Content-Driven Analysis of an Online Community forSmoking Cessation: Integration of Qualitative Techniques,Automated Text Analysis, and Affiliation NetworksSahiti Myneni, PhD, MSE, Kayo Fujimoto, PhD, Nathan Cobb, MD, and Trevor Cohen, MBChB, PhD

Epidemiological evidence indicates that modi-fiable risky health behaviors place a substantialsocioeconomic burden on human health andwellness.1 Understanding human behavior inreal-time settings is essential to improvinghealth outcomes related to these behaviors.2,3

Technological advances in connectivity offerthe means to obtain potentially valuable datasets in the form of electronic traces of theactivities of online social communities. Thesedata may help us to understand the intra- andinterindividual intricacies of health-related be-haviors. Studies of online and offline socialnetworks provide valuable insight into socialinfluence, information spread, and behavioraldiffusion.4---6 Most of these analyses have paidmore attention to the frequency of communi-cation between members than to its content.The content, however, is relevant to behaviorchange theories, which address the use ofspecialized content to stimulate and supportindividuals to achieve a desired change.7,8

Contemporary work on social media datararely addresses this fundamental concern ofbehavior change theorists.

Outside the context of online networks,several theories have been formulated to ex-plain behavior change. Some, such as theTranstheoretical Model,9 belong to the intra-personal category; others, such as Social Cog-nitive Theory10 and social network and supportmodels,11 are classified as interpersonal. (Ap-pendix A, available as a supplement to theonline version of this article at http://www.ajph.org, provides an overview of the theoreticalconstructs.) Empirical research on the applica-bility of these models to behavior change ofhealth consumers in the digital era is minimal.12

Recent research showed that participation inhealth issue---specific social networking sitessignificantly influenced social factors such asidentification, perceived subjective norms, and

social support, which in turn resulted in greatersmoking cessation self-efficacy.13 Content inclu-sion in analytical models of social networks canenable us to examine the content-specific pat-terns of social factors underlying behaviorchange. Through mapping of the specific contentto theories, such content inclusion can facilitatethe development of network interventions forhealth behavior changes by harnessing thepower of social relationships.

Studies of QuitNet, an online social networkfor smoking cessation, have examined thestructure of peer-to-peer communication pat-terns and provided insights into social integra-tors and opinion leaders.5,14 Previous workshowed the applicability of affiliation networksto real-world diffusion networks, enriching ourunderstanding of the affiliation-based sourcesof influence on individuals’ behavior. Examplesinclude the diffusion of (1) ratification of theWorld Health Organization Framework Con-vention on Tobacco Control by comembershipwith an online forum among countries15; (2)

gunshot victimization by co-offending with vic-tims among Chicago, Illinois, gangsters16; (3) sub-stance use by coparticipating in school-sponsoredsports or co-identifying with the same crowdtypes17,18; and (4) sexual behavior by coaffiliatingwith venues among male sex workers.19

We used affiliation networks to analyzemessages for content-specific patterns of net-work diffusion. We took an interdisciplinaryapproach, integrating methods from sociobe-havioral sciences, social network analytics, andbiomedical informatics. We employed qualita-tive techniques derived from grounded theory,automated text analysis, and affiliation networkanalysis to investigate the communication pat-terns underlying human behavior in onlineenvironments. Our study had 3 major compo-nents: (1) a qualitative study of human com-munication within user-generated data inQuitNet, (2) computational text analysis tofurther extend this analysis, and (3) identifica-tion of communication patterns pertinent tobehavior change in affiliation networks. We

Objectives. We identified content-specific patterns of network diffusion un-

derlying smoking cessation in the context of online platforms, with the aim of

generating targeted intervention strategies.

Methods. QuitNet is an online social network for smoking cessation. We

analyzed 16 492 de-identified peer-to-peer messages from 1423 members,

posted between March 1 and April 30, 2007. Our mixed-methods approach

comprised qualitative coding, automated text analysis, and affiliation network

analysis to identify, visualize, and analyze content-specific communication

patterns underlying smoking behavior.

Results. Themes we identified in QuitNet messages included relapse, QuitNet-

specific traditions, and cravings. QuitNet members who were exposed to other

abstinent members by exchanging content related to interpersonal themes (e.g.,

social support, traditions, progress) tended to abstain. Themes found in other

types of content did not show significant correlation with abstinence.

