WORLD WIDE WEB COVERAGE OF

AGRICULTURAL ISSUES: A CONTENT ANALYSIS

 

Clint Saunders

Cindy Akers

Jacqui Haygood

David Lawver

Texas Tech University

Abstract

 

            Convenience is making the Internet a popular means of disseminating information, and agricultural news is no exception.  It is vital that the American public receives an accurate image of the food and fiber system, which is dependent on the agricultural literacy of individuals in the media.  Therefore, researchers studied the coverage of agricultural issues on the World Wide Web to evaluate bias.

 

            The studied utilized content analysis methodology based on the Hayakawa-Lowry news bias categories to code the identified articles.  

 

            The majority (55%) of these articles proved to be report sentences, which are factual and verifiable sentences.  Thirty-seven percent of the sentences were judgment sentences, which are expressions of the writer’s or quoted speaker’s opinions.  Only 5% of the sentences were categorized as inference sentences, which are subjective and immediately verifiable sentences.

           

Results of this study show the importance of agricultural literacy in the media field in order to better report about the industry.  More factual statements by reporters will help provide a more accurate image of the agricultural industry.

 

Introduction and Theoretical Framework

 

            Someway, somehow, agriculture affects everyone’s life on an everyday basis.  However, Terry and Lawver (1995) stated that a substantial amount of attention has been given to the fact that the American society is “agriculturally ignorant.”  With each passing generation, this country has become one step further removed from direct ties to production agriculture (Flood & Elliot, 1994).

 

            Today’s world is becoming more and more technologically advanced, and agriculture is no exception.  These changes, and many more, are propelling agriculture to new levels.  Because of these changes, and many more, the need for agricultural literacy is becoming more important. According to the USDA Agricultural Statistics Service (2001), the percent of the U.S. population involved in production agriculture was 1.8% in the 1990s, compared to 16% in the 1950s. 

 

Due to dramatic decreases in the farming and ranching population, it is vital that the general public has accurate perceptions about agriculture, because of its impact on our society, the economy, the environment, and personal health (Terry & Lawver, 1995).  “Reporters must strive to be neutral observers, collecting information and reporting it to let readers form their own opinions” (Baker-Woods et al., 1997, p. 73).  Writers should present their stories by portraying both sides of the issue equally and excluding their personal opinion of the subject (Sitton, 2000).  Numerous studies have been conducted investigating the role and impact of the press in delivering agricultural news and information.

 

Journalists have a responsibility to report news both accurately and fairly.  If they fail in their duties, responsible reporting and consumption of agricultural news will not occur.  Likewise, misinformed individuals may make important decisions affecting the food and fiber industry.  (Whitaker & Dyer, 1998, p. 445)

 

            Journalists have many different means of disseminating information: newspapers, television, radio, and the World Wide Web.  According to the Office of the U.S. Press Secretary (2000), almost one-half of all American households now use the Internet, and more than 700 new households connect every hour.

 

A simplified version of the Theory of Reasoned Action is shown in Figure 1.  Since 1967, researchers have utilized this theory to explain and predict a variety of human behaviors.  Based on the premise that humans are rational and that the behaviors being explored are under volitional control, the theory provides a construct that links individual beliefs, attitudes, and behavior (Fishbein, Middlestadt, & Hitchcock, 1994). 

           

            The theory of reasoned action depicts the process a person goes through to reach a desired outcome or behavior.  This process is extremely important to those studying the perceptions of agriculture.  The theory of reasoned action will help to form a person’s attitude or perception, which in turn leads to a specific behavior or no behavior at all.

           

 

 

 

 

 

 

           

 

 

 

 

 

 

 

 

 

 

Figure 1:  Theory of Reasoned Action Model (simplified version).  Source: Adapted from Ajzen and Fishbein (1980, p. 84). 

 

Purpose and Objectives

 

            The purpose of this study was to evaluate the coverage of agriculture available by popular agricultural websites on the World Wide Web for one calendar month.  The following objectives were formulated to accomplish the purpose of this study:

 

1.      To identify all the articles written about agriculture on the most popular agricultural websites on the World Wide Web for a selected month;

2.      To categorize World Wide Web articles into agricultural literacy concept areas;

3.      To categorize the sentences in the identified articles using the Hayakawa-Lowry News Bias Categories and;

4.      To determine bias of judgment statements in the identified articles.

 

Methods

 

            A descriptive research design was used for this study.  Ary, Jacobs, and Razavieh (1996) state that descriptive research asks questions concerning the nature, incidence, or distribution of educational variables and relationships among these variables.  This study sought to evaluate agricultural articles taken from popular agricultural websites; thus, a descriptive design was deemed the most appropriate.

