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Framing the SARS Crisis: A Computer-Assisted Text Analysis of CNN and BBCOnline News Reports of SARSYan Tian PhD & Concetta M. Stewart

Available online: 22 Aug 2006

To cite this article: Yan Tian PhD & Concetta M. Stewart (2005): Framing the SARS Crisis: AComputer-Assisted Text Analysis of CNN and BBC Online News Reports of SARS, Asian Journal ofCommunication, 15:3, 289-301

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Framing the SARS Crisis: AComputer-Assisted Text Analysis ofCNN and BBC Online NewsReports of SARSYan Tian & Concetta M. Stewart

This study compared how CNN and BBC framed the SARS crisis. Framing theory

provided the theoretical framework for the study, and CatPac, a computer program for

text analysis, was used to conduct the analysis. With CatPac the authors were able to

identify the frequency of concepts and the semantic relationship among the highly

frequent concepts from the CNN and BBC reports on SARS. A total of 332 reports from

CNN’s website and 408 reports from BBC’s website were downloaded and analyzed. The

results suggested that while CNN and BBC framed the SARS crisis in different ways,

there were many more similarities. Furthermore, possible factors contributing to these

differences and similarities were discussed.

Keywords: Global Media; SARS; Framing Analysis; CNN; BBC

The Severe Acute Respiratory Syndrome (SARS) crisis was a global concern in the

spring and summer of 2003. With its fatal potential and its spread through

interpersonal contact, SARS had significant impact in a number of countries and

areas including Mainland China, Hong Kong, Taiwan, Singapore, and Canada. These

countries and areas suffered most directly from serious SARS outbreaks, although

indirect effects of this disease were felt worldwide. The effects were not only felt in the

public health and medical systems, but in the economic and political arenas as well

(Janigan, 2003; Koo & Fu, 2003).

Even countries without a direct SARS outbreak were affected by the crisis.

According to the US Federal Reserve, SARS affected the US economy by contributing

to a decrease in tourism in some regions of the country as well as a decline in air

Correspondence to: Yan Tian, PhD, Department of Communication, University of Missouri-St. Louis, One

University Boulevard, St. Louis, MO 63121, USA. Tel.: �/1 314 516-5600; Email: tiany@umsl.edu

Asian Journal of Communication

Vol. 15, No. 3, November 2005, pp. 289�/301

ISSN 0129-2986 (print)/ISSN 1742-0911 (online) # 2005 AMIC/SCI-NTU

DOI: 10.1080/01292980500261605

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travel. Air traffic to Asian destinations declined most significantly. Furthermore,

companies with business in the affected areas were faced with a decrease of profits

(CNN, 2003). Segments of the UK economy suffered as well. The hotel group of the

Intercontinental, Crowne Plaza and Holiday Inn chains in the UK reported a 31%

decrease in half-year profits. SARS, together with the Iraq War, were believed to be

the primary causes for the decrease (BBC, 2003).

Given the global nature of this crisis, the role of global media in framing the crisis

becomes an important topic. The significance of this question lies not only in direct

impact of the SARS disease, but also in the indirect impact brought about by the

global media’s positioning of this crisis. However, the global media coverage and

portrayal of the emergence and spread of this disease is likely to vary by region. It is

important, therefore, to examine these differences to determine whether differences

in culture and proximity played a role in how the outbreak was covered in the media

of a respective country or region.

Consequently, this paper compares the US-based Cable News Network (CNN) and

the British Broadcasting Corporation (BBC)’s framing of this issue through their

online news reports. CNN and BBC are chosen for the following reasons.

First, while there are a number of international and regional news providers, CNN

and BBC are the well-known global news media outlets. They are the most

recognizable brands in global news operation. CNN had tremendous success in

establishing its television news throughout the world more than 20 years ago, while

BBC has enjoyed a reputation for global news reporting since the early 20th century.

Therefore, CNN and BBC are the most appropriate brands for comparison in global

news reporting (Madia, 1998).

Second, it is reasonable to assume that there are some differences between CNN’s

and BBC’s orientation in this issue, given that the US and UK have different

relationships, proximities and interests in such affected areas as Mainland China,

Hong Kong, Taiwan, and Toronto. Hong Kong was under the control of the UK

until 1997, and Toronto directly borders the US. Also, the relationships among

the US, Mainland China, and Taiwan are complicated. While the US and China

are increasingly interconnected in economics and politics on a global level, they

have different perspectives on the status of Taiwan (Roden, 2003; Sutter, 2003).