Conclusions.Modeling health-related affiliation networks through content-driven

methods can enable the identification of specific content related to higher abstinence

rates, which facilitates targeted health promotion. (Am J Public Health. 2015;105:

1206–1212. doi:10.2105/AJPH.2014.302464)

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anticipate that the insights gained from thisresearch will enhance our understanding ofbehavior change and will have implications forthe design of sociobehavioral interventions thatdraw upon social influence.

METHODS

QuitNet is one of the first online socialnetworks whose purpose is health behaviorchange. It is widely used, with more than100000 new registrants per year.20 Previousstudies of QuitNet found that participation inthe online community was strongly correlatedwith abstinence.21 Our data set came froma previously studied quality improvement da-tabase of de-identified messages in publicthreaded forums, in which participants postmessages and reply directly to each other. Thisdatabase comprised 16 492 public messages(original posts and their replies) exchangedbetween March 1 and April 30, 2007. Allmessages were stripped of identifiers butrecoded for sender ID, receiver ID, self-reported smoking status, date, and positionwithin the thread.

Analysis of voluminous and context-rich so-cial network data requires scalable methods.Qualitative coding provides useful insights intotext data exchanged between QuitNet members.For larger data sets, the qualitative methodneeds to be complemented with an automatedtechnique to optimize resource utilization. Suchan extension enhances the validity and gener-alizability of the manual coding in addition tofacilitating the application of network analysismethods to an entire data set. In our case,affiliation network analysis allowed us to un-derstand the content-specific attributes of net-work diffusion on smoking abstinence. Figure 1provides detailed information about the re-search methods and their rationale.

Qualitative and Automated Text Analysis

We first analyzed the message content ofQuitNet with the qualitative techniques ofgrounded theory.22,23 We employed opencoding, axial coding, and constant comparisonto arrive at concepts and themes that capturedthe behavioral, interpersonal, and individualis-tic concepts expressed in QuitNet communica-tion. We analyzed 585 randomly selectedmessages to derive the themes embedded in

the messages. We analyzed an additional 210messages to ensure thematic saturation.

Two researchers coded a separate data set of100 messages according to the 12 themes thatemerged from the grounded theory analysis.The codes we assigned had a Cohen’s jmeasure of 82%. We resolved disagreement incoding through follow-up discussions. We thenmapped the derived themes to the theoreticalconstructs of behavior change. A full list of theconstructs used in the mapping process can befound in Appendix A. The goal of the mappingprocess was to understand the interplay be-tween the behavior change constructs andQuitNet communication content.

Next, we exploited recent developments inautomated text analysis to measure the extentto which key concepts of interest wereexpressed within messages between QuitNetusers, regardless of the specific terms used toexpress these concepts at the surface level. Weapplied latent semantic analysis, a method ofdistributional semantics,24 in conjunction witha machine-learning algorithm to derive a mea-sure of relatedness between a given messageand the previously identified QuitNet themes toestimate the distribution of different types ofcontent across QuitNet. This use of automatedtechniques facilitates scaling qualitative analy-sis to large-scale social media data sets. This, inturn, mediates the development of a novelapproach to structuring a network in accor-dance with the content of the communicationwithin it, so it can be subjected to adaptations ofexisting network analysis methods.

Affiliation Network Analysis

Finally, we employed 2-mode affiliationnetwork analysis, which uses 2 distinct sets ofnodes (actors [n] and events [V]).25 The firstdistinct set of nodes in our affiliation networkrepresented the set of QuitNet members(n = 1423). The second distinct set representedthe communication themes (V=12). We con-sidered a QuitNet member to be affiliated witha particular theme if a message exchanged bythat member was classified under that theme.We divided the themes into 4 groups accordingto the behavior change constructs to whichthey related: (1) group-centric interpersonal(social support, traditions, motivation), (2)individual-centric interpersonal (progress,virtual rewards, family and friends), (3)

intrapersonal with sociobehavioral beliefs(benefits, obstacles, conflict), and (4) intraper-sonal themes with personal experience stories(cravings, relapse, nicotine replacement ther-apy). Then, we created a user-by-theme 2-modematrix (1423 users by 12 themes) that repre-sented affiliation ties between users and themes,which we used for subsequent analyses.