 

            In 2001, AgWeb conducted market research by surveying several hundred randomly selected farmers and ranchers regarding their Internet use (M. Gibson, personal communication, December 4, 2001).  They found the most accessed news sites included AgWeb.com, AgDayta.com, and Agriculture.com.  Therefore, those three websites were used for the purpose of this study. 

        

All articles, market reports, weather reports, etc. posted under the news section of each of the three websites were downloaded for January 2002, totaling 1,132 items. Results from this particular month should not be inferred to other months of the year.

 

            A panel of three then sorted through the items and selected news articles. For the purpose of this study news articles was defined as an article that tells a story for the purpose of informing.  All market reports, weather reports, links, and other items that did not fit the definition of news article were deleted from the population.

 

            The population of the study consisted of all news articles retrieved from the three chosen websites for January 2002 (N=821).  A systematic random sample (n=262) was selected (Krejcie & Morgan, 1970) according to the population size.

 

 

            To conduct this study, a content analysis based on the Hayakawa-Lowry news bias categories was used.

 

            S.I. Hayakawa (1940) developed a system to analyze sentences in news articles.  He placed the sentences into one of three categories: (a) report sentences, (b) inference sentences, and (c) judgment sentences.

 

            Lowry (1971) expanded Hayakawa’s method, which includes six new sentence categories, making a total of nine categories for the Hayakawa-Lowry method.  Lowry took into consideration attribution of the information and reporter bias.  The nine categories include:

 

Reported Attributed Sentences—Information which is factual and attributed to the source (Lowry, 1971).

Report Unattributed Sentences—Information which is factual without citing someone as the source (Lowry, 1971).

Inference Labeled Sentences—Statements about the unknown based on the known.  These are often interpretations or generalizations of events.  Labeled inferences use “tip-off” specific words such as appear, could, may, perhaps, possible…to let the reader know the information is subjective to some extent (Lowry, 1971).

Inference Unlabeled Sentences—Statements about the unknown based on the known.  Often interpretations or generalizations of events, without “tip-off” words.  Considered to have more bias because the “tip-off” is not used to “warn” the reader (Lowry, 1971).

Judgment Attributed, Favorable Sentences—Statements of the writer’s approval or disapproval of an event, person, object, or situation that are attributed to a source and favorable toward the subject (Lowry, 1971).

 Judgment Attributed, Unfavorable Sentences—Statements of the writer’s approval or disapproval of an event, person, object, or situation that are attributed to a source and unfavorable toward the subject (Lowry, 1971).

Judgment Unattributed, Favorable Sentences—Statements of the writer’s approval or disapproval of an event, person, object, or situation that are not attributed to a source, but are favorable toward the subject (Lowry, 1971).

Judgment Unattributed, Unfavorable Sentences—Statements of the writer’s approval or disapproval of an event, person, objective, or situation that are not attributed to the source, and unfavorable to the subject (Lowry, 1971).

Other Sentences—All other sentences.  These sentences normally include rhetorical questions and introductory statements (Lowry, 1971).

 

Lowry used a two-part study at Liberty University and Ohio University to successfully establish the construct validity of the Hayakawa-Lowry News Bias Categories.  Lowry (1985) stated:

 

The assumptions underlying the Hayakawa-Lowry category system were twice put to the test, and a group of subjects…for the most part, evaluated the news stories and sentences as predicted.  Thus, the results strongly suggest that the differences measured by these categories, when used by researchers in content analysis studies, are differences that do indeed make a meaningful difference to news consumers.  (p. 580)

 

Lowry dealt with problems of inter-rater reliability through the development of a tested rater manual (Terry et al., 1996).  Figure 2 shows how sentences are classified using the Hayakawa-Lowry method.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


Figure 2:  Hayakawa-Lowry Method. Source: Haygood, Hagins, Akers, and Kieth 2002.

