As any parent or educator will likely tell you, the behavior of teens closely linked in a social network will often display many similarities: teens who smoke or drink, for instance, are often friends with other teens who smoke or drink. By establishing and tracking the spread of these behaviors scientifically, we can gain a greater understanding of the mechanisms at work and perhaps harness them to help spread healthy behaviors instead of unhealthy ones.

When we look at two teens who share a common behavior pattern (healthy or unhealthy), we must ask ourselves: Did they become friends because of their similar behavior (selection), did their behavior become similar as a result of being friends (influence), or was there some third factor at work which influenced them both separately (confounding factors)? One simple way to attempt to answer this question is through a longitudinal study, where data is collected for the same group of subjects at multiple times. By looking at the co-evolution of the social network and the behavior network, we can parse out the role that each of these factors plays. Here, I wanted to briefly discuss a few studies which have employed social network analysis and longitudinal data collection to gain a better understanding of how unhealthy behaviors can spread amongst teens.

The first is a study by Ennett and Bauman (PDF) which examines smoking as a function of position in the social network.(1) Specifically, they name three classes of social network patterns: cliques (a small group of at least three adolescents whose primary friendships are with each other), liaisons (adolescents who maintain multiple friendships without being in a particular friendship clique), and isolates (adolescents who have relatively few friendships with others.) Past research showing that cliques tend to share smoking behaviors leads many people to the assumption that smoking is a primarily peer group phenomenon.  However, after looking at data from 1,092 students collected across 5 schools over 1 year (from the start of 9th grade to the start of 10th grade), the authors found that smoking was far more common among isolates (17-40% across schools) than among clique members (4-16%).  Additionally, within the 87 cliques identified, they found that smokers tended to associate in the same cliques, with the majority of cliques composed entirely or almost entirely of non-smokers. In looking at the roles played by influence and selection, they indicate that both processes contributed equally to similarity in smoking behaviors for clique members, though they do not discuss how they performed their analysis.

The next paper described a 2009 study by Mercken, et al. (PDF) which utilizes a “stochastic actor-based model” to help in separating the roles of influence and selection “by simultaneously representing changes in friendship network structure and changes in smoking behavior among adolescents.” (2) In this study, they interviewed 1326 subjects from 11 Finnish schools 4 times over the course of 30 months (starting at the beginning of 7th grade). Each time, they asked about their friendship ties, their smoking behavior, that of their families, and their alcohol consumption. They found that adolescents who smoked more had a tendency to choose friends who likewise scored high on smoking behavior. Adolescents who smoke less than one cigarette per week were most likely to make friends with classmates who don’t smoke at all, while the most attractive potential friends for those who smoke one or more cigarettes per week were those who smoked at the highest rate. The authors did not report findings regarding the data collected on alcohol consumption, which hints at the fact that the patterns of spread may be different for different behaviors.

A natural question at this point is: are all friends created equal? Most of us growing up had a “best” friend in addition to our peer group. A 1997 study by Urberg, et al. (PDF) attempts to parse out the influence of close friends vs. that of one’s peer group as it pertains to both cigarette smoking and alcohol use.(3) In this study, they collected data from 1,028 Mid-western school-children in the 6th, 8th, and 10th grades; data was collected in two waves, once in the Fall and once in the Spring, and included assessments of friendship ties as well as cigarette and alcohol use. Interestingly, they found that it was smoking behavior of the peer group and not the close friend which predicted a transition into cigarette use, while it was the drinking behavior of the close friend and not the peer group which predicted a transition into alcohol use. They also found that those who have tried cigarettes or alcohol are more likely to know current users than those who have not (echoing the Mercken findings above).

Finally, because I couldn’t close a post on social networks and health without a Christakis/Fowler study, I wanted to mention a study from March of this year (2010) from Mednick, Christakis, and Fowler (PDF), which examined the interaction of two separate behaviors–low sleep and drug use–within a social network. (4) Looking at a sample of 8,349 adolescents from the ADD Health Data Corpus, they presented a number of interesting findings. First, they found that an individual’s behavior is correlated with the behavior of others in their network up to 4 degrees away. In the case of sleep, an individual was 29% more likely to sleep 7 hours or less if they had a friend who sleeps less than 7 hours; a friend of a friend correlated with a 17% increase, all the way down to a 5% increase for the friend of a friend of a friend of a friend. In the case of marijuana use, a direct connection to a user resulted in a 190% increase in the likelihood of use, while a 4th-degree connection still correlated with a 11% increase in use. Another interesting finding was that individuals central in the network were more likely to sleep less, with a two standard-deviation increase in centrality increasing the probability of sleeping 7 hours or less by 13% (controlling for other factors). Finally, they report on the interrelation between these behaviors, claiming that having a friend who slept 7 hours or less actually correlated with a 19% increase in smoking marijuana.

