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Journal of the Medical Library Association : JMLA logoLink to Journal of the Medical Library Association : JMLA
. 2007 Oct;95(4):445–450. doi: 10.3163/1536-5050.95.4.445

Characteristics of cancer blog users

Sujin Kim 1, Deborah S Chung 1
PMCID: PMC2000789  PMID: 17971894

INTRODUCTION

Blogs are a relatively new medium in computer-mediated health communication and are regarded as highly opinionated journals maintained by millions of users who read and write personal remarks on issues ranging from news stories to health care [1–3]. Of the 120 million US adults with Internet access, 7%, or 8 million people, have created blogs [4], and the increasing use of blogs has been reported in several studies [1,4,5]. Rainie found that the typical blogger is a young, male, Internet veteran; has a broadband connection; and is financially secure [5]. The gender of the blogger has also been a topic for research. Herring et al. found that even though women participate in blogging activities (focusing on emotional support), men are more likely to create filter blogs and k-logs (knowledge blogs) that are considered focused on information [1].

Blogs have been described as a new medium, one that shifts mainstream control of information into the hands of the audience. The potential use of blogs for cancer patients, basic scientists, clinical researchers, and practicing oncologists to discuss findings and suggestions has been envisioned in several cancer journals [6]. In addition, the use of online communication tools to share emotional support in all aspects of cancer-related issues has been frequently described [2,6].

While blogs are becoming more frequently researched, empirical studies regarding blogs and their users, especially cancer patients and their companions (defined for this study as patient family and friends), are noticeably lacking. Most research has been in the area of news media [7]. Some research has been reported in lexical (textual) analyses from studies designed to provide technological frameworks to classify blog messages for improved accessibility [8,9]. However, questions regarding motivation to post or comment on blogs and the perceived outcomes of using blogs still remain challenging research tasks [10,11]. Understanding how blogs are used can allow information providers to better understand the impact blogs can have on cancer patients, their friends, and families. This study used cluster analysis techniques to classify cancer blog users' demographics, as well as their use and perceptions of blogs.

METHODS

The study, approved by the University of Kentucky's Institutional Review Board, used an online survey to target users of blogs with cancer-related content. Invitations to participate in the survey were posted on 153 individual personal blogs* that were identified through bi-weekly searches by the authors between March 30 and June 3, 2006, using Google Blog Search [12]. Searches were limited to include only blogs with the word “cancer” in their titles, written in the English language, and with posts created within the past month.* In addition, the search was limited to frequently used blogging services, such as Blogspot, LiveJournal, and Typepad. Before participating in the survey, individuals read a study information sheet and provided consent to participate in the study.

Survey questions sought demographic information, usage, motivation, behavioral changes, and limitations of using blogs among cancer blog users (Appendix online). The survey questions were designed and modified based on previous cancer research and research on motivations for using the Internet [13–15]. In particular, questions about motivations for blog use were modified to better encompass cancer patients, family, and friends as prior research typologies focused on entertainment, diversion, and habitual motivations and were not appropriate or relevant for this study. In completing the survey, participants were asked to identify themselves as a cancer patient, companion, or health care provider.

Cluster analysis can be useful in identifying natural groupings of homogeneous groups of people in a manner that both minimizes within-group variations and maximizes between-group variations [16–22]. Because little is known about cancer blog users, this study used cluster analysis to classify users and ascertain patterns of characteristics represented by those groups. The optimal number of clusters was determined based on the visual analysis of clustering results, such as a dendrogram and scree diagram. The significance of the clustering results was based on the Best-Cut suggestion by Mojena rules 1 and 2.

RESULTS

Overall sample demographics

The survey was completed by 113 respondents; 59.29% (n = 67) were cancer patients; 31.86% (n = 36) were friends or family of cancer patients; and 6.19% (n = 7) were health care providers. Three participants did not answer this question. About 77% of respondents were female (n = 87), and 22.12% were male (n = 25). About 94% percent (n = 99) of the sample were Caucasian. The most frequently reported salary earned by the population was between $60,000 and $75,000. The average age of the respondents was 57, and 91 (71.68%) respondents held bachelor's degrees or higher.

Characteristics of cancer bloggers

Cluster analysis revealed three clusters among the data. Visual analyses of the dendrogram and scree diagram (Figures 1 and 2, online) confirmed the three-cluster solution as the optimal number for the given data set. Table 1 displays each cluster group's general demographics, and Table 2 displays the means of the variables blog usage, motivation, behavioral changes, and limitations of blog use.