 

            A panel of three experts was used to code the identified articles to ensure coder reliability.  The experts were trained in the Hayakawa-Lowry News Bias Categories.  Each sentence of the identified articles was coded using the Hayakawa-Lowry News Bias Categories. Each expert coded all identified articles.  All coding was compared.  Experts reviewed discrepancies until a consensus was reached on the code assigned to each sentence. 

 

            The agricultural literacy concept areas developed by Terry et al., (1996). All were used to categorize the articles into separate groups.

 

            Descriptive statistics were used.  Statistical analysis was performed using Microsoftâ Excel.  

 

Results

 

Objective One Findings

 

            Agricultural news articles were collected during the month of January 2002, for a total of 821 articles.  The average number of agricultural news stories posted daily was 35.7.  The number of articles varied daily.

 

AgWeb posted the highest number of agricultural news articles (434), AgDayta posted the second highest (222), and AgOnline posted the least (165). 

 

            The sample size, which was determined by a systematic random sampling procedure, used for this study was 262.  AgWeb (n=152) posted the largest number of agricultural news stories for the month of January 2002, representing 58% of the sample size. AgDayta (n=61) had the second most agricultural news stories, with 23% of the sample size. AgOnline (n=49) represented 19% of the sample size. 

 

Table 1 indicates the amount of agricultural news stories that were randomly selected from each of the three websites.  The total number of sentences is also included in the table.

 

            The total number of sentences in the selected articles was 3,360.  The average number of sentences per article was 12.82.

 

Table 1:  Number of Agricultural News Articles Selected from each Website

Website

Number of Articles

%

Number of Sentences

AgDayta

61

23

497

AgOnline

49

19

545

AgWeb

152

58

2,318

TOTAL

262

100

3,360

  

Objective Two Findings

 

            All 262 articles were placed into primary and secondary concept areas.  The largest category in the primary concept area was the marketing category (n=69), which consisted of 26% of the stories.  The plant science category was the second largest primary concept area (n=44), representing 17% of the agricultural news stories.  The animal science category (n=43) consisted of 16% of the stories in the primary concept area.  The natural resources category (n=36) contained 14% of the news stories, while the public policy group (n=34) consisted of 13% of the news stories.  The significance category (n=26) contained 10% of the news stories, and the processing category (n=10) had the least amount with 4% of the agricultural news stories.

 

            In the secondary concept area, the significance category (n=99) had the most agricultural news stories with 38%.  The plant science category (n=61) represented 23% of the news stories.  The animal science category (n=44) characterized 17% of the news stories, while the marketing category (n=36) was indicative of 14% of the sample.  The smallest categories were the natural resources category (n=10), the processing category (n=7), and the public policy category (n=5), representing 4%, 2%, and 2% respectively.  Table 2 indicates this information.

 

Table 2:  Concept Areas According to Terry et al. (1996)

Category

Primary

%

Secondary

%

Significance

26

10

99

38

Animal Science

43

16

44

17

Plant Science

44

17

61

23

Natural Resources

36

14

10

4

Public Policy

34

13

5

2

Marketing

69

26

36

14

Processing

10

4

7

2

TOTAL

262

100

262

100

    

Objective Three Findings

 

            Report sentences (n=1,856) represented 55% of the total sentences, inference sentences (n=154) represented 5% of the total sentences, and judgment sentences (n=1,245) represented 37% of the total sentences.  Hayakawa states that reporters who write judgment sentences usually use bias in their writing.  Judgment sentences can be attributed, unattributed, favorable, and/or unfavorable.  The other sentences (n=105) represented 3% of the total sentences.  Table 3 shows the breakdown of sentence types.

 

Table 3:  Sentence Types

Sentence Type

Number of Sentences

%

Report

1,856

55

Inference

154

5

Judgment

1,245

37

Other

105

3

TOTAL

3360

100

 

           

            Nine different categories make up the subcategories of the original categories: report, inference, judgment, and other (Lowry, 1971).  Report attributed sentences (n=755) represented 22% of the total sentences.  The largest category was the report unattributed sentences (n=1,101), representing 33% of the total sentences.  The inference labeled sentences (n=66) represented 2% of the total sentences, the smallest of the nine categories.  Inference unlabeled sentences (n=88) represented 3% of the total sentences. The judgment attributed, favorable sentences (n=620) represented 18% of the total sentences.   Judgment, attributed, unfavorable sentences (n=351) consisted of 10% of the total sentences.  Judgment unattributed, favorable sentences (n=190) represented 6% of the total sentences.  The judgment unattributed, unfavorable sentences (n=84) category comprised 3% of the total sentences.  Other sentences (n=105) represented 3% of the total sentences in the agricultural news stories.  Table 4 shows the breakdown of the nine sentence categories.