It is possible that this relationship triangle is reflected in framing the SARS crisis.

Hence, the framing of news from CNN, as the leading US company in global news

reporting, and BBC, as the top company in British news operations, deserves

exploration.

Finally, as both CNN and BBC have archival news on their websites, the

comparison of their framing on a specific issue during a specific period becomes

feasible. This simplifies the process of retrieving archival materials, which is very

difficult to do with television news, as BBC archives only the most recent episodes,

even though CNN has a complete archive of tapes (Madia, 1998).

290 Y. Tian & C. M. Stewart

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Framing Analysis

News reporting is more like telling a story about the world than presenting

information, even though there are factual elements in the stories (Gamson, 1989).

This story-telling process can be explained by the concept of framing. Framing as an

activity is defined as selecting ‘aspects of a perceived reality and mak[ing] them more

salient in a communicating text, in such a way as to promote a particular problem

definition, causal interpretation, moral evaluation, and/or treatment recommenda-

tions’ (Entman, 1993: 52). Frames are the ‘organizing principles that are socially

shared and persistent over time, that work symbolically to meaningfully structure the

social world’ (Reese, 2001: 11).

The fact that different frames define an event or issue causes this same event or

issue to be understood in different ways (Gandy, 2001). Consequently, framing

analysis, by identifying the frames the message producers use, provides a way to

understand how media structure messages and people’s perceptions of the messages

(Miller & Riechert, 2001). Framing analysis is related to the research in gatekeeping

and agenda setting in that they are all concerned with selection and salience

(Scheufele, 1999). However, while gatekeeping and agenda setting research, which

have long histories, have been focused on the selection and salience of issues, framing

analysis pays special attentions to the aspects or attributes of the issues (Scheufele,

1999).

There are two general approaches to framing analysis: the inductive and deductive

approach (Semetko & Valkenburg, 2000). The inductive approach starts with loosely

defined presuppositions of the frames, and it aims to identify all the possible frames

(Gamson, 1992; Semetko & Valkenburg, 2000). The deductive approach starts with

stronger presuppositions. It predefines certain frames, and examines the occurrence

of these frames in the news (Semetko & Valkenburg, 2000). To increase the objectivity

of the research, this study chooses the inductive approach. In this case, the authors do

not pre-specify certain frames, and leave the computer software to identify all the

possible frames.

Computer-Assisted Text Analysis

The definition of computer-assisted text analysis is based on the concept of text

analysis, which refers to ‘a research technique for making replicable and valid

inferences from text to their context’ (Popping, 2000: 7). Built upon Popping’s

definition of text analysis, computer-assisted text analysis can therefore be defined as

a research technique involving the essential use of computer software for making

replicable and valid inferences from text to their context.

The application of information-processing technology in communication studies

has a history of over 50 years. In the 1950s, Pool, Lasswell, and other researchers used

punch cards and tabulation machines for their text analysis (Stone, 1997). However,

the real computer-assisted text analysis was not employed in earnest until the 1960s,

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when researchers began to use mainframe computer programs to count words or

phrases in textual data (Popping, 2000). In 1966, the General Inquirer, a computer

program for text analysis, was introduced to the public (Kelle, 1995). Utilization of

computer-assisted text analysis in research has continued since then (Stone, 1997),

but there were not many computer programs for text analysis before the 1980s (Prein,

Kelle, & Bird, 1995). The main approach to computer-assisted text analysis before

1980 was thematic text analysis. It was the invention of microcomputers and

computer packages for software development in the1980s that enabled the emergence

of the new approaches to text analysis, semantic text analysis and network text

analysis. These two new approaches, together with thematic text analysis, have

become the primary applications of computer-assisted text analysis ever since

(Popping, 2000; Roberts, 1997).

Compared with manual text analysis, computer-assisted text analysis can be

thought of as potentially ‘more objective’. With computer programs such as CatPac,

the research does not need to pre-read the texts and code them according to pre-

specified rules (Woelfel & Stoyanoff, 2000). Without the pre-specification of

categories or concepts to be identified in the text, computer-assisted text analysis is

free from ‘the presuppositions knowingly or unknowingly imposed by the researcher’

(Murphy, 2001: 284). This independence from the researcher’s presuppositions

increases the objectivity and therefore validity of research.