Table 1 General characteristics of the three identified clusters

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Table 2 Means of blog use, motivations, and behavioral changes

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Demographic characteristics

Cluster 1 included 38 (33.63%) bloggers whose average age was slightly higher than that of the sample (40.82, SD 11.03, vs. 40.26, SD 12.31). Among the 38 members in cluster 1, 25 were patients and 9 were friends or family members. Over 71% (n = 27) of cluster 1 members answered that they hosted their own blogs, again representing the highest among the 3 clusters. Cluster 2 had the most members in its group (n = 48, 42.48%). Similar to the other 2 clusters, Caucasian women dominated this group (n = 33, 68.75%). Eighteen of 30 bloggers in this cluster were single. The number of friends and family members found in this group was 20, more than half of the total friends and family members in this study. Cluster 3 had the fewest male bloggers (n = 4, 16%), and the average age of the members was 40.63 (SD 11.96). Cluster 3 also included the least number of individuals who hosted their own blogs (n = 15, 55.55%).

Blog use

Cluster 1 used blogs for an average of 16.76 months, which was slightly less than the other 2 clusters. Cluster 2 reported higher mean scores (4.11) for seeking health care providers as their information source than the other 2 clusters. Information sought in medical libraries and patient education centers was more frequently sought in cluster 3 (mean score 2.92) compared to the other 2 clusters.

Motivations for blog use

Cluster 1 had slightly higher mean scores for using cancer blogs to seek cancer knowledge. Cluster 2 more frequently used blogs to express their own opinions. Across the 3 clusters in this study, encouraging others and sharing personal cancer stories were the primary motivators for blog use (mean score = 4.33 and 4.24, respectively). In cluster 1, seeking a second opinion, looking for timely updated information, and looking for compiled cancer information were the least motivating factors (mean scores = 2.84, 3.11, and 3.13, respectively). In addition, cluster 1 had the lowest mean score for expanding cancer knowledge (3.92) and cluster 2 had the lowest mean score for validating information (3.04).

Behavioral changes

Cluster 1 members encountered fewer limitations for using cancer blogs than the other two clusters. Members in clusters 2 and 3 indicated that poor searching functions restricted their participation in cancer blogs, while members in cluster 1 indicated less agreement with that statement.

Summary of cluster characteristics

Based on analysis of the data as determined by interpretation of survey results, Table 3 summarizes major characteristics of the 3 clusters found in this study. Cluster 1 (n = 38, 33.63%) was more likely to include new bloggers who were motivated to seek compiled information and were frequent online information seekers. Cluster 2 (n = 48, 42.48%) was more likely to include long-time cancer blog users who also use traditional sources for information seeking. Individuals in cluster 3 (n = 27, 23.89%) were highly motivated and sought medically related information. In addition, bloggers in cluster 3 made the most frequent behavioral changes while using cancer blogs. Further details are described in Table 3.

Table 3 Summary of cluster characteristics*

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DISCUSSION/CONCLUSION

This study used an agglomerative, hierarchical clustering method to classify characteristics of unique groups among cancer bloggers. Employing this technique was most appropriate given that there was limited prior knowledge of the underlying structure and nonhierarchical methods could not clearly and objectively determine the number of clusters in a data set. Moreover, this method is useful when a study is still in its exploratory phase.

Some demographic data found in this study were different from previously reported data. Results from the analysis illustrated a dominant demographic group across all clusters: highly educated Caucasian females. This demographic group is inconsistent with other research findings about online cancer information seekers [5]. In this study, the bloggers were also older (average age fifty-seven) than in other studies. As older patients and their friends and family seek health-related issues (especially cancer), there are potential roles for using this new technological medium to deliver cancer information.

The study findings suggest that blogs are used more frequently to share emotional support and personal stories than medical knowledge, thus agreeing with reports and research that indicate blogs have gained their popularity over the past few years by supporting personal narratives, political commentaries, or accounts of personal experiences. This study confirmed the findings of previous research that suggested the use of blogs can lead cancer patients and their companions to engage in meaningful conversation and that sharing personal experiences via blogs may help patients better cope with their cancer-related health conditions [6].

These results can inform the design of cancer blogs that provide customized (or personalized) assistance depending on the category of cancer blog users and their distinct characteristics. For instance, more attention by cancer information specialists (including medical librarians) might be given to people in cluster 1 because their motivation is to expand their knowledge about cancer as compared to people in cluster 2 who use blogs to seek emotional support.

Additionally, medical librarianship should not overlook bloggers and their uses, because blogs can be used as a health communication medium to disseminate cancer health information. In this sense, further in-depth analysis of cancer blog messages, including both posts and comments, may be beneficial in providing subject categorization to unorganized blog contexts. Medical librarians can, thus, play a key role in making information on blogs more easily assessable.

While this study provides useful findings, it has some limitations. The study sample is small and includes primarily Caucasian patients with bachelor's degrees. In addition, the use of convenience sampling and self-reported data could present bias in the reported results. Future studies should target a larger pool of participants in a longitudinal setting for more valid, reliable, and generalizable findings.