These 4 papers served as a useful introduction to both the methods of social network analysis and some of the interesting findings as they pertain to health behaviors and teens (across a number of behaviors – sleep, smoking, drinking, drugs). It is important for educators and health professionals to have an understanding of the social mechanisms as these will likely be a critical factor in preventing unhealthy behaviors from spreading amongst teens and persisting in their lives. Perhaps these same mechanisms can be used to spread positive behaviors such as exercise and civic-mindedness. In addition, it will be interesting to see how methods like these can be applied on a larger scale to Twitter or Facebook-sized social network corpora to track the spread of behaviors, ideas, diseases, and more across entire states or countries.

References:

1. Ennett, S.T. and Bauman, K.E. (2000). Adolescent social networks: Friendship cliques, social isolates, and drug use risk. In Hansen, W.B., et al. (eds) Improving prevention effectiveness. Tanglewood Research, Inc. Greensboro, NC.
2. Mercken, L., et al. (2009). Dynamics of adolescent friendship networks and smoking behavior. Social Networks.
3. Urberg, K.A., Değirmencioğlu, S.M., and Pilgrim, C. (1997) Close Friend and Group Influence on Adolescent Cigarette Smoking and Alcohol Use. Developmental Psychology, vol 33(5), pp. 834-844.
4. Mednick, S.C., Christakis, N.A., Fowler J.H. (2010) The Spread of Sleep Loss Influences Drug Use in Adolescent Social Networks. PLoS ONE vol 5(3): e9775.

Also, I want to thank Sarita Yardi and Vladimir Barash for directing me towards some of these papers.

Here’s a quick list of the papers mentioned:

• “How Medical Expertise Influences Web Search Interaction” [1] and “Characterizing the Influence of Domain Expertise on Web Search Behavior” [2] by Ryen White, Sue Dumais, and Jaime Teevan.  This poster abstract and longer paper present a large-scale, log-based analysis of web searches in 4 domains (Medicine, Finance, Law, and Computer Science), looking specifically at how domain experts differ from non-domain experts in terms of search behavior.  The data for the study were extensive, comprised of a sample of URL visits from users of a browser toolbar over the course of a 3-month period and representing “more than 10 billion URL visits from more than 500 thousand unique users.”
• “Knowledge in the Head and on the Web: Using Topic Expertise to Aid Search” [3] by Geoffrey Duggan and Stephen Payne.  This paper looks at the role of domain expertise in predicting search performance for people searching within their domain of expertise.  The study involved asking 34 university students trivia questions on two topics – Football (they meant to write “Soccer”) and Pop Music – and asking them to answer, first using only their own knowledge, and then again with the help of the Internet.
• “Web search behavior of Internet experts and newbies” [4] by Christoph Holscher and Gerhard Strube.  This is a somewhat earlier paper focusing on identifying the search strategies of internet (search) experts, and then using that ifnromation to help compare the effects of serach expertise and domain expertise on search performance.  In the first study, they had 12 internet experts first do a mental walk-through of their search strategies and then carry out real search tasks using a teach-aloud/think-aloud sort of protocol.  In the second study, they had 24 university students conduct web-based search tasks pertaining to economics – they were divided by domain expertise (half were economics students) and search expertise (assessed by interview and pre-test).
• “Domain knowledge, search behaviour, and search effectiveness of engineering and science students: an exploratory study” [5] by Xiangmin Zhang, Hermina G.B. Anghelescu, Xiaojun Yuan.  This paper examined the relationships connecting domain knwoledge, search behavior, and search effectiveness.  The study established the domain knowledge of 22 engineering studies through familiarity with terms from an engineering thesaurus, and then had them search on 3 assigned topics.

The papers that focused on quantifying search behavior showed that domain experts tended to do more exploration overall than domain novices.  [2] found that they issued more queries, they branched more (branching defined as stepping back to a previous page and then moving forward to a new page), they visited a larger number of unique domains, and they spent a longer time overall per search tasks.  [5] also found that domain experts tended to issue more queries (34.64 queries/subject vs. 20.09) over the course of their search tasks.  In addition, the domain experts explored this space faster: [3] found that greater topic expertise led to less time spent per page and to faster decisions about whether or not to stop a line of inquiry, a finding corroborated by [4].

