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Online Cancer News: Trends Regarding Article Types,Specific Cancers, and the Cancer ContinuumRyan J. Hurley a , Julius Matthew Riles b & Angeline Sangalang ca Department of Communication , North Carolina State Universityb Department of Communication , University of Illinois at Urbana–Champaignc Annenberg School for Communication and Journalism , University of Southern CaliforniaPublished online: 28 Jan 2013.

To cite this article: Ryan J. Hurley , Julius Matthew Riles & Angeline Sangalang (2014) Online Cancer News: TrendsRegarding Article Types, Specific Cancers, and the Cancer Continuum, Health Communication, 29:1, 41-50, DOI:10.1080/10410236.2012.715538

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Health Communication, 29: 41–50, 2014Copyright © Taylor & Francis Group, LLCISSN: 1041-0236 print / 1532-7027 onlineDOI: 10.1080/10410236.2012.715538

Online Cancer News: Trends Regarding Article Types, SpecificCancers, and the Cancer Continuum

Ryan J. HurleyDepartment of CommunicationNorth Carolina State University

Julius Matthew RilesDepartment of Communication

University of Illinois at Urbana–Champaign

Angeline SangalangAnnenberg School for Communication and Journalism

University of Southern California

The Internet is one of the fastest growing news sources for many worldwide (Pew ResearchCenter’s Project for Excellence in Journalism, 2011), and cancer news is one frequently con-sumed form of online health information (Google, Inc., 2007). This content analysis of onlinecancer news (n = 862) retrieved from the four most frequented news websites describes trendsregarding specific cancers, stages in the cancer continuum, and types of news articles. In gen-eral, treatment information received the most attention in online cancer news. Breast cancerreceived the most attention of each specific cancer, followed by digestive and genitourinarycancers. Research reports and profiles of people (more than 60% of which were about celebri-ties) were the most common article types. Risk, uncertainty, and clinical trials were also presentacross several types of cancer news articles. Implications of content trends are discussed asrelevant to consumers, producers, health campaign designers, and researchers alike.

As recently as two decades ago, the vast majority of peo-ple received their news from television and newspapers. TheInternet has now become the fastest growing and most pop-ular source of news for American youth, suggesting thatthis trend will continue (Pew Research Center’s Projectfor Excellence in Journalism, 2011). This radical trans-formation of news dissemination and consumption raisesmany concerns for producers, consumers, and researchers.Specifically, researchers need to know more about the char-acteristics of messages, including news, transmitted via newcommunication technologies.

Correspondence should be addressed to Ryan J. Hurley, Departmentof Communication, North Carolina State University, 201 Winston Hall,Campus Box 8104, Raleigh, NC 27695. E-mail: rjhurley@ncsu.edu

ONLINE AND CANCER NEWS

One type of online information commonly sought but lack-ing in research attention is news concerning health issues.In the year 2000, 54 million people reported going onlinefor health information, but that number has grown to morethan 175 million (Harris Interactive, 2010). Cancer, in partic-ular, is one of the most searched health topics on the Internet(Shim, 2008). Moreover, people are using this information tomake critical decisions about their health, making inquiriesregarding Internet-based cancer news content vital. The PewInternet and American Life Project (2006) reported that 53%of online health information seekers said that the informa-tion they retrieved had some impact on how they took careof someone else or themselves, and 11% of those said thatthe information had a major impact on their health behav-iors. Understanding the landscape of Internet-based health

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information in many contexts is clearly warranted (see Rice& Katz, 2001, for a detailed treatment of this topic).

In 2006, cancer was the third most searched term onGoogle News (Google, Inc., 2007), and in 2010 all forms ofnews dissemination lost audience members except for one:online news (Pew Research Center’s Project for Excellencein Journalism, 2011). Still, an analysis of online cancernews content is relatively missing from the literature. Muchresearch has examined traditional cancer news (i.e., news-papers, television). For example, Slater, Long, Bettinghaus,and Reineke (2008) examined the amount of attention thenews pays to stages of the cancer continuum, suggest-ing that treatment information was the most prominent innewspapers, but second to detection in magazines and sec-ond to reports of cancer-related deaths on TV. In 2008,McDonnell, Lee, Kim, Kazinets, and Moskowitz completedan analysis of online cancer news that paid attention tothe frequencies of specific cancers and stages of cancercare. When compared to the Korea Daily, news culled fromthe LA Times was slightly more likely to mention preven-tion and treatment information, while less likely to coversome cancers, including breast, colon, and lung cancers.Though this project was a good comparison of two onlinenews portals, cancer news on the Internet is still grosslyunderstudied.