Our smoking behavior measure was thereported abstinence quotient (RAQ), an aggre-gate measure of an individual’s reportedsmoking behavior. We determined the RAQ byaveraging the abstinence status self-reportedby QuitNet members every time they logged into the system over a period of 60 days, whichgave us an estimate of a member’s self-reportedabstinence status during this period. The RAQcould range from 0 to 1; 0 indicated thata member did not report a period of abstinenceduring the study period, and 1 indicated thata member stayed abstinent throughout. Thehigher the RAQ, the higher the proportion ofself-reported status updates that reported ab-stinence from smoking.

We generated a visual representation of theaffiliation networks between members andcommunication themes derived from actualuser communication in the QuitNet commu-nity. We used these networks to identify themajor content types connecting the QuitNetmembers by estimating the degree centralitymetrics. We constructed graphs with the Net-Draw plugin of UCINET 6.26

We used the Affiliation Exposure Model tomeasure the extent to which individual userswere exposed to other abstinent membersthrough affiliating with at least 1 commoncommunication theme.17 The resulting expo-sure values ranged from 0 to 1, which weused as the main explanatory variable in ourregression analysis. In addition, we useda1-mode Network Exposure Model to measurethe extent to which users were exposed toabstinent members, according to their peer-to-peer communication network.27---29 A commu-nication network was represented by an adja-cency matrix that recorded the number ofdirect message exchanges among QuitNet usersduring the study period. Technical details onthe 1-mode and 2-mode network exposuremodels are provided in Appendix A.

We estimated a Network AutocorrelationModel by including an affiliation exposure

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term30 and a 1-mode communication networkexposure term. We treated the former as ourexplanatory variable and the latter as one ofour controlled variables. Other controlled var-iables in our model were gender, age, andnumber of themes each user affiliated with.

The network autocorrelation model handlespotential violations of correlated errors com-monly observed in network data in the param-eter estimation process.31,32 We employed thesna package in R, an open-source statisticalanalysis software for this purpose.33 The resultsindicated that 43% of our affiliation exposurevalues were unity (full exposure to abstinentusers). To address this issue, we includeddummy variables that indicated nonunity ex-posure values versus unity exposure values.34

RESULTS

More than three fourths of users in our dataset were female, and the mean age was 44.3

years. Around 70% of the users had a self-reported RAQ of 1, and 22% had a 0 RAQ.On average, a user posted 22.4 messages. Thedifference between the RAQ=0 and RAQ=1groups was statistically significant for gender(2-sided v2 = 335.07; P< .05), but not signifi-cant for age and posting frequency.

Qualitative Analysis

We identified 43 different concepts, groupedunder 12 themes (e.g., traditions, obstacles,progress). A complete list of the themes, theirdefinitions, and sample messages can be foundin Appendix A. The identified themes related tothe constructs of existing behavior change the-ories. Detailed description of the theoreticalmapping process is beyond the scope of thisarticle, but is described in detail elsewhere,35

and we give an example here.Stimulus control, a construct from the Stages

of Change Model,9 involves the use of re-minders that encourage healthy behavior as

a substitute for unhealthy behavior. For earlymorning smokers, QuitNet has a traditionwhereby members post messages describingearly morning weather and reaffirm theircommitment to stay abstinent. Examples ofother traditions are (1) bonfire, a virtual eventhosted regularly in which members bring theirunsmoked cigarettes and throw them into a fire,which facilitates observational learning, and (2)pledge, a ritual in whichmembers extend a handto the next member, indicating their commit-ment to staying abstinent and providing helpingrelationships. Community-driven activities suchas these traditions organized in QuitNet aremanifestations of behavior-related theoreticalconstructs in online settings.

