percentage of folders reached the target?

2 out of 8 folders (25%)

This low number could be explained because of the strict organizational structure of the university and thus the relative unreachability of the target. In other words, it is likely that Communication majors were largely distanced from a lab technician at the Wistar Institute in terms of age, affiliation, field of study, and (for most people) gender.

In addition, the apathy of students translated into a failure to pass on the folder. Indeed, much anecdotal evidence indicates that alters complained that passing on the folder was “too much work.” In addition, many participants, feeling that they had no idea how to get a folder to someone in an institute as obscure as the Wistar Institute, neglected to continue the chain.

Our number, 25%, is similar to the 28% (44 out of 160) chains that traveled from the starting point in Nebraska to the target in Milgram’s study (p.65).

Homophily

Question #4: Is there “gender” homophily among chain links?

Whereas females passed folders on to alters of the same sex approximately as often as they passed folders on to males (53% versus 47%), the males in the study demonstrated slightly more gender homophily in their choice of people to whom to pass the folder (62.5% versus 37.5%). However, the sample size appears to be too small to make any sweeping statements. Nevertheless, these findings do differ from the finding by Stevenson et al. that “women relied more on homophilous ties to pass folders compared to men.”

In our study, the percentage of transfers to the same gender was 56.52% (55.56% for completed chains and 57.14% for incomplete chains). McPherson argues that “[u]ntil men and women enter the sex segregated voluntary association structure and labor force, most sex homophily is created by inbreeding rather than baseline homophily” (p.422). The results here do demonstrate a roughly equal split between homophilous and heterophilious transfers, demonstrating only a slight impact of this inbreeding homophily.

Question #5: Is there “affiliation” homophily among the chain links?

The folders appeared to be passed largely among students. In fact, folders were only passed to non-students 5 times, and of those 5 times, (a) 2 times occurred when the folder was given to the target person, (b) 2 times, the folder was passed back to a student, and (c) 1 time, the faculty member failed to pass on the folder. Perhaps such decisions were based on familiarity, trust, or convenience.

The abundance of student-to-student passings is indicative of what McPherson termed “status homophily” (p.419), in which people base their similarity on informal, formal, or ascribed status. Indeed, the high number of student-to-student transfers is consistent with McPherson’s comment that “[p]eople who are more structurally similar to one another are more likely to have issue-related interpersonal communication” (p.428). Thus, they are more likely to have had the requisite “several conversations … outside the classroom” (Assignment #2, Part #1) necessary to allow for the formation of a link in the chain.

These results run counter to Korte’s finding that “[t]he target typically occupied a lower status than that of the person who forged the final link” (p.105). Both individuals who forged the final link were students, whereas the target person was staff.

Question #6: Is there “years at Penn” homophily among the chain links?

9 folders were passed on to people who had been at the University of Pennsylvania for the same number of years (same class).
8 folders were passed on to people who had been at the University of Pennsylvania for more years (upper classes).
6 folders were passed on to people who had been at the University of Pennsylvania for fewer years (lower classes).

It appears that people passed folders on to “upper classes” and “lower classes” equally as often (especially considering that 2 of the 8 folders passed on “upper classes” went to Polley). There is a slightly higher incidence of people passing folders on to people in the “same class.” Such results differ from those found by Stevenson et al., who found that no one passed folders on to lower classmen.

Question #7: Is there “school” homophily among the chain links?

Of the 23 links, 16 (70%) were passed on to people in the same school. Such a finding suggests that members of the University of Pennsylvania community may have more interactions with people in the same school as themselves. If this stark division truly exists, it may account for the difficulty students found in reaching an individual who is removed by many degrees (graduate school, affiliate institute) from undergraduate Communication majors.

Such a gap can be liked, in a way, to the gaps that Korte found along racial lines. In our study, it appears that the folder had trouble crossing school lines, whereas in Korte’s study, the folder needed to cross racial lines. In both studies, the cross between groups occured very close to the target person (in terms of the number of links).

Question #8: Is there “department” homophily among the chain links?

In contrast to the high incidence of school homophily, there appears to be a remarkably low incidence of department homophily. Only 5 ties were between people of the same department (2 Biochemistry, 1 Communication and Political Science, 2 Wistar), and 2 of those ties were from one person to Polley. Thus, one can determine that departmentally homophilous ties were effective in completing the chains. Nevertheless, they were not too common along the path of the chain for either completed or incomplete chains

Time

Question #9: How much time does it take between “passings”? Does this differ for completed chains versus incomplete chains?

This data is incredibly flawed, and identifying the flaws may be more telling that analyzing the numbers. For example, the fact that there was both missing data (N/A) and negative numbers demonstrates that our method of determining the number of days between the time a person received a folder and the time he or she passed it on is inherently flawed. In addition, I know that my 2nd alter collected and mailed the cards for himself, Alter #3, and Alter #4. Such beneficence further skews the data. Finally, it is easy to conceive of someone passing on the folder on a different day from the day that he or she mailed the postcard (and the post office stamped it). Thus, the time advance or the time lag further renders this data inconclusive.

It is interesting to note that the chains which were completed had an average “days between ‘passings’” of 2 and 7. Of the other chains, 1 had fewer days than “2 days,” 1 had more days that “7 days,” and the other 4 had between “2 days” and “7 days.” Thus, it appears that completed chains were basically representative of all chains, allowing nothing to really be determined about the difference between “days between ‘passings’” for completed and incomplete chains.