AGENDA SETTING

When McCombs and Shaw (1972) first explicated agendasetting (AS), it was a way of describing how media can influ-ence what topics people think about, but not how they thinkabout those topics. This has been called first-level (or first-order) agenda setting. Essentially, the media demonstrateissue importance through how prominently an individualtopic is featured in media messages. Issue prominence, usu-ally defined as frequency, length, and/or location of thecoverage, is taken to represent the media agenda (Wang &Gantz, 2010). Content work has examined the prominenceof health information in a number of ways. For televi-sion, stories receiving more airtime are typically consideredhigher in importance (Wang & Gantz, 2010), whereas front-page placement and/or length (i.e., word count or columnlength) are frequently used to indicate importance for news-paper messages (Graber, 1988). Still, frequencies regardingappearances in the media (total or per unit of time) remainthe most common importance indicator across AS contentwork (e.g., Wang & Gantz, 2010). What remains unclearin AS work is whether basic frequencies are the most validmeasure of topic attention.

From an effects standpoint, AS helps make predictionsabout importance ratings of a particular topic using thedepiction of that issue in the media agenda. In other words,when exposed to a particular media agenda, consumers’personal agendas might be influenced. Lewison, Tootell,

Roe, and Sullivan (2008) found that trends in coverage ofsite-specific cancer types appeared to inform policymak-ers of active and successful research domains, and Jones,Denham, and Springston (2006) observed that people whoread news articles about breast cancer also reported more fre-quent screenings. Although it is imprudent to make causalclaims from these types of associations, they do speakto a potential relationship between media consumption,health-related behaviors, and even governmental resourceallocation.

More recently, second-order (or second-level) AS pro-cesses have been examined. Second-level AS research tendsto focus on how specific topics are discussed, makingsecond-level AS difficult to distinguish from research onframing. In fact, Taylor-Clark, Mebane, Steelfisher, andBlendon (2007) appear to use second-level AS and framingsynonymously while noting that the way an issue is dis-cussed could impact who audiences perceive is responsiblefor solving the issues. These findings all point to a need forunderstanding how certain health content is present in thenews.

RESEARCH QUESTIONS

This research project was designed to analyze several qual-ities of cancer news information as related to first- andsecond-level agenda-setting processes. Previous content-analytic endeavors with traditional news media outlets haveprovided insight into the types of content that could bepresent in online cancer news. Of primary concern in sev-eral inquiries were frequencies of specific cancers (e.g.,McDonnell et al., 2008). In fact, some research has com-pared reports of cancer in the news to cancer incidence ratesin the population, suggesting that comprehensive analyses ofall types of cancer are excellent ways of tracking such inter-reality distortions (e.g., Jensen et al., 2010). These findingssuggest serious AS implications for policymakers and vot-ers alike, such that cancers that do not receive proportionalmedia attention might be perceived as less important thantypes that receive a lot of media attention.

RQ1: With what frequencies do specific cancers appear inonline cancer news?

Content explorations have coded article types in can-cer news, noting that reports of research and stories aboutpersons dealing with cancer present significantly differentcontent (Moriarty et al., 2010). Though profiles of people(many of which are about famous individuals) and researchreports were the most common types of stories found intheir analysis of newspaper coverage, online news coulddiffer. Understanding which story types appear online andtheir characteristics could help researchers better prepare foreffects studies in an online environment.

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RQ2: With what frequencies do different article typesappear in online cancer news?

Although many have noted the importance of docu-menting relative attention to stages of the cancer expe-rience, few have examined these characteristics in onlinecancer news (for an exception, see McDonnell et al.,2008). The cancer experience has been described as con-taining five distinct chronological stages: the prevention,detection, treatment, survivorship, and end-of-life stages(Stryker, Emmons, & Viswanath, 2007). Several stud-ies have indicated a “treatment focus” in mainstreammedia, particularly news media (e.g., Jensen et al., 2010),that could be problematic and is worthy of furtherverification.

H1: Treatment content will receive the most attention inonline cancer news.

RQ3: With what frequencies do the cancer continuum stagesappear in online news?

Several other characteristics of news information andcharacteristics specific to health and cancer information(e.g., uncertainty, risk, and wire service presence) have beenexamined in previous research regarding traditional news(e.g., Jensen et al., 2010; Moriarty et al., 2010; Slater et al.,2008). Gatekeeping and article selection function differentlyonline compared to traditional information structures. Forexample, Google News and other online news servicesadd an additional computerized filter that is not present innewspaper or TV news (for more on this issue, see Hurley &Tewksbury, 2012) and might result in a different relationshipwith wire services. In a recent study of the most popularbreast cancer websites, those that ranked highest in Googlesearches contained information about clinical trials (Mericet al., 2002). Risk information also has been sought by indi-viduals to evaluate their probabilities of developing cancerand to manage uncertainties about the illness (Brashers,2007), making each of these article characteristics worthyof note.