Similarly, we mapped other themes to thetheoretical constructs. The alignment washighest for traditions, with 13 constructs, fol-lowed by relapse, with 9 constructs. This resultindicated that the member-generated group-centric strategies, such as bonfires and pledges,

QuitNet messages

Smoker

Ex -smoker

Grounded theory analysis

• Open coding

• Axial coding

• Constant comparison

Analysis method

Explains reason behind

choice of method/material

• Local culture• Microscopic view

Output

QuitNet concepts

and themes

Method application

Results derivation

Automated

text analysis

• Voluminous data• Less labor

intensive

• Semantically related

QuitNet members

• Network-amenable

affiliations

Social network analysis

to measure content-

specific communication

patterns

Smoker

Ex -smoker

Analysis method

Explains reason behind

choice of method/material

Output

Method application

Results derivation

Legend

• Incorporate content into the analytic framework of online social networks

Subset

Full set

FIGURE 1—Overview of research strategy in analysis of QuitNet user messages and smoking cessation.

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that formed traditions were associated withmore theoretical constructs than were otherthemes. It is important to note that no singlebehavior change theory provides a basis for allof the themes that emerged from the QuitNetmessages.

Automated Analysis

We processed our entire QuitNet database,consisting of 16492 messages, with an auto-mated classification system, the Semantic Vec-tors package,36 developed through methods ofdistributional semantics.14 For each unanno-tated message, the system provided a ranked listof themes derived from the nearest neighborsfrom the annotated set of messages. The pro-gram produced a score for a particular theme byadding the relatedness measures of the nearestneighbors that corresponded to that theme.

The reliability measures calculated by usingCohen’s j indicated that the average agreementof the system with human raters was 0.71.(Appendix A shows reliability measures foreach theme.) Figure 2 illustrates the distribu-tion of the QuitNet communication themesderived from automated analysis. Havingestablished user---message---theme connections,we were able to analyze the QuitNet data setfor discernible relationships between smokingstatus and communication themes.

In addition to determining the distribution ofthe QuitNet communication themes, the large-scale qualitative analysis facilitated by theautomated methods allowed us to quantify thetheoretical constructs embedded in unanno-tated QuitNet messages. The results indicatedthat the QuitNet messages facilitated thetransmission of behavior change constructs,such as emotional coping responses, helpingrelationships, and self-efficacy.

Affiliation Network Analysis

Figure 3 illustrates the affiliation networksformed by the 1423 users and the 12 themesrepresenting QuitNet communication. Accord-ing to their degree centrality, social support andcravings were the most prevalent themes em-bedded in the communication among QuitNetusers who were self-reported smokers duringthe study period. Progress was the theme iden-tified most often in the messages communicatedamong QuitNet users who were successfulquitters (i.e., they changed their self-reportedstatus from smokers to ex-smokers during thestudy period). Social support was the mostcommunicated theme amongQuitNet users whorelapsed 1 or more times, and traditions was theleading theme among messages communicatedamong QuitNet users who self-reported asex-smokers during the study period.

The effect of affiliation exposure to abstinentcoaffiliates on abstinence was statistically sig-nificant for interindividual themes, accordingto a 2-tailed test at the a=0.05 level (Table 1).The autocorrelation parameter estimateshowed that as QuitNet users were moreexposed to other users who reported absti-nence through sharing the group-centric in-terpersonal themes, they were more likely tostay abstinent themselves (b = 0.038; P< .01),after adjustment for age, gender, and commu-nication network exposure to abstinent mem-bers. Similarly, as QuitNet users were moreexposed to other users who reported absti-nence by engaging in communication related toindividual-centric interpersonal themes, theywere more likely to be abstinent themselves(b = 0.078; P< .05).

Among intrapersonal themes, the parameterestimate indicated that members who were moreexposed to other abstinent QuitNet membersthrough sharing of personal experiences weremore likely to abstain themselves. The numberof themes embedded in the messages a QuitNetmember exchanged in the intrapersonal beliefcategory was significant and indicated that themore themes a member exchanged throughmessages in this category, the more likely it wasthat the member abstained from smoking. It isimportant to note that the significance of thenumber of themes was not a network construct.The number of themes, per se, was not signifi-cant for the rest of the thematic groups. How-ever, their affiliation exposures were significant.Results of 1-mode network exposure effect weresignificant for interpersonal themes (social sup-port, progress, traditions) and intrapersonalthemes encompassing sociobehavioral beliefssuch as benefit and obstacles.