Furthermore, computer-assisted text analysis can improve the reliability of

research. The process of computer-assisted text analysis can be repeated by other

researchers, and, with the fixed algorithms in the computer software, the results

should be the same for any researcher with the same text. Therefore, the reliability of

computer-assisted text analysis can be 100% (Shapiro, 1997). For example, the

‘dendograms’ (as pictures from hierarchical cluster analysis of concepts) and the

clusters from CatPac would be same with the same texts, since the output is

independent of the researcher (Murphy, 2001). With traditional manual content

analysis, inter-coder reliability is always a concern. Coders need to be trained, and

sometimes the codebook, the protocol for coding, has to be revised to reach an

acceptable level of inter-coder reliability (Popping, 2000).

In addition, computer-assisted text analysis can improve the efficiency of research,

making computer-assisted text analysis a cost-effective approach (Shapiro, 1997).

With the computer’s superior capacity in data processing, far more data can be

handled and in much shorter timeframes. For example, in Murphy’s study (2001),

CatPac analyzed 38,000 words of transcribed testimony and 82,000 words in another

project (Murphy & Maynard, 2000), which would be a major undertaking for

researchers using traditional content analysis method. Compared with the capacity

and efficiency of computer-assisted text analysis, manual content analysis can be very

time-consuming and laborious (Woelfel & Stoyanoff, 2000).

Computer-assisted text analysis is an especially useful approach for studying

framing, since frames can be identified by the presence or absence of certain themes

(Entman, 1993). Thematic text analysis assumes that the frequency of the occurred

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themes in a text is related to the interest of the text’s producer. Consequently, it is an

ideal tool for presenting the occurrences and prominence of the themes in a text

(Popping, 2000; Stone, 1997). Moreover, to explain the use of particular frames in a

text, the relationship among the concepts in a text and the relationship between those

concepts and the text producer’s interests have to be recognized (Gandy, 2001).

Network text analysis positions each concept in a network according to the semantic

interrelationships among the concepts in a text. This approach, therefore, provides a

tool to interpret these relationships as well as the corresponding social phenomena, as

evidenced by previous studies (e.g. Miller & Riechert, 2001; Murphy, 2001; Murphy &

Maynard, 2000).

As this study is aimed at understanding how CNN and BBC frame the SARS crisis,

computer-assisted text analysis is believed to be the most appropriate approach, given

its benefits mentioned above. Hence, this study will use computer software to analyze

the online news of CNN and BBC on SARS, and to compare the framing process of

CNN and BBC on the issue of SARS. The research questions guiding this analysis are:

RQ1: How did CNN and BBC frame the crisis of SARS?

RQ2: Were there similarities or differences in their framing?

RQ3: What factors contributed to these similarities and differences in theirframing?

Method

In this section, we will describe the sample, as well as explain the capabilities of the

computer software and how it was used to conduct the analysis of the news archives.

Samples

Samples were downloaded from both the CNN and BBC websites. The search engine

on each website was used to locate the reports on SARS. With the keyword ‘SARS’,

CNN had 525 reports up to October 7, while BBC had 425 reports. This study

focused on the reports between March 1, 2003 and September 1, 2003, when the

outbreak was most severe and the world was most concerned with the SARS crisis.

Therefore, only the reports during that period were downloaded. Specifically, CNN

had 339 links during those six months, out of which 17 links were inactive. Therefore,

only 322 reports were actually downloaded from the CNN website. BBC had 412 links

during those six months. All these links were active, but four of them were identified

as unrelated to SARS. Thus, 408 reports were actually downloaded from the BBC

website.

All the reports were downloaded from October 7 to October 10, 2003, and these

downloaded web pages were then converted into text files. All the files from CNN

were combined into one big text file, and all of the files from BBC were combined

into another big text file. Only the main body of each report was included in these

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two text files. Information about the sources of the reports (e.g. ‘CNN’s Mike Chinoy

contributed to this report’, or ‘From the newsroom of the BBC World Service’)

was not included in the text, since this information was not significant to the

study. Excluding non-significant portions of the texts helps to provide uniform

criteria and focus on the most important content in text analysis (Murphy, 2001).

The final text file for CNN had 35,618 words, and the final text file for BBC had

30,154 words.

Computer Software

CatPac (Category Package), a computer program for text analysis, was used to

analyze the two text files for CNN and BBC, respectively. As a neural network

program, CatPac cannot only identify the occurrences of each concept, as traditional

thematic analysis does, but can also reveal the semantic relationship among the

concepts in a text through clustering the concepts in a text (Murphy, 2001; Popping,

2000; Woelfel & Stoyanoff, 2000). Each concept is treated as a neuron in a network,

and the network is composed of neurons (concepts) and the semantic relationships

among these concepts. The clusters indicate the relationships among the concepts

within the network. Or, in other words, concepts in a same cluster are believed to be

more semantically related to each other than concepts in different clusters (Murphy,

2001; Woelfel & Stoyanoff, 2000). With CatPac, the researchers do not pre-specify

some categories or pre-code some information in a text, as traditional quantitative

content analysis does. Instead, the computer program categorizes the concepts. This

increases the reliability and objectivity of research (Murphy, 2001; Woelfel &

Stoyanoff, 2000).