RQ4: With what frequencies do wire services, clinical trials,risk, and uncertainty-related information appear inonline cancer news?

Furthermore, specific cancers, article types, the cancercontinuum, and the other news article characteristics dis-cussed earlier should be investigated regarding how they arerelated to each other. For example, knowing that treatmentinformation is the most prevalent cancer stage discussed inthe news is good, but understanding the relationship betweentreatment and uncertainty-related content (for example) pro-vides deeper insight into and foundation for investigationsregarding potential second-level AS effects. Correlationsbetween several different variables in this study might be ofsimilar interest.

RQ5: How are each of the article characteristics and typesof cancer information herein related to each other?

METHOD

This project was developed in order to detail online cancernews characteristics and trends, because attention to partic-ular topics could influence consumer cognitions about theillness. The following section describes the sample, samplingmethods, coding procedures, and variables coded for thisproject. As a method, content analysis is a unique tool that ishighly useful for understanding characteristics of messages(Krippendorff, 2004). Those interested in an agenda-settingeffect need to know what information is receiving mediaattention, before studies of the influence of these messagescan be conducted.

Sample

Stratified random sampling was used to capture Internet-based cancer news stories from four of the most popularwebsites for news around the time of data collection (GoogleNews, Yahoo! News, MSNBC.com, and CNN.com; PewResearch for People and the Press, 2006). Data collectiontook place over four contiguous months in 2008 (Marchthrough June). The sample was stratified by day of the week,and then, using random sampling methods, the researchersselected seven dates (e.g., Monday, March 10) during eachmonth to represent that day of the week for that sam-pling month (i.e., representative of a Monday in March).This design yielded a complete composite week for eachsampling month, totaling to four complete weeks of newscoverage. On each date, a researcher collected articles fromeach website at four different times of day. Data collectionwas conducted at 6-hour intervals (i.e., 6:00 a.m., 12:00 p.m.,6:00 p.m., 12:00 a.m.). Collecting data at these differentequal temporal intervals was intended to be representativeof the convenience and availability of online news infor-mation at any time of the day (Pew Internet & AmericanLife Project, 2006). Through the four sampling months, eachwebsite was represented during each day and each time inter-val, yielding a complete and symmetrical 28-day month ofonline cancer news.

The first three search pages (10 stories were locatedon each page, totaling 30 stories) were collected fromMSNBC.com, CNN.com, and Yahoo! News at each time.Research has suggested that it is uncommon for individualsto go beyond the first 20 items (approximately two pages)of search-engine results (Molassitosis & Xu, 2004); there-fore, capturing the first 30 items should be sufficient forgarnering the most likely viewed cancer news articles fromthese news websites. Ten stories were then selected from the30 for analysis, using random sampling methods to ensureeach story was equally likely to be included in the sample.

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Articles from Google News were collected in a slightlydifferent manner. Google News returns story clusters withone featured headline, distinguished by larger font size andproximity to the top of the page, and a few related headlineslisted below. For Google News, the first 30 clusters were col-lected but only the featured headline articles were used inanalysis. Previous research suggests that headline size andposition are associated with story selection in newspapers(see Graber, 1988), and utilizing the featured stories in eachcluster (located on the first three search pages of GoogleNews search results) allowed the researchers to apply nearlythe same sampling method to select stories from GoogleNews as the other three news websites.

To acquire the broadest selection of cancer news stories,cancer was selected as the search term. Utilizing cancer forsearches across all news websites was useful for comparisonbetween specific cancers. Furthermore, cancer, as a searchterm, seems to be representative of actual search queriesemployed by many using the Internet for cancer information(e.g., Google, Inc., 2007). This method of story collectiondid yield duplicate articles. In other words, the same arti-cle might have been captured by both Yahoo! News and byCNN.com. For this analysis, all duplicate articles were leftin the analysis to best reflect frequencies of characteristicsin the constructed week. Leaving duplicates in the analysisshould lead to a more externally valid representation of thecancer information being viewed by online news seekers.

Coding Procedure

Human coders evaluated the news articles in the sample forcancer-related themes. Pilot tests were conducted with setsof articles that were not from the final sample to establishreliability and revise the coding scheme. Intercoder relia-bility was calculated using Krippendorf’s (2004) alpha on10% of the final sample (n = 112), and all reliabilities estab-lished were above .85. Following reliability training, twocoders were randomly assigned half of the data in the sam-ple (28 total days, 14 days per coder) to code. The articlesthat were dually coded for reliability were included in thealready-described random assignment and only the data fromthe appropriate coder after this randomization were includedfor analysis.