DISCUSSION

To our knowledge, ours was among the fewstudies that have attempted to explain theunderexplored area of network diffusionaccording to affiliation exposure in the contextof online communities for smoking cessation.Our work provides new techniques to betterpersonalize interventions by our use ofcontent-inclusive network analysis of onlinepeer-to-peer exchanges.

Our study provides public health researchersand interventionists with new ways to tailor

2 41 84 92 102 243 523 16071909

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e C

ou

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in

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Theme

Conflict

Family

and fr

iends

Nicotine re

placem

ent thera

py entri

es

Virtual r

eward

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Obst

acles

Relapse

Teachable

mom

ents/b

enefits

Motiv

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Cravin

gs

Progre

ssTra

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Social s

upport

FIGURE 2—Distribution of themes in QuitNet user messages, derived from automated

analysis.

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sociobehavioral intervention programs. Wefound that engaging in peer-to-peer commu-nication involving specific kinds of content isrelated to smoking cessation. This finding caninform the design of targeted interventionsthat encourage people to sustain positivebehavior change, which sets the stage fora new generation of empirically groundedinterventions in public health. Our interdisci-plinary method integrated qualitative coding,automated text analysis, and affiliation net-work analysis to provide further insight intothe structural features underlying health be-havior in social networks. Inclusion of content

attributes in network models of QuitNetresulted in valuable insights into communica-tion patterns that may enable us to bettertailor network-related interventions. Exam-ples of these approaches include identifyingcontent-specific key players and engineeringmentor---mentee matches according to userneeds.

We examined the structural aspects ofQuitNet members’ content-based affiliationwith different kinds of communication contentas a means to investigate content-based net-work diffusion. Unlike the majority of studieson social networks, where the ties between

actors were deductive in nature,37 our studytook an empirical, bottom-up approach toexplaining the communication patterns withinQuitNet. We derived themes from groundedtheory---based qualitative analysis of QuitNetmessages. Our findings suggest new directionsfor developing network interventions38 forpublic health by focusing on content-based,targeted behavior change strategies. In addi-tion, our research shifts intervention tailoringfrom an exclusive reliance on theory39,40 to anempirical approach driven by the characteris-tics and needs of end users, as revealed by theircommunication content.

Note. Green circles represent QuitNet users who have maintained successful quitting with abstinence status “1.” Lime green circles represent QuitNet users who have changed their status from

smokers to ex-smokers. Light red circles represent QuitNet users who have relapsed (changed their status from ex-smokers to smokers). Yellow circles represent users with multiple relapses (multiple

changes in self-reported abstinence status). Blue squares represent the QuitNet communication themes.

FIGURE 3—Affiliation networks formed by QuitNet users and themes in their communication.

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Limitations

Our QuitNet data set was recorded in 2007and was limited in size. Future studies shouldobtain more recent data. The accuracy of theautomated system may be further improved bysophisticated choice of machine-learning algo-rithms.

Our 2-mode network analysis also had a fewlimitations. First, although themes were empir-ically derived from the QuitNet data set, wemight not have captured all of the communi-cation themes. Network diffusion throughcontent-based affiliation may not have beenlimited to these themes. Second, our analysis

was cross-sectional and not longitudinal,which might have limited the understandingof causality of content affiliation to abstinencebehavior as well as to potentially dynamicpatterns of content affiliation in a network.Third, our analysis considered only onlinecommunication that occurred on the QuitNetplatform. Some of these QuitNet membersmight have had offline communication,41 andprevious research indicates that both areimportant in influencing behaviors.42 Futureresearch should consider both in-person andonline communication that occurs among in-dividuals.

Finally, the autoregression models we useddid not account for the dependency of ties in thedata. Modeling the networks with exponentialrandom graph models is 1 way to considernetwork dependency.43---45 However, complex-ity of estimation and simulation of these modelscause some practical difficulties in achievingmodel convergence, especially for large net-works like ours. Nonetheless, future work wouldbenefit from the use of exponential randomgraph models to model 2-mode46,47 and multi-level48 networks to address the interdepen-dencies associated with the network data.

Conclusions

Our findings offer insights into the utility of(1) qualitative analysis of QuitNet communica-tion data to understand the manifestation ofbehavior change constructs in the context ofonline platforms, (2) automated text analysismethods to facilitate large-scale analysis ofonline network data, and (3) affiliation net-works to understand the nature of the special-ized content underlying communication eventsin the context of smoking cessation efforts in anonline community.