Operation

CatPac has a default exclude file, in which the meaningless words such as prepositions

and verbs of being are included. The words in the exclude file are not analyzed

(Murphy, 2001). For this study, words such as Monday and according were added into

the exclude file, and the analysis therefore can focus on more important concepts.

The following changes were made to the original CNN and BBC texts to keep the

consistency of concepts and to improve the validity of the research:

. Severe Acute Respiratory Syndrome was replaced with SARS , so that all instances of

the two terms would be combined;

. Hong Kong and HK were replaced by HongKong , given that both Hong and Kong

could be used separately in some Asian languages, and this study was interested in

the position of Hong Kong as one concept in two networks;

. similarly, United States was replaced by USA , United Kingdom was replaced by

UK , and United Nations was replaced by UnitedNations . Finally, World Health

Organization/Organisation and WHO were replaced by WHORG , since WHO

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could be regarded as ‘who’, which has been put into the exclude file, by the

computer program.

The program was asked to rank the top 40 words with the highest frequency for the

CNN and BBC texts, respectively. Though the researcher can ask CatPac to rank as

many words as possible with their frequencies, 40 was the appropriate number for the

optimal interpretability. Furthermore, the program was asked to perform cluster

analysis for these 40 words to identify the semantic connections among these words

for each of the texts. Ward’s method, or minimum various cluster analysis, was used

to get smaller and easy-to-interpret clusters (Murphy, 2001). Then the outputs for

both texts were compared.

Results

The bottom 10 arrows were chosen as the basic dividing point of clusters for the

dendograms from both texts. Based on this bottom row, more clusters were identified

with their relative positions in the dendograms. The clusters from the dendograms

were summarized in Table 1 and Table 2.

Similarities

The main themes of the CNN text and BBC text were similar. Specifically, they were

similar in the following ways.

They were both concerned with the spread of SARS

CNN covered issues on ‘symptoms’, ‘patient’, ‘virus’, ‘outbreak’, and ‘reported’, while

BBC reported issues on ‘disease’, ‘infected’, ‘outbreak’, ‘cases’, ‘virus’, and ‘reported’. In

the CNN text, the term ‘SARS’ represented 13.7% of all the occurrences of words, and

it accounted for 15.1% of all the occurrences in the BBC text.

Table 1 Clusters from the CNN Text

Cluster # Cluster theme Keywords

Cluster 1 Beijing Beijing, millions, last, casesCluster 2 Public health disease, health, SARS, WHOrg, Hong Kong,

peopleCluster 3 Symptoms, statistics, & effects

on travelsymptoms, patient, virus, outbreak, officials,reported, China, travel, number

Cluster 4 Chinese government Chinese, Taiwan, first, full, governmentCluster 5 Toronto story, TorontoCluster 6 Economic impacts case, countries, death, hit, Yen, down, higherCluster 7 Treatment and control world, control, city, hospital, Singapore,

infected, spread

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They both discussed SARS’ impact on public health and the travel industry

‘Health’ and ‘travel’ were frequent words in both texts. Both texts reported how SARS

threatened public health and its negative impact on travel. ‘Health’ was the fourth

most frequent word in the CCN text, and the eighth most frequent in the BBC text.

‘Travel’ was the 12th most frequent word in the CNN text, and 15th most frequent in

the BBC text.

They both framed the crisis from a global perspective

‘Countries’ and ‘world’ were frequent in both texts. Furthermore, ‘WHO’ was the

third most frequent word in both the CNN and BBC texts. The use of these terms is

consistent with SARS’ potential to spread worldwide, and WHO was the most

important international organization dealing with this crisis.

The position of Hong Kong was special in both texts

Hong Kong was a prominent concept in both texts, and its position is special in both

dendograms. Hong Kong was not close to China, or Chinese, or Taiwan, in either the

CNN text or the BBC text. Actually, in the CNN text, ‘Chinese’ and ‘Taiwan’ were in a

same cluster, next to each other, but Hong Kong was not. Instead, Hong Kong was in

a same cluster with WHO in both texts, which suggested Hong Kong’s special role in

both CNN’s and BBC’s framing.