Variables

Coders were asked to identify a variety of article char-acteristics and cancer-related information described in thefollowing. All measures were coded at the story level.

Cancer article type. Coders categorized the arti-cles into one of six mutually exclusive categories simi-lar to previous research on cancer news (e.g., Moriartyet al., 2010): reports of research (1), person profiles (2),event or fundraiser descriptions (3), politics or policy

stories (4), disease awareness or education articles (5), orother news reports (6). Reliability for determining articletypes was sufficient (α = .93). Coders also noted (usinga presence/absence measure) when a profile of a personwas about a famous individual with excellent reliability(α = 1.0).

Specific cancers. Coders measured whether any spe-cific cancers (e.g., brain, breast, prostate, skin, etc.) werepresent in the articles (presence/absence). Coders also mea-sured the amount of attention each cancer type (i.e., any andall cancers could be coded in one article) was given in thearticles: no mention (0), mention only (1), additional infor-mation (2), major emphasis (3), and primary focus (4). To becoded as “mention only,” a cancer had to be mentioned byname with no other associated information. The addition ofany other information resulted in a code of “additional infor-mation” (e.g., Mae suffered from a highly treatable form ofbreast cancer). If a specific cancer was the focus of more thanone-third of the article (i.e., was the focus of one-third ormore of the paragraphs), it was coded as a “major emphasis.”Only if a specific cancer was the focus of the entire arti-cle was that article coded as having a primary focus on thatcancer. Reliabilities using the presence/absence and atten-tion items for each specific cancer were between a .85 and1.00 using Krippendorff’s alpha.

Cancer continuum. Each of the five stages of the can-cer continuum (i.e., prevention, detection/diagnosis, treat-ment, survivorship, and end of life) was individually codedusing the aforementioned attention scale. Conceivably, a sin-gle article about cancer could discuss any number of thesefive stages to varying degrees, and measuring all discussionfor each stage was a project goal. Capturing each stage onits own scale allowed for a more valid picture of attention tothese stages both within and across articles in this sample.

Prevention information was identified as any mention ofsteps to reduce the likelihood of an individual to developcancer (e.g., sunscreen use may prevent skin cancer).Detection/diagnosis was coded when any article mentionedscreening, detection tests, precancerous symptoms, and othersigns that may identify the cancer disease. Treatment referredto any attempt to medically remove or alter an individual’scancer or cancer symptoms (e.g., surgery, chemotherapy).Survivorship was identified when there was any discussionof overcoming cancer. Finally, end of life was coded as anymention of cancer-related death. Each stage of the continuumwas coded with high agreement (.96 to 1.0 on Krippendorff’salpha).

Wire service, clinical trials, numeric risk, anduncertainty. The degree to which online news is reliantupon wire services is questionable. Therefore, coders wereasked to identify whether an article originated from a wireservice (Associated Press, Reuters, or other) and did so

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TABLE 1Attention to Specific Cancers, Stages of the Cancer Continuum, Uncertain Terms, Risk, & Clinical Trials

Cancer Continuum Five Most Frequent Cancer Types

Prev. Detect. Treat. Surv. End BreastDigestive

(e.g., Colon)Genitourinary(e.g., Prostate)

Gynecological(e.g., Cervical) Skin

Atten Rank 4 2 1 3 5 1 2 3 4 5Ave Atten1 2.86a 2.56b 2.94a 2.43c 2.44c 2.92a 2.59b 2.51b 2.45b 2.58b

Total Attn2 1.03a 1.42b 2.14c 1.10a 0.93a 0.97a 0.71b 0.58c 0.30d 0.29d

Prm Focus3 11b

(95)6c

(50)22a

(189)4d

(31)3d

(29)10

(87)4

(33)3

(30)2

(18)1

(10)% All Ment 36

(310)56

(479)73

(629)45

(390)38

(330)33

(287)27

(237)23

(198)12

(107)11

(97)

Uncertain4 92a 92ac 80bc 81abd 62d

Risk 91a 78b 57c 68bc 72bc

Clin Trials 13b 14b 37a 0c 0c

1Average attention was calculated by weighing all mentions from 1 to 4, from mention only to primary focus respectively, to create an overall attentionscore. This attention score was then divided by the total number of articles mentioning the column variable.

2Total attention was calculated by dividing the aforementioned attention score by the number of valid cancer news articles (n = 862). Averages within rowand type (i.e., attention regarding the cancer continuum was not compared to attention regarding specific cancers) that do not share superscripts differ at leastat p < .05 using independent-samples t-tests.