One interesting point of disagreement involved the length of queries.  [2] pointed to past literature demonstrating that domain experts tended to issue longer queries and more technical query terms, a result replicated in their study.  Longer queries were seen from domain experts in [5], as well (4 terms/query vs. 2.86).  [3], however, found that the domain experts studied used shorter queries than the domain novices, contradicting these other studies (though the scope of consideration was restricted greatly to come to this conclusion – they looked at just 2 of the football questions).  In the first study from [4], when the search behavior of the internet experts was compared against search logs from the Fireball Search Engine, it was found that the internet experts use longer queries (3.64 vs. 1.66 words), but in the second study, those with domain knowledge were found to make shorter queries than those without (1.97 vs. 2.96 words).

The papers that attempted to highlight specific search strategies also revealed some interesting differences.  [2] examined the domain suffixes of the sites visited, and noticed that domain experts tend to visit different types of sites than domain novices.  Computer Science experts, for instance, were more likely to visit *.org or *.edu sites than novices, while novices were more likely to visit *.com sites, representing that experts might be more familiar with academic or industry sites, while novices might be more familiar with consumer-oriented commercial sites.  [4] also found that so-called “double experts” (those with Internet AND domain expertise) tended to navigate directly to “go-to” sources of information, while all other groups were more likely to start with search engines.

Regarding overall performance, it is perhaps not surprising that domain experts performed better in all search tasks.  [3] attempts to distinguish between searching within one’s domain for information already known vs. searching within one’s domain for information that one doesn’t already know, and found that domain experts perform better in both scenarios.  Because [2] did not control user tasks, they coded successes as logged searches where the final click was a URL and failures as when the final click was another search.  Given this coding, they found that experts were more successful than novices when searching in-domain, but that these same experts performed the same as novices when searching for information out of their domain of expertise, highlighting the difference between domain expertise and search expertise.

Some of the interesting questions that come out of this field of research relate to how we can transfer the advantage that domain (and search) experts have to novices.  One possible method is to pin down what these experts are doing that helped them perform better and attempt to work these strategies into instruction.  Obviously, Internet search skills are already immensely important, and I would hope that this would trickle down into educational curricula (if they haven’t already).

Domain experts find information faster because their expertise in the space allows them to identify relevant information faster and to build off of it.  But, for those of us who are attempting to use the web to learn things on our own, there is a serious boot-strapping problem here.  As someone who is mostly self-taught when it comes to programming, I know how difficult it is to face the problem of searching for information when you are not entirely sure what to search for.  Once you get over the initial learning curve, it becomes much easier.  For those of us interested in building new technologies, here is a challenge: How can we create tools that support domain novices by doing this bootstrapping for them?

If we can find a way to identify domain novices and present them with useful information such as definitions or important “go-to” sources, we can significantly speed up their learning so that they can more quickly fend for themselves.  Faceted search tools such as MrTaggy take a solid step towards tackling this problem by providing searchers with additional cues that provide context.  More detailed study is needed regarding how to connect the gaps in which domain novices get lost – as better tools become available for providing socially or computationally-derived contextual data, it will be interesting to see what technologies evolve to support these needs.

Links for Papers Above (Most from ACM Digital Library):

• [1] “How Medical Expertise Influences Web Search Interaction” by Ryen W. White, Susan Dumais, and Jaime Teevan in Special Interest Group on Information Retrieval (SIGIR) 2008.
• [2] “Characterizing the Influence of Domain Expertise on Web Search Behavior” by Ryen W. White, Susan Dumais, and Jaime Teevan in Conference on Web Search and Data Mining (WSDM) 2009.
• [3] “Knowledge in the Head and on the Web: Using Topic Expertise to Aid Search” by Geoffrey B. Duggan and Stephen J. Payne in Conference on Human Factors in Computing Systems (CHI) 2008.
• [4] “Web search behavior of Internet experts and newbies” by Christoph Holscher and Gerhard Strube in Computer Networks 33 (2000), pp.337-346.
• [5] “Domain knowledge, search behaviour, and search effectiveness of engineering and science students: an exploratory study” by Xiangmin Zhang, Hermina G.B. Anghelescu, and Xiaojun Yuan in Information Research 10(2), Jan 2005.

Some Other Reading on this Topic:

• “The Effects of Topic Familiarity on Information Search” by Diane Kelly, Colleen Cool in Joint Conference on Digital Libraries (JCDL) 2002.
• “Domain-Specific Search Strategies for the Effective Retrieval of Healthcare and Shopping Information” by Suresh K. Bhavnani in Conference on Human Factors in Computing Systems (CHI) 2002.
• “The Effects of Domain Knowledge on Search Tactic Formulation” by Barbara M. Wildermuth in Journal of the American Society for Information Science ant Technology, 2004.