Differences

Despite the above similarities between the CNN and BBC reports, there were

differences in their respective framing of the SARS crisis. These differences are

presented in the following section.

CNN had a cluster about SARS’ economic impact, while BBC did not

The sixth cluster of CNN focused on the economic impact of SARS including such

keywords as ‘case’, ‘countries’, ‘death’, ‘hit’, ‘Yen’, ‘down’, and ‘higher’. The outbreak

and control of SARS seemed to affect the fluctuation of the Yen and therefore the

Table 2 Clusters from the BBC Text

Cluster # Cluster theme Keywords

Cluster 1 Public health affected, public, country, death, authoritiesCluster 2 world Canada, Asia, world, countriesCluster 3 Singapore quarantine, Singapore, spreadCluster 4 China & Toronto Beijing, China’s, Chinese, government, reported, far,

officials, patients, Toronto, city, hospital, numberCluster 5 Hong Kong & WHO cases, Hong Kong, people, WHOrg, SARS, virusCluster 6 Outbreak & impacts disease, China, infected, illness, outbreak, first, health,

died, travel

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whole Asian economy. BBC, however, did not have such a corresponding cluster. This

does not suggest that BBC did not report SARS’ impact on the economy, since it was

highly likely that they did report such impact. However, in the BBC text the economic

impact of SARS was not as prominently featured as it was in the CNN text, as

evidenced by both the frequency of words and by the cluster analysis.

‘Control’ was a more noticeable theme in the CNN text than in the BBC text

CNN made more frequent mention of what was being done to control the outbreak.

‘Control’ was the 29th most frequent word in the CNN text, whereas it was not on the

top 40 list of the BBC text. Moreover, in the dendogram for CNN, ‘control’ was in a

same cluster with many important concepts such as ‘world’, ‘city’, ‘hospital’,

‘Singapore’, ‘infected’, and ‘spread’.

The role of Taiwan was different in the CNN and BBC texts

Taiwan was the 19th most frequent word in the CNN text, whereas it was not even on

the list of the 40 most frequent words for the BBC text. Again, this suggests that the

role of Taiwan in the SARS crisis was more prominent in the CNN text than in the

BBC text.

The position of Toronto was different in the CNN and BBC texts

In the CNN text, Toronto was with ‘story’ in a separate cluster, whereas it was in a

cluster together with ‘China’s’ and ‘Chinese’ in the BBC text. This suggested that

though both Toronto and China suffered heavily from SARS, they were in different

positions within the CNN’s framework. Within BBC’s framework, however, the two

terms were much closer.

Discussion

This study revealed that CNN and BBC framed the SARS crisis in similar ways. They

both focused on the SARS outbreak and its effects on public health and the medical

system, and they both framed the issue from a global perspective. WHO played a key

role in both texts, and public health worldwide was a common concern.

Interestingly, the position of Hong Kong was similar in CNN’s and BBC’s online

news, though the UK had a much closer relationship with Hong Kong in politics,

economics, and culture than had the US. In the dendograms for the CNN and BBC

text, the concept of Hong Kong and China was not in a same cluster. Instead, Hong

Kong was close to WHO in both dendograms. This seemed to suggest that in both

CNN’s and BBC’s framework, Hong Kong was still like a link between Mainland

China and Western countries. Before China’s open-door policy, Hong Kong served as

‘a bridge’ between China and the Western world. This role did not seem to differ in

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CNN’s or BBC’s view in the case of the SARS crisis, even though the SARS outbreak

occurred six years after the transition of Hong Kong’s power from the UK to China.

However, CNN and BBC also framed the SARS crisis in different ways. With a

cluster on SARS’ economic impact, CNN seemed to be more concerned with the

economic dimension of this crisis than BBC. Or in other words, the economic

dimension of this crisis was more prominent in the CNN news than in the BBC news.

Meanwhile, CNN made frequent mention of what was being done to control the

outbreak. This may suggest a difference in the two cultures’ views of disease as a

social, medical and economic phenomenon.