3Numbers in parentheses are frequencies. Those that do not share superscripts differ at least at p < .05 using a single-variable chi-squared test.4Percentages regarding uncertainty-related language, risk, and clinical trials are based upon their respective cancer continuum primary focus totals. Those

that do not share superscripts differ at least at p < .05 using a two-variable chi-squared test that accounts for primary focus totals.

with perfect reliability (α = 1.0), which was expected con-sidering wire-service articles were explicitly noted as such.Coders also indicated the individual presence/absence (0 to1) of explicit mentions of clinical trials (α = 1.0), numericrisk (α = .95), and uncertainty-related information (e.g.,chemotherapy may have severe negative side effects, mam-mograms might miss some cancerous tissue; α = .93) withhigh reliability.

RESULTS

The results of this content analysis detail general trends andcharacteristics of online cancers news. Of the 1,120 arti-cles collected, 862 were actually about human cancer andserved as the final sample for this analysis. Some generaltrends in these data suggested that about 37% of articles orig-inated from a wire service (226 from AP, 53 from Reuters,and 42 from other sources). Numeric risk statements werepresent in 55% of articles and clinical trials mentioned in15%. Next, data are explored as related to three major con-tent themes (e.g., specific cancers, article types, and thecancer continuum; i.e., not necessarily in sequential orderof the research questions and hypothesis) using a series oft-tests, chi-squared tests, and bivariate correlations.

Specific Cancers

RQ1 asked about the frequencies of specific cancers inonline cancer news because of the potential of first-order ASeffects. Specific cancers were mentioned in 84% of sampled

articles. The most frequently mentioned cancers were breast(33%), digestive (e.g., colon; 27%), and genitourinary can-cers (e.g., prostate; 23%; see Table 1). Relative attention tothe five most frequent cancers was analyzed using a series ofindependent-samples t-tests, which are presented in Table 1.1

On each attention measure, breast cancer dominated onlinecancer news.

In partial answer to RQ3, cancer news that primarilyfocused on a specific cancer was also connected to storiesprimarily focusing on stages in the cancer continuum (seeTable 2). Interestingly, genitourinary cancer articles werepredominantly about treatment (70%); however, other cancerarticles were dispersed differently across the cancer contin-uum. Breast cancer, for example, was linked most frequentlyto detection (29%), prevention (23%), and treatment (22%).Gynecological cancer articles were most likely to have a pri-mary focus on prevention (44%). Substantial variation waspresent between cancers and is detailed in Table 2, providinginteresting insights for those interested in second-order ASand/or framing.

Specific cancers were dispersed across different articletypes, as well (in partial answer to RQ5). Stories about thefive most frequent specific cancers were most likely to bereports of research, except in the case of skin cancer. Table 3presents a series of two-variable chi-squared tests that estab-lished frequency differences regarding the most frequent fivecancers and article types while controlling for article-type

1No alpha adjustments were made in this analysis. For informationon the pros and cons of alpha adjustments when conducting multiplesignificance tests (e.g., t-tests) see O’Keefe (2003) and Hewes (2003).

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TABLE 2Specific Cancer123 and Article Types by mentions of Uncertainty, Risk, Clinical Trials, and the Cancer Continuum1

Cancer Continuum

P Focs Tot Uncert Risk C TrialsPrevention

(n = 95)Detection(n = 50)

Treatment(n = 189)

Survivorship(n = 31)

End of Life(n = 29)

Brain 12 92% 92 25 8 0 42 0 0Breast 87 83 74 16 23 29 22 1 1Digestive 33 85 67 30 18 21 30 0 0Genitourinary 30 87 57 40 7 7 70 0 0Gynecological 18 100 61 22 44 11 28 0 0Head and neck 7 100 86 0 0 71 0 0 0Lung 8 88 63 0 0 25 13 0 0Pediatric 10 50 10 0 0 0 10 40 0Skin 10 90 90 40 30 0 30 0 0

Research reports 81a 50c 67b 65abc 62abc

Person profiles 5b 20a 16a 19a 14ab

Event/fundraisers 3a 2ab 0b 10a 0ab

Policy/politics 5b 2b 13a 3ab 0b

Awareness/education 5b 26a 3b 3b 24a

Note. Specific cancer and cancer continuum numbers represent primary focus articles only. Articles primarily about leukemia (n = 4), bone (n = 2),lymphoma (n = 1), and other cancers (n = 2) were left off for low frequencies. Specific cancer percentages are based upon the primary focus frequencies inthe first column. Article type percentages are based upon cancer continuum primary focus totals presented at the top of each column. Percentages in rows thatdo not share superscripts differ at least at p < .05 using a two-variable chi-squared test that accounts for cancer continuum primary focus totals.