Supplementary Material

Appendix
jmla-095-04-0445-s1-a1.pdf (69.1KB, pdf)
Figure 1
jmla-095-04-0445-s1-f1.pdf (67.9KB, pdf)
Figure 2
jmla-095-04-0445-s1-f2.pdf (38.1KB, pdf)

Footnotes

Figures 1 and 2 and a supplemental appendix are available with the online version of this journal.

* The invitation was only posted in personal blogs that do not restrict postings and comments by the public.

REFERENCES

  1. Herring SC, Kouper I, Scheidt LA, and Wright E. Women and children last: the discursive construction of Weblogs. [Web document]. [cited 28 Jun 2006]. <http://blog.lib.umn.edu/blogosphere/women_and_children.html>. [Google Scholar]
  2. Hillan J.. Physician use of patient-centered Weblogs and online journals. Clin Med Res. 2003;1(4):333–5. doi: 10.3121/cmr.1.4.333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Mishne G. Experiments with mood classification in blog posts. In: Style2005: The 1st Workshop on Stylistic Analysis of Text for Information Access, SIGIR 2005; Salvador, Brazil; Aug 15–19, 2005. [Google Scholar]
  4. Lenhart A, Horrigan J, and Fallows D. Content creation online. [Web document]. Pew Internet & American Life Project, Feb 2004. [cited 20 Feb 2006]. <http://www.pewinternet.org/pdfs/PIP_Content_Creation_Report.pdf>. [Google Scholar]
  5. Rainie L. The state of blogging. [Web document]. Pew Internet & American Life Project. [cited 28 Jun 2005]. <http://www.pewinternet.org/pdfs/PIP_blogging_data.pdf>. [Google Scholar]
  6. Oransky I.. Cancer blogs. Lancet Oncol. 2005;6(11):838–9. [Google Scholar]
  7. Harper C. Blogging and journalistic practice. Paper presented at: Association of Internet Researchers Annual Conference; Chicago, IL; 5–9 Oct 2005. [Google Scholar]
  8. Anjewierden A, Hoog DE, Brussee R, and Efimova L. Detecting knowledge flows in Weblogs. Presented at: 13th International Conference on Conceptual Structures. [Web document]. [cited 13 Jan 2006]. <http://anjo.blogs.com/metis/articles/kflows-pub.pdf>. [Google Scholar]
  9. Cayzer S.. Semantic blogging and decentralized knowledge management. Commun ACM. 2005;47(12):47–52. [Google Scholar]
  10. Gumbrecht M. Blogs as protected space. [Web document]. Presented at: WWW 2004: Workshop on the Weblogging Ecosytem: Aggregation, Analysis, and Dynamis. [cited 3 Mar 2006]. <http://www.blogpulse.com/papers/www2004gumbrecht.pdf>. [Google Scholar]
  11. Kahn R, Kellner D.. New media and Internet activism: from the battle of Seattle to blogging. New Media Soc. 2004;6(1):87–95. [Google Scholar]
  12. Google Blog Search. [Web document]. Google. [cited 2 May 2007]. <http://www.blogsearch.google.com>. [Google Scholar]
  13. Johnson JD. Cancer-related information seeking. Cresskill, NJ: Hampton Press, 1997. [Google Scholar]
  14. White MD.. Questioning behavior on a consumer health electronic list. Libr Q. 2000;70(3):302–34. [Google Scholar]
  15. Papacharissi Z, Rubin AM. Predictors of Internet use. J Broadcast Electron Media. 2000 Spring; 44:175–96. [Google Scholar]
  16. ClustanGraphics features. [Web document]. ClustanGraphics. [cited 23 Mar 2006]. <http://www.clustan.com/clustangraphics_features.html>. [Google Scholar]
  17. Chen HM, Cooper MD.. Using clustering techniques to detect usage patterns in a Web-based information system. J Am Soc Inf Sci Technol. 2001;52(11):888–904. [Google Scholar]
  18. Frank RE, Green PE.. Numerical taxonomy in marketing analysis: a review article. J Mark Res. 1968;5:83–98. [Google Scholar]
  19. Milligan GW.. An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika. 1980;45:325–42. [Google Scholar]
  20. SPSS. SPSS base 13.0 user's guide. Upper Saddle River, NJ: Prentice Hall, 2004. [Google Scholar]
  21. Mojena R.. Hierarchical grouping methods and stopping rules: an evaluation. Comput J. 1977;20:359–63. [Google Scholar]
  22. Shaaban S, McKechnie J, Lockley S.. Modeling information seeking behavior of AEC professionals on online technical information resources. ITcon. 2003;8:265–81. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix
jmla-095-04-0445-s1-a1.pdf (69.1KB, pdf)
Figure 1
jmla-095-04-0445-s1-f1.pdf (67.9KB, pdf)
Figure 2
jmla-095-04-0445-s1-f2.pdf (38.1KB, pdf)

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