 

 

 

Table 4: Categories of Sentences

Sentence Categories

Number of Sentences

%

Report Attributed

755

22

Report Unattributed

1,101

33

Inference Labeled

66

2

Inference Unlabeled

88

3

Judgment Attributed, Favorable

620

18

Judgment Attributed, Unfavorable

351

10

Judgment Unattributed, Favorable

190

6

Judgment Unattributed, Unfavorable

84

3

Other

105

3

TOTAL

3,360

100

 

Objective Four Findings

 

            Judgment sentences (n=1,245) represented 37% of the total sentences.  Judgment attributed, favorable sentences (n=620) represented the largest percentage of judgment sentences with 50% of the total judgment sentences.  Judgment attributed, unfavorable (n=351) had the second largest percentage of judgment sentences, representing 28% of the total judgment sentences.  Judgment unattributed, favorable (n=190) consisted of 15% of the total judgment sentences.  Judgment unattributed, unfavorable (n=84) was the smallest category, representing 7% of the total judgment sentences found in the agricultural news stories.

 

Overall, 78% of all judgment sentences were attributed to a source, leaving 22% of the total sentences unattributed.  Also, 65% of all judgment sentences were favorable to the subject. Therefore, 35% of the total judgment sentences were unfavorable towards the subject. Table 5 shows the breakdown of judgment sentences.

 

Table 5:  Judgment Sentences

Judgment Sentences

Number of Sentences

%

Attributed, Favorable

620

50

Attributed, Unfavorable

351

28

Unattributed, Favorable

190

15

Unattributed, Unfavorable

84

7

TOTAL

1,245

100

 

Conclusions

 

Objective One Conclusions

 

1.      On average, there were about 7 articles a day posted on the selected websites.

2.      AgWeb provides the most agricultural coverage out of the three agricultural websites.  AgWeb posted 434 articles in January 2002.  AgDayta posted the second most articles with 222 articles in January 2002.  AgOnline posted 165 articles in January 2002.

 

Objective Two Conclusions

 

1.      There is a diverse range of topics written about agriculture and posted on the selected websites.

2.      The most frequently written about topic during January 2002 was the marketing category, with 26% of the articles representing this category.

3.      The least frequent written about topic during January 2002 was the processing category, with 4% of the articles representing this category.

 

Objective Three Conclusions

           

1.      A majority of the sentences were report statements, which are factual and verifiable sentences.  Report sentences characterized 55% of the total sentences.  These sentences are desirable, and report sentences should become more frequent.

2.      Inference sentences, which are subjective and immediately verifiable sentences, represented a mere 5% of the total sentences.  These sentences should be avoided when writing about agricultural topics. 

3.      Agricultural reporters are using their opinions when writing agricultural articles, and these are referred to as judgment sentences; expressions of the writer’s or quoted speaker’s opinions.  Thirty-seven percent of the sentences were judgment sentences.  Agricultural reporters should refrain from including their personal opinions when reporting about agricultural issues in order to paint a more accurate picture of agriculture. 

4.      The “other” sentence category represented a small portion of the sentences.  Only 3% of the total sentences were included in the “other” category, which are normally rhetorical questions and introductory statements.

5.      The agricultural reporters that wrote the articles used in this study wrote more report sentences than any of the other categories.  Therefore, a factual image of agriculture is being conveyed.

6.      In the report category, there were more report sentences not attributed to a source than were attributed to a source.  Twenty-two percent of the total sentences were report attributed, while 33% of the total sentences were report unattributed.  More sentences should be attributed to a source.

7.      The inference labeled and inference unlabeled categories were very close, representing 2% and 3%, respectively.  These sentences should be avoided.  Agricultural reporters are limiting the use of inference sentences. 