TABLE 3Article Type by Clinical Trials, Uncertainty, Risk, and Specific Cancers

Five Most Frequent Cancer Types

Article TypeTotals Uncert Risk

ClinicalTrials Breast

Digestive(e.g., Colon)

Genitourinary(e.g., Prostate)

Gynecological(e.g., Cervical) Skin

Research reports 399a

(46%)344a

(86)274a

(67)106a

(27)56a

(14)25a

(6)25a

(6)14a

(4)3b

(1)Person profiles 237b

(27)112c

(47)109b

(46)8cd

(3)16bc

(7)5b

(2)0b

(0)0bc

(0)2ab

(1)Event/fundraisers 86c

(10)16d

(19)15c

(17)0d

(0)2c

(2)1ab

(1)0bc

(0)2ab

(2)1ab

(1)Policy/politics 68cd

(8)44b

(65)29b

(43)11ab

(16)5bc

(7)0b

(0)4a

(6)1ab

(1)1ab

(1)Awareness/education 56d

(6)36b

(64)44a

(79)4bc

(7)8ab

(14)2ab

(4)1ac

(2)1ab

(2)3a

(5)Other reports 16e

(2)10b

(63)2c

(13)0bcd

(0)0ac

(0)0ab

(0)0ab

(0)0ac

(0)0ab

(0)

TOTALS N = 862 562(65)

473(55)

129(15)

87(10)

33(4)

30(3)

18(2)

10(1)

Note. Specific cancer frequencies represent primary focus articles only and rank (first through fifth) in terms of attention from left to right. The percentagesin parentheses are based upon the article type totals in the first column, whereas percentages in column one and the totals row are calculated using the total nof 862. Frequencies within columns that do not share superscripts differ at least at p < .05 using a two-variable chi-squared test that accounts for article typetotals, except in the first column, where a single-variable chi-squared was employed.

totals. This conservative way of establishing frequency dif-ferences depicts some variation across specific cancers, seenin Table 3.

An examination of the articles that focused primarily onsome specific cancer revealed that uncertainty and risk wereever present, in partial answer to RQ4 (see Table 2 for fre-quencies; see Table 4 for correlations). For example, 92% of

articles focusing primarily on brain cancer contained risk andthe same amount contained uncertainty-related messages.Clinical trials were mentioned with less frequency than riskand uncertainty-related messages. Clinical trials appeared in40% of genitourinary cancer articles and were never men-tioned in stories primarily focusing on lung or pediatriccancers (see Table 2).

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TABLE 4Pearson’s r and Spearman’s Rho Matrix for the Cancer Continuum, Uncertainty, Risk, and Other Article Characteristics

WireService

SpecificCancer Prevention Detection Treatment Survivorship

End ofLife

ClinicalTrials Risk Uncertainty

Wire Service 1 .11∗∗ .08∗ .12∗∗ .06 (.09) .03 (.43) .23∗∗ .03 (.44) .16∗∗ .09∗∗Specific Cancer .15∗∗ 1 .15∗∗ .24∗∗ .15∗∗ .11∗∗ .13∗∗ .01 (.85) .25∗∗ .15∗∗Prevention .08∗ .15∗∗ 1 .17∗∗ −.39∗∗ −.12∗∗ .09∗∗ .01 (.69) .40∗∗ .25∗∗Detection .13∗∗ .27∗∗ .20∗∗ 1 −.01 (.83) .18∗∗ .28∗∗ −.15∗∗ .38∗∗ .09∗∗Treatment .05 (.12) .16∗∗ −.36∗∗ −.03 (.36) 1 .31∗∗ .14∗∗ .21∗∗ .04 (.23) .09∗∗Survivorship .02 (.87) .15∗∗ −.10∗∗ .18∗∗ .29∗∗ 1 .27∗∗ .06 (.1) .23∗∗ .00 (.97)End of Life .22∗∗ .18∗∗ .10∗∗ .29∗∗ .12∗∗ .31∗∗ 1 −.00 (.9) .30∗∗ .08∗Clinical Trials .03 (.44) .02 (.61) .01 (.7) −.15∗∗ .24∗∗ .07 (.06) .01 (.81) 1 .15∗∗ .19∗∗Risk .16∗∗ .29∗∗ .40∗∗ .38∗∗ .06 (.11) .23∗∗ .3∗∗ .15∗∗ 1 .34∗∗Uncertainty .09∗∗ .17∗∗ .24∗∗ .09∗∗ .11∗∗ −.01 (.9) .08∗ .19∗∗ .34∗∗ 1

Note. Pearson’s r calculations are on the top of the matrix and Spearman’s rho are below the diagonal. Values in parentheses are p values. Significanceindicated by ∗p < .05, ∗∗p < .01, n = 862.