Similarly, Taiwan was a more prominent theme on the CNN map than on the BBC

map. This is consistent with the fact that the US has a closer relationship with Taiwan

than does the UK. The US government has been providing military support to

Taiwan ever since the outbreak of the Korean War (Chai, 2002). Though President

Carter recognized that People’s Republic of China is the only legal government of

China in 1978, the US Congress passed the Taiwan Relations Act to protect Taiwan

from attack from Mainland China in 1979 (Chai, 2002). Today’s US government

follows the same model. The Bush Administration policy in the US�/China

relationship is the ‘one China’ policy, which is a basic principle for Chinese

government, though the United States continues to increase military and other

support for Taiwan (Sutter, 2003). Given the relationship between Taiwan and the

US, not surprisingly Taiwan was a prominent theme in the CNN’s news reports on

SARS, whereas the BBC, a UK-based corporation, did not show the same concern.

Nevertheless, even on the CNN’s dendogram the concept of Taiwan and China were

next to each other in the same cluster, which suggests that, at least on the issue of

SARS, the US media giant did not differentiate Mainland China and Taiwan.

Toronto, however, was another matter entirely. Toronto was almost by itself in the

CNN dendogram, while it was together with ‘China’s’ and ‘Chinese’ in the BBC text.

This indicated that from BBC’s point of view, Toronto was in a similar position as

other SARS-affected areas such as China, though it was quite special from CNN’s

perspective. This could be related to the fact that the US shares a common border

with Canada, and more specifically the close proximity of Toronto to the US. There is

also a very strong economic and cultural connection with Toronto. The US and

Canada are each other’s largest trading partners and they share a free trade zone

through the North American Free Trade Agreement. In addition, there are strong

media ties between the countries, particularly with the far-reaching broadcast signals

of US radio and television.

Implications

This study is limited in that only frequency ranking and cluster analysis were

conducted for the texts. In future studies, more sophisticated techniques such as

multidimensional scaling and interactive neural network analysis are going to be used

to identify more subtle frames and connections between key concepts of the texts.

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Despite the limitation, this study suggests that, although we purport to live in a global

information age with media systems that transcend national borders, there are still

differences in coverage of both national and international news stories. Additionally,

we see that ‘country of origin’ for the particular media outlet can still make a

difference in how various news stories are presented. This illustrates the gatekeeping

and agenda setting functions of news media as well as how different aspects or

attributes of the same issue are presented through different frames. In future research

on this topic, we will examine how the SARS outbreak was covered in Asian media,

what if any differences there were among those outlets, and then compare them with

CNN and the BBC. The reason for this approach is that, while we saw some

important differences between CNN and the BBC, we saw many more similarities.

Consequently, it is important that we now compare these results to non-Western*/

more particularly, Asian*/media outlets to see what the real differences are. It is

reasonable to assume that media in Asian countries such as China and Vietnam frame

this issue in very different ways from the Western media. It is also worthwhile to

examine if the flow of information in this case is a ‘one-way unbalanced traffic’

between the Western and Asian countries (Frederick, 1992).

This study also suggests that computer programs could be useful tools for text

analysis. A popular criticism of computer-assisted text analysis is that meaning of text

is not numerical, and the process of understanding meaning is not algorithmic, and

therefore it cannot be computerized (Kelle, 1995). However, as in this study,

computer text analysis is simply one means of analysis in a larger investigation of an

issue. In this case, the authors employed both framing theory and a historical

perspective in the interpretation of the results. In addition, computer-assisted text

analysis can provide a means for identifying areas of a text that would benefit from

more in-depth, manual examination.

A quantitative approach to text analysis also provides insights for interpreting

the meaning of texts from a different perspective than the qualitative approach, as the

quantitative research helps to systemize knowledge (Woelfel & Stoyanoff, 2000).

The structure of the ‘unstructured’ texts needs to be identified (Kelle, 1995: 3). The

clusters in the dendograms of this study helped to identify the similar and different

frames used by CNN and BBC. More importantly, these clusters originated from the

computer program’s automatic computation, which was more objective than the

traditional manual text analysis with pre-specified categories (Murphy, 2001; Woelfel

& Stoyanoff, 2000).

Computer-assisted text analysis is especially useful in this information age, when

we have more than enough information but not enough understanding. With

computer’s superior capacity in data processing, computer-assisted text analysis can

improve the efficiency of research (Murphy, 2001; Woelfel & Stoyanoff, 2000). In this

study, CatPac analyzed 322 reports (35,618 words) from the CNN website and 408

reports (30,154 words) from the BBC website. This could be extremely time-

consuming for manual text analysis; in fact, for large data sets such analysis would be

practically impossible. The Internet is also providing computer text analysis with

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exciting opportunities. With the ready availability of digital verbal sources on the

Internet, computer-assisted text analysis can be one of the most useful approaches to

understanding and structuring the vast amount of online information.

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