8.      The most frequently used sentence type in the judgment category was judgment attributed, favorable, representing 18% of the total sentences.  Therefore, there were more attributed judgment sentences than there were unattributed judgment sentences.  Also, there were more favorable judgment sentences than there were unfavorable judgment sentences.         

 

Objective Four Conclusions

 

1.      Agricultural reporters are using their personal opinions when writing about agriculture.

2.      This study concluded that agricultural reporters are writing more positive bias towards agriculture than negative bias.  Half (50%) of the judgment sentences were attributed and favorable.  Sixty-five percent of all judgment sentences were favorable, while 35% were unfavorable.

3.      Also, agricultural reporters are attributing to a source more often than not.   Seventy-eight percent of all judgment sentences were attributed to a source.

 

Recommendations

 

1.      Reporters should be aware of the accuracy of the information they report so that they provide the general public with factual information by writing report sentences and attributing to a source.

2.      This study should be replicated every other year in order to stay current on the progress of agricultural reporters.

3.      This study should be expanded into other modes of the media, including radio, television, newspaper, etc.

4.      This study could additionally expand to other industries, for example, the medical industry.

 

References

 

Ajzen, Lock & Fishbein, Martin. (1980). Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice Hall.

 

Ary, D., Jacobs, L.C., & Razavieh, A. (1996). Introduction to research in education (5th ed.). Fort Worth: Harcourt Brace College Publishers.

 

Baker-Woods, G., Dodd, J.E., Ford, K., Keller, K., Plumley, J., Jr., Smeyak, G.P., & Walsh-Childers, K. (1997). Mass media writing: An introduction. Scottsdale, AZ: Gorsuch Scarisbrick.

 

Fishbein, M., Middlestadt, S.E., & Hitchcock, P.J. (1994). Using information to change sexually transmitted disease-related behaviors. In R.J. DiClemente and J.L. Peterson (eds.), Preventing Aids: Theories and methods of behavioral interventions (p. 61-78). New York: Plenum Press.

 

Flood, R.A. & Elliot, J. (1994). Agricultural awareness in Arizona. Proceedings of the National Agricultural Education Research Meeting, 21, 103-109.

 

Hayakawa, S.I. (1940). Language in thought and action. New York: Harcourt, Brace and Co.

 

Haygood, J., Hagins, S. Akers, C.,  & Kieth, L., (2002). Associated Press Wire Service Coverage of Agricultural Issues. Proceedings of the National Agricultural Education Research Meeting, Las Vegas NV.Krejcie, R.V. & Morgan, D.W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.

 

Lowry, D.T. (1971). Agnew and the network TV news: A before/after content analysis. Journalism Quarterly, 48, 205-210.

 

Lowry, D.T. (1985). Establishing construct validity of the Hayakawa-Lowry news bias categories. Journalism Quarterly, 62(3), 573-580.

 

Sitton, S.R. (2000). 1998 Newspaper coverage of Oklahoma swine production issues: A content analysis. Unpublished doctoral dissertation, Oklahoma State University, Stillwater, OK.

 

Terry, R., Jr. & Lawver, D.E. (1995). University students’ perceptions of issues related to agriculture. Journal of Agricultural Education, 36(4), 64-71.

 

Terry, R., Jr., Dunsford, D., Lacewell, B., & Gray, B. (1996). Analysis of Agricultural literacy information sources: national news periodicals. Proceedings of the Southern Agricultural Education Research Meeting, Georgia, 45, 81-93, 215-226.

 

Thomas, E. & Vistica, G. (1998). Fallout from a Media Fiasco. Newsweek, p. 25.

 

U.S. Census Bureau. (2001). Home Computers and Internet Use in the United States: August 2000. www.census.gov.

 

United States Department of Agriculture, Agricultural Statistics. (2001). Production Agriculture. http://www.nass.usda.gov.

 

Whitaker, B.K., & Dyer, J.E. (1998). A comparison of levels of bias in environmental and food safety articles: Agriculture versus news periodicals. Proceedings of the National Agricultural Education Research Meeting, 25, 436-446.

 

The White House, Office of the Press Secretary. January 21, 2000. Information Technology Research and Development: Information Technology for the 21st Century, http://www.whitehouse.gov/WH/New/html/20000121_2.html.