Article Types

RQ2 asked about the frequencies of article types in onlinecancer news because of potential connections to first-orderAS effects. Table 3 shows that research reports (46%) andperson profiles (27%) were the clear leaders in online can-cer news frequencies using single-variable chi-squared teststo compare each individual article type frequency to eachother article type frequency. Also of interest in Table 3 (andin partial answer to RQ4) is the disproportionate number ofclinical trial mentions within reports of research (n = 106;27%), particularly compared to mentions of trials withinawareness articles (n = 4; 7%).

An examination of the cancer continuum across thesedifferent article types revealed that research reports(n = 127), person profiles (n = 31), and policy articles(n = 25) were most frequently about treatment, provid-ing a partial answer to RQ5. A series of two-variablechi-squared tests in Table 2 depict significant differenceswithin article types. Interestingly, when controlling for pri-mary focus totals, research reports about treatment aresignificantly less present than prevention focused arti-cles. Significant differences regarding uncertainty, risk, andclinical trials are also explored using two-variable chi-squared tests in Table 3 (regarding RQ5), providing datafor those interested in second-order AS and/or framingeffects.

Cancer Continuum

H1 predicted that the treatment information would be themost frequent and RQ3 inquired about all of the stagesin the cancer continuum in online cancer news. Table 1details articles that focused primarily on one stage of thecancer continuum. A one-variable chi-squared tests sug-gested that treatment articles (22%) were the most com-mon (H1 was supported), followed by prevention (11%),and then detection (6%). Table 1 presents some significant

differences regarding risk, uncertainty, and clinical trialswithin the cancer continuum in partial answer to RQ4 andRQ5 (potentially linked to first-order AS). The dispersionof mentions of the cancer continuum across different articletypes (potentially second-order AS and/or framing) suggeststhat research reports are the most frequent article types foreach cancer stage; however, awareness articles about detec-tion (n = 13) and policy/politics articles about treatment(n = 25) had notably high frequencies. A still more detailedpicture of the cancer continuum arises through examina-tion of each stage’s relative presence scores. A series ofindependent-samples t-tests revealed that the treatment stageof the cancer continuum received the most attention in thissample of cancer news coverage, both in terms of average(2.94) and total attention (2.14; see Table 1). Table 4 pro-vides correlations regarding the cancer continuum and otherarticle characteristics, depicting several interesting relation-ships in partial answer to RQ5. One finding of note isa negative relationship between treatment and preventioninformation.

DISCUSSION

This content analysis of cancer news information wasdesigned to describe trends in online cancer news withrespect to article types, specific cancers, the cancer contin-uum, uncertainty, and risk. Next, results of this study arereviewed and implications of the findings are discussed withparticular attention to media producers and consumers. Dataregarding frequencies provide significant insight regardingpotential first-order AS effects, whereas data regardingconnections between multiple variables offer those inter-ested in second-order AS and/or framing fodder for futureeffects endeavors. Limitations and future explorations areconsidered for those interested in online cancer news, AS,and/or framing.

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48 HURLEY, RILES, AND SANGALANG

Specific Cancers

Specific cancer data told one clear story: Breast cancerreceived substantially more attention than any other specificcancer. Furthermore, breast cancer articles contained moreinformation about breast cancer than other articles did abouttheir respective cancers. This finding is consistent with pre-vious studies of traditional media (e.g., Jensen et al., 2010),which seems to suggest that breast cancer dominates mediamessages about cancer. Some researchers have shown inter-est in interreality comparisons of cancer news coverage andincidence rates (e.g., Jensen et al., 2010). These data sug-gest that breast cancer followed by digestive (e.g., colon),genitourinary (e.g., prostate), gynecological (e.g., cervical),and skin cancers were the five most covered specific cancers;however, incidence data suggested that digestive cancersfollowed by prostate and breast cancer respectively betterreflect actual incidence rates at the time of the sample (Jemal,2006). This comparison suggests that breast cancer is sig-nificantly overrepresented in online news with respect toits actual presence in the population. Also, digestive andgenitourinary cancers appear to be slightly underrepresentedin Internet-based cancer news. These trends mirror previ-ous findings in traditional media sources (e.g., Jensen et al.,2010). An important next step is examining the implicationsof such interreality incongruities, likely via surveys of per-ceived cancer rates and media exposure, as it is possiblethat an agenda-setting effect is present in those regularlyconsuming cancer media. To be sure, scholars should becareful not to criticize the breast cancer lobby for “steal-ing the show,” but future research should examine the effectsof breast-cancer-dominated cancer coverage. Also, becausearticle characteristics vary significantly by cancer type, workregarding priming, framing, and AS effects would benefitby attending to the data herein to increase external validityby making manipulations that better match real cancer newstrends.