We found that QuitNet members who wereexposed to abstinent members who exchangedcontent related to group-centric interpersonalthemes (e.g., social support, traditions) tendedto remain abstinent. This result can be used tobuild better online social interventions, withenhanced persuasive and engagement features.For instance, incorporation of an explicit dis-play of member profiles that contribute suchcontent may enhance affiliations to these peo-ple and, thereby, facilitate meaningful connec-tions that harness network diffusion and affil-iation exposure.

Online social networks have been gainingin popularity and present health researcherswith a unique opportunity to understandhuman behavior change and to deliver scal-able and sustainable interventions. The de-velopment of better tools to analyze socialnetwork content of this nature allows usa greater understanding how such socialnetworks mediate behavior change andthereby provides us the opportunity to im-plement empirically grounded interventionsto further help these communities attain theirgoals. Content-based network analysis ofQuitNet, made feasible by large-scale quali-tative analysis with automated methods, hasbeen shown to yield content-specific patternsthat can be used for targeted health promo-tion and behavior change. j

About the AuthorsSahiti Myneni and Trevor Cohen are with the School ofBiomedical Informatics, University of Texas Health ScienceCenter at Houston, and Kayo Fujimoto is with the Divisionof Health Promotion and Behavioral Sciences, School ofPublic Health, University of Texas, Houston. Nathan Cobbis with the Division of Pulmonary and Critical Care,Department of Medicine, Georgetown University MedicalCenter, Washington, DC, and MeYou Health LLC, Boston,MA.Correspondence should be sent to Sahiti Myneni, PhD,

MSE, 7000 Fannin St, Suite 165G, Houston, TX 77030(e-mail: Sahiti.Myneni@uth.tmc.edu). Reprints can beordered at http://www.ajph.org by clicking the “Reprints”link.This article was accepted November 6, 2014.Note. Nathan Cobb is an employee of MeYou Health

LLC, whose parent company owns and operates QuitNet.

ContributorsS. Myneni originated the study design and performedmethodological integration. All authors helped to con-ceptualize ideas, interpret findings, and review drafts ofthe article.

AcknowledgmentsThis study is supported in part by UTHealth Innovationfor Cancer Prevention Research Pre-doctoral Fellowship,University of Texas School of Public Health---CancerPrevention and Research Institute of Texas (grantRP101503).

We thank QuitNet LLC for sharing the data set andTom Landauer for providing us with the TouchstoneApplied Science Associates Inccorpus.

Note. The content is solely the responsibility of theauthors and does not necessarily represent the official viewsof the Cancer Prevention and Research Institute of Texas.

Human Participant ProtectionThe institutional review board at the University of HealthScience Center at Houston reviewed the study anddetermined that it was exempt.

TABLE 1—Affiliation Exposure Among

QuitNet Members and Network

Exposure Computations for Reported

Abstinence Quotient Derived From

Network Autocorrelation Model, 2007

Type of Theme b (SE)

Group-centric interpersonal

Affiliation exposure 0.038** (0.014)

Network exposure 0.021** (0.012)

Age 0.008** (0.0007)

Gender 0.037 (0.027)

Themes affiliated, No. 0.008 (0.018)

Individual-centric interpersonal

Affiliation exposure 0.078* (0.032)

Network exposure 0.092** (0.041)

Age 0.009** (0.0008)

Gender 0.176 (0.032)

Themes affiliated, No. 0.03 (0.038)

Intrapersonal (personal

experience stories)

Affiliation exposure 0.04 (0.023)

Network exposure 0.017 (0.021)

Age 0.003** (0.001)

Gender 0.08 (0.03)

Themes affiliated, No. –0.03 (0.03)

Intrapersonal (sociobehavioral

beliefs)

Affiliation exposure –0.02 (0.022)

Network exposure 0.034 (0.016)

Age 0.008** (0.002)

Gender 0.08 (0.06)

Themes affiliated, No. 0.18** (0.049)

Note. The sample size was n = 1423.*P < .05; **P < .01.

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References1. Global Health Risks: Mortality and Burden of DiseaseAttributable to Selected Major Risks. Geneva, Switzerland:World Health Organization; 2009.