Path of Folder

Question #10: Is there a funnel shape to the folders’ path (meaning that they all converge on a couple of individuals)?

Our data cannot comment on this topic, since only 2 chains were completed, and each of them had a different “2nd to last alter.” Nevertheless, it is interesting to note that the “Susan Yoon” group had 8 completed chains which came through 7 different “2nd to last alters.” Thus, in our two studies, there appears to be no demonstration of the strong funneling effect of which Stevenson et al. and Milgram (p.66) spoke.

Challenging My Own Initial Assumptions

Question #11: Was Dave the best person to originally pick?

As Killworth said, “[I]naccuracy in selection of small world chain intermediaries is predominant” (p.95). After passing my folder to Dave in belief that his work in a laboratory in the School of Medicine would put him (and therefore my folder) en route to Polley, I learned that my two friends Adam and Laura had much better connections to the Wister Institute. Adam mentioned knowing people in the Wister Institute, whereas a simple Facebook search revealed that Laura actually works in the Wistar Institute. I thus demonstrated inadequate choice in both “path accuracy” (Killworth, p.92) and “next person accuracy” (Killworth, p.93). Dave’s stories about having trouble finding someone who would accept the folder – since many potential 3rd Alters felt that they did constitute adequate “next choice accuracy” (Killworth, p.93) – demonstrates an inadequate ability to macroscopically assess the optimal route.

Question #12: Did the folder pass through the chain I had originally anticipated?

I had originally predicted that the folder would go from Dave to his boss at the Medical School to an official at Wistar to Polley. However, I soon learned how unfamiliar I am with the structure of laboratories at the University of Pennsylvania. Dave’s boss did not know anyone who he felt would bring the folder closer to Polley. The other people in Dave’s lab did not feel like they knew anyone either. Not only did Dave thus have trouble finding someone who would accept the folder, but after the three links which Dave oversaw, the folder actually never left his lab.

General Reflections

Question #13: What are some possible causes for the relative success or failure of folder delivery to the two targets based on the aggregate results?

1. Obscurity of Target. Based on the success of the Yoon folder and the failure of the Polley folder, one might conclude that a lab technician is more obscure than a professor in the Education School. Indeed, Stevenson et al. mentioned that “[s]mall world studies in the organizational setting have shown that barriers between professional groups exist and these barriers make it difficult for SW folders (and other communication) to cross these barriers.” Whereas Yoon is an educator, Polley is a lab technician (staff). As students, we are more able to reach an educator, even if she is in a different school, than a lab technician; such a result may be because of our familiarity with the structure of the educational system at the University of Pennsylvania.

2. Macroscopic Insight of Yoon Team. Perhaps the Yoon team picked “better” people to whom to pass their folder. “Better” could be defined by (a) more dedicated, (b) more strategically positioned, or (c) closer ties, (since 63% of completed Yoon folders were “Strong” or “Very Strong” ties).

Question #14: What are some possible reasons for my folder not being delivered?

1. The alters did not care about my project. [This is a true fact, as Dave (Alter #2) said that he had to mail Alter #3’s and Alter #4’s postcards.]

2. People felt that they could not reach Polley, as demonstrated by the fact that Dave had to ask a number of people to participate in the project before he could find someone who agreed to accept the folder.

3. I picked the wrong 2nd Alter (since, as mentioned above, I should have picked Laura or Adam).

Class Results and Stevenson et al. Study

Question #15: What are some of the similarities between class results and the Stevenson et al. study?

1. University Setting

2. Degrees of Separation. Both the Stevenson et al study and our study found fewer than Milgram’s 6 degrees of separation for completed chains. (See Question #16, Part 4, below).

3. Response Rate. Stevenson et al. had a response rate (measured by completed chains) of 27%. Our (Polley) study had a response rate of 25%.

Question #16: What are some of the differences between the class results and the Stevenson et al. study?

1. Class Homophily. Stevenson et al. found that undergraduates were (a) most likely to pass folders on to people of the same class and (b) not at all likely to pass folders on to people in a lower class. Our study of the Polley folder found (a) only a slight favoritism for passing the folder on to people in the same class and (b) the occurrence of people passing the folder on to people in lower classes.

2. Gender Homophily. Stevenson et al. found that women tended to pass the folder on to other women whereas men did not favor homophilous ties as much. Our study found no strong evidence of a preference for homophilous ties.

3. Funneling Effect. Stevenson et al. found that the “folders converge[d] on a small number of sociometric ‘stars’ before reaching the target person.” Our study found no such funneling effect in either the Polley or the Yoon group.

4. Number of Links. Stevenson et al. found that completed chains had an average of 1.25 links. The Polley and Yoon groups found that completed chains had an average of 4.5 and 3.25 links, respectively. Nevertheless, it is important to note that all three of these numbers are smaller than Milgram’s 6 degrees of separation.

5. Postcards. Stevenson et al. discusses how their decision not to attach postcards for intermediaries to send in caused them to have “received much less information on the links between individuals in the chains of communication.” Our study’s methodology included postcards and thus gave us more of the “richer set of data” which Stevenson et al. wanted.

6. Originating Alters. Stevenson et al. gave the folders to people in all grade levels. Our study used our class’s members as originating alters, therefore limiting the scope of the study.