Article Types

The story being told by the article-type data is that researchreports dominate the online news landscape, followed byprofiles of people, of which more than 60% focused oncelebrities. Also of particular note is the frequency ofresearch reports being linked to articles primarily focusingon treatment. Perhaps research on new cancer medications isin demand for financial and personal reasons, as the generalpublic might readily consume information about lucrativedrug investments and/or new treatments that might aid intheir personal recovery efforts; however, the answer to whythis type of content so dominates online cancer news is stilluncertain.

The relative lack of awareness/education articles is trulyunfortunate. Those interested in reducing the number of peo-ple diagnosed with new cancers need to take note. Online

cancer news does not currently appear to be promoting dis-ease awareness. Those interested in the study and creationof media campaigns might take a look at Internet news asa viable place to increase visibility in the general public.Millions of potentially untapped online newsreaders couldbe reached via prevention and detection campaigns waged inthis arena.

Cancer Continuum

Treatment is the story from the cancer continuum data.Treatment information had the highest average and totalpresence of all the cancer stages, followed by detection andsurvivorship. This focus on treatment information in thenews is not a new finding, as several reports have notedthis and contended that this focus might contribute to per-ceptions of cancer being something people typically reactto as opposed to preventing (e.g., Jensen et al., 2010). Thisreaction versus prevention impact warrants further study inexperimental AS, priming, and framing projects. The notablelack of articles with a primary focus on prevention mightexacerbate treatment-centered thinking about cancer, a claimwarranting experimental attention.

Uncertainty, Risk, and Clinical Trials

Uncertainty and risk were common among all types of sto-ries and all stages of the cancer continuum. Perhaps mostinteresting are the overall frequencies, with 65% of cancerarticles containing uncertainty-related information and 55%containing numeric risk statements. Research on the impactof depicting cancer as uncertain certainly is warranted, asare investigations of numeric risk, beliefs about personalsusceptibilities, and likelihood of survival.

Clinical trial mentions are telling, as well. Treatment arti-cles contained the most mentions of clinical trials; however,an informal post hoc analysis of the data revealed that onlyfour of 129 (3%) articles mentioning clinical trials includedany information on how to find and/or participate in one. Themajority of these mentions were in conjunction with com-pleted trials and were not intended for would-be participants.This idea warrants further attention as to why participa-tion in such trials is not being advertised via online newsoutlets.

Limitations and Future Directions

Though we believe the present study creates a valid pic-ture of the online cancer news landscape, notable limitationsdo exist. For example, only four news portals were sam-pled. Though Google News, Yahoo! News, MSNBC.com,and CNN.com were the most popular news portals on theInternet at the time of sampling (Pew Research Center forthe People & the Press, 2006) and remain the four mostpopular in terms of page visits (Pew Research Center’s

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Project for Excellence in Journalism, 2011), other websitesreceive significant patronage (e.g., New York Times online,AOL.com) and warrant study. Along similar lines, this sam-ple was constructed over four contiguous months. It ispossible that cancer coverage could differ by season/timeof year (e.g., more skin cancer coverage in the summer).Broadening the sample to include all four seasons and/or12 months would provide increased clarity and interestingcomparisons.

A few measurement clarifications are also worthy of note.Operationalizing risk as numeric necessarily undercountsthis concept. Also, types of risk were not specified in thiscoding scheme. Future content explorations should examineindividual types of risk (e.g., mortality risk) and nonnu-meric representations of risk. Similarly, uncertainty-relatedstatements were captured using a single presence/absencemeasure and would benefit from further explication andoperationalizations in content projects like this one. Finally,in the calculation of average attention scores, articles wereassigned numbers for the sole purpose of distinguishing arti-cles high in attention versus those low in attention within thissample. This numbering system allows for relative compari-son within this sample only, while the values of the numbersthemselves are meaningless outside of this study. These rel-ative attention calculations and scales could be employedin future studies to aid in between-study comparisons ofrelative attention.

Though limitations exist in all research endeavors, thisproject could serve as a building block for future studiesof media content and effects, particularly as related to ASand framing. Several types of health-related information aresought and found on the Internet, but this research endeavorwas driven by one straightforward question: What doesonline cancer news look like? This project provided answersto that question with respect to article types, specific can-cers, the cancer continuum, risk, and uncertainty. Not onlydo content projects such as this one provide unique insightinto popular human messages, but they also can serve as afoundation for experimental work. Future research endeav-ors could use these data for longitudinal comparisons andfor constructing experimental manipulations regarding ASand framing effects. Messages are fundamental to humancommunication, and knowing the characteristics of certainmessages is an important first step in comprehending theireffects.

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