2. Centola D. Social media and the science of healthbehavior. Circulation. 2013;127(21):2135---2144.

3. Shiffman S, Stone AA, Hufford MR. Ecologicalmomentary assessment. Annu Rev Clin Psychol.2008;4:1---32.

4. Centola D. The spread of behavior in an onlinesocial network experiment. Science. 2010;329(5996):1194---1197.

5. Cobb NK, Graham AL, Abrams DB. Social networkstructure of a large online community for smokingcessation. Am J Public Health. 2010;100(7):1282---1289.

6. Christakis NA, Fowler JH. The collective dynamicsof smoking in a large social network. N Engl J Med. 2008;358(21):2249---2258.

7. Abraham C, Michie S. A taxonomy of behaviorchange techniques used in interventions. Health Psychol.2008;27(3):379---387.

8. Glanz K, Rimer BK, Viswanath K. Health Behaviorand Health Education: Theory, Research, and Practice.4th ed. San Francisco, CA: Jossey-Bass; 2008.

9. Prochaska JO, Velicer WF. The transtheoreticalmodel of health behavior change. Am J Health Promot.1997;12(1):38---48.

10. Bandura A. Social cognitive theory. In: Kazdin AE,ed. Encyclopedia of Psychology. Vol 7. New York: OxfordUniversity Press; 2000:329---332.

11. Heaney CA, Israel BA. Social networks and socialsupport. In: Glanz K, Lewis FM, Rimer BK, eds. HealthBehavior and Health Education: Theory, Research, andPractice. 2nd ed. San Francisco, CA: Jossey-Bass; 1997:179---205.

12. Cobb NK, Graham AL, Byron MJ, Niaura RS,Abrams DB; Workshop Participants. Online social net-works and smoking cessation: a scientific researchagenda [published correction appears in J Med InternetRes. 2012;14(1):e12]. J Med Internet Res. 2011;13(4):e119.

13. Phua J. Participating in health issue-specific socialnetworking sites to quit smoking: how does onlinesocial interconnectedness influence smoking cessationself-efficacy? J Commun. 2013;63(5):933---952.

14. Myneni S, Cobb NK, Cohen T. Finding meaning insocial media: content-based social network analysisof QuitNet to identify new opportunities for healthpromotion. Stud Health Technol Inform. 2013;192:807---811.

15. Wipfli HL, Fujimoto K, Valente TW. Global tobaccocontrol diffusion: the case of the framework conventionon tobacco control. Am J Public Health. 2010;100(7):1260---1266.

16. Papachristos AV, Wildeman C, Roberto E. Tragic,but not random: the social contagion of nonfatal gunshotinjuries. Soc Sci Med. In press.

17. Fujimoto K, Unger JB, Valente TW. A networkmethod of measuring affiliation-based peer influence:assessing the influences of teammates’ smoking on ado-lescent smoking. Child Dev. 2012;83(2):442---451.

18. Fujimoto K, Valente TW. Alcohol peer influence ofparticipating in organized school activities: a networkapproach. Health Psychol. 2013;32(10):1084---1092.

19. Fujimoto K, Williams ML, Ross MW. Venue-basedaffiliation network and HIV risk behavior among malesex workers. Sex Transm Dis. 2013;40(6):453---458.

20. QuitNet. Available at: http://www.QuitNet.com.Accessed September 15, 2014.

21. Graham AL, Cobb NK, Raymond L, Sill S, Young J.Effectiveness of an internet-based worksite smokingcessation intervention at 12 months. J Occup EnvironMed. 2007;49(8):821---828.

22. Glaser BG, Strauss AL. The Discovery of GroundedTheory: Strategies for Qualitative Research. Chicago, IL:Aldine Publishers; 1967.

23. Strauss A, Corbin J. Basics of Qualitative Research:Techniques and Procedures for Developing GroundedTheory. Thousand Oaks, CA: Sage; 2008.

24. Landauer TK, Dumais ST. A solution to Plato’sproblem: the latent semantic analysis theory of acquisi-tion, induction, and representation of knowledge. PsycholRev. 1997;104(2):211---240.

25. Wasserman S, Faust K. Social Network Analysis.Cambridge, UK: Cambridge University; 1994.

26. Borgatti SP. NetDraw: Graph Visualization Software.Lexington, KY: Analytic Technologies; 2002.

27. Burt RS. Social contagion and innovation: cohesionversus structural equivalence. Am J Sociol. 1987;92(6):1287---1335.

28. Valente TW. Network Models of the Diffusion ofInnovations. Cresskill, NJ: Hampton Press; 1995.

29. Valente TW. Network models and methods forstudying the diffusion of innovations. In: Carrington PJ,Scott J, Wasserman S, eds. Models and Methods in SocialNetwork Analysis. New York, NY: Cambridge UniversityPress; 2005:98---116.

30. Fujimoto K, Chou CP, Valente TW. The networkautocorrelation model using two-mode data: affiliationexposure and potential bias in the autocorrelation pa-rameter. Soc Networks. 2011;33(3):231---243.

31. Doreian P. Estimating linear models with spatiallydistributed data. Sociol Methodol. 1981;12:359---388.

32. Dow MM, Burton ML, White DR, Reitz KP. Galton’sproblem as network autocorrelation. Am Ethnologist.1984;11(4):754---770.

33. Butts CT. Social network analysis with sna. J StatSoftw. 2008;24(6):1---51.

34. Robertson C, Boyle P, Hsieh CC, Macfarlane GJ,Maisonneuve P. Some statistical considerations in theanalysis of case-control studies when the exposure vari-ables are continuous measurements. Epidemiology.1994;5(2):164---170.

35. Myneni S. Attributing Meaning to Online SocialNetwork Analysis for Tailored Socio-behavioral SupportSystems [dissertation]. Houston, TX: University of TexasHealth Science Center at Houston; 2013.

36. Widdows D, Cohen T. The semantic vectorspackage: new algorithms and public tools for distribu-tional semantics. Paper presented at: Fourth IEEEinternational conference on semantic computing;September 22---24, 2010; Pittsburgh, PA.

37. Laumann EO, Marsden PV, Prensky D. Theboundary specification problem in network analysis. In:Burt RS, Minor MJ, eds. Applied Network Analysis.Newbury Park, CA: Sage; 1983:18---34.

38. Valente TW. Network interventions. Science.2012;337(6090):49---53.

39. Champion V, Foster J, Menon U. Tailoring inter-ventions for health behavior change in breast cancerscreening. Cancer Pract. 1997;5(5):283.

40. Strecher VJ, Kreuter M, Den Boer D-J, Kobrin S,Hospers HJ, Skinner CS. The effects of computer-tailoredsmoking cessation messages in family practice settings.J Fam Pract. 1994;39(3):262---270.

41. Wellman B, Boase J, Chen W. The networkednature of community: online and offline. IT Soc. 2002;1(1):151---165.

42. Huang GC, Soto D, Fujimoto K, Valente T. Theinterplay of friendship networks and social network-ing sites: longitudinal analysis of selection and in-fluence effects on adolescent smoking and alcohol use.Am J Public Health. 2014;104(8):e51---e59.

43. Lusher D, Koskinen J, Robins G. ExponentialRandom Graph Models for Social Networks: Theory,Methods, and Applications. Cambridge, MA: CambridgeUniversity Press; 2012.

44. Robins G, Pattison P, Kalish Y, Lusher D. Anintroduction to exponential random graph (p*) models forsocial networks. Soc Networks. 2007;29(2):173---191.

45. Snijders TAB, Pattison P, Robins GL, Handcock MS.New specifications for exponential random graph models.Sociol Methodol. 2006;36(1):99---153.

46. Wang P, Sharpe K, Robins GL, Pattison PE.Exponential random graph (p*) models for affiliationnetworks. Soc Networks. 2009;31(1):12---25.

47. Wang P, Pattison P, Robins G. Exponential randomgraph model specifications for bipartite networks—a dependence hierarchy. Soc Networks. 2013;35(2):211---222.

48. Wang P, Robins G, Pattison P, Lazega E. Expo-nential random graph models for multilevel networks.Soc Networks. 2013;35(1):96---115.

RESEARCH AND PRACTICE

1212 | Research and Practice | Peer Reviewed | Myneni et al. American Journal of Public Health | June 2015, Vol 105, No. 6