A number of members of the PEJ staff assisted in the production of this report, \u201cWhen Technology Makes Headlines: The Media\u2019s Double-Vision About the Digital Age.\u201d <\/p>\n\n
The team leaders on the project were research analyst Kenny Olmstead and Senior Researcher Paul Hitlin. Sovini Tan, Mahvish Khan and Josh Appelbaum aided in the research, including coding and content analysis of 437 stories about technology.<\/p>\n\n
Other staff members who made substantial contributions to the report were: weekly News Index manager Tricia Sartor, analyst\/coder Laura Houston Santhanam, deputy director Amy Mitchell and director Tom Rosenstiel. <\/p>\n\n
When Technology Makes Headlines: The Media\u2019s Double-vision About the Digital Age was conducted in three parts. The first was made up of coding from PEJ\u2019s weekly News Coverage Index (NCI). That sample analyzed data from June 1, 2009 through June 30, 2010 and is referred to here as the broad sample<\/strong>. It consists of all news stories including those that were about subjects unrelated to technology. <\/p>\n\n The second part of the study includes a closer examination of a sub-section of technology-related stories as they were originally coded in the NCI. This sample covers the time period of June 1, 2009 through June 30, 2010 and is referred to here as the technology-focused sample.<\/strong> Details of that process are below. Details of each of the three analyses follow: <\/p>\n\n The broad sample included all the outlets that are part of PEJ\u2019s regular News Coverage Index. The complete methodology of the News Coverage Index is available here<\/a>. <\/p>\n\n At the beginning of each year, PEJ reexamines the outlets including in the NCI sample and makes changes in order to keep up with circulation and viewership trends in the media. <\/p>\n\n Because this particular study includes data from both 2009 and 2010, the makeup of the sample differs slightly during those two years. <\/p>\n\n In 2009, these outlets, along with the methods of rotation, were as follows: <\/p>\n\n Newspapers (Sun-Fri)<\/em> Coded two out of these four every weekday and Sunday Coded two out of these four every weekday and Sunday Coded 2 out of these 4 every weekday and Sunday Web sites (Coded 6 of 12 each day, Mon-Fri) <\/em> Morning Network TV (Mon-Fri) <\/em> Evening Network TV (Mon-Fri)<\/em> Cable TV (Fifteen in all, Mon-Fri) <\/em> Nighttime CNN \u2013 coded 2 out of the 4 every day <\/em> Nighttime Fox News \u2013 coded 2 out of the 4 every day <\/em> Nighttime MSNBC \u2013 coded 2 out of the 4 every day <\/em> News Radio (Mon-Fri) <\/em> Talk Radio (Mon-Fri) <\/em> In 2010, the outlets, along with the methods of rotation, changed to the following: <\/p>\n\n Newspapers (Sun-Fri)<\/em> Coded two out of these four every weekday and Sunday Coded 1 or 2 out of these 3 every weekday and Sunday Web sites (Coded 6 of 12 each day, Mon-Fri) <\/em> Morning Network TV (Mon-Fri) <\/em> Evening Network TV (Mon-Fri)<\/em> PBS Cable TV (Fifteen in all, Mon-Fri) <\/em> Nighttime CNN \u2013 coded 2 out of the 4 every day <\/em> Nighttime MSNBC \u2013 coded 2 out of the 4 every day <\/em> News Radio (Mon-Fri) <\/em> Radio Headlines<\/em> Talk Radio (Mon-Fri) <\/em> 1 liberal every other day Story Inclusion<\/strong> The technology-focused sample constitutes the main analysis for this report. It was made up of stories coded as technology stories in the broad sample from June 1, 2009 through June 30, 2010. First, we gathered the stories that were coded with a broad topic of Technology or Technology and Culture. In addition to these two broad topic areas we also included content identified as technology-related through the more specific variable of \u201cstoryline.\u201d\u00a0 The storyline variable tracks specific events in the news. At times, technology-related events fell under different broad topic areas such as business or crime.\u00a0 This two-layer approach ensures that we cast as wide a net as possible in identify the content to analyze. <\/p>\n\n The list of technology-related storylines was as follows: <\/p>\n\n Cyberspace Issues In all, 953 stories were identified as technology-related. <\/p>\n\n From this group of 953 stories, we next selected every other story to examine in greater detail through additional content analysis. Before coding, though, a trained coder examined each of the selected stories to make sure that it applied to the subject matter in this study. <\/p>\n\n In all, the technology-focused sample included 437 stories. <\/p>\n\n Capture and Retrieval <\/strong> For newspapers that are available in print in the Washington, D.C. area, hard copies are used. For newspapers that are not available for delivery, digital editions of the paper are retrieved either through the newspaper\u2019s own Web site, or through the use of digital delivery services such as pressdisplay.com and newsstand.com. When necessary, the text of articles are supplemented by the archives available in the LexisNexis computer database. <\/p>\n\n Radio programs are captured through online streams of the shows. Using automated software, we record several local affiliates that air the program in various markets throughout the country. The purpose of this method is to ensure that we have a version of the program in case one of the streams is unavailable on a particular day, and so that we record the show in a manner that represents the way a typical listener would hear the program with commercials and newsbreaks. <\/p>\n\n Online websites are captured manually by a member of PEJ\u2019s staff. The capture time is rotated daily between 9-10 am ET and 4-5 pm ET. The home pages and pages with the top articles for all sites are saved so that when we reference the material, the format is the same as it appeared online at the time of capture. <\/p>\n\n Finally, all television shows are recorded digitally and archived for coding purposes. PEJ is a subscriber to DirectTV satellite service and all programs are recorded onto multiple TiVo recording units before being burned onto DVDs for archival purposes. <\/p>\n\n All television and radio programs are then coded by a member of PEJ\u2019s staff who watches or listens to the archived version of the program. <\/p>\n\n Coding Team & Process for Weekly Index Coding<\/strong> Additional Coding of Technology-focused stories<\/strong> \u2022\u00a0\u00a0\u00a0 Tech storyline <\/strong>refers to particular storylines that occurred often in news media during the time period under study <\/p>\n\n \u2022\u00a0\u00a0\u00a0 Tech area<\/strong> involves the issue or larger subject being covered <\/p>\n\n \u2022\u00a0\u00a0\u00a0 Lead company<\/strong> designates the company that is the main focus of the story<\/p>\n\n \u2022\u00a0\u00a0\u00a0 Presence of companies<\/strong> identifies whether any of the major tech companies tracked (Apple, Google, Twitter, Facebook, and Microsoft) where present in at least 25% of the story<\/p>\n\n \u2022\u00a0\u00a0\u00a0 General technology thread<\/strong> refers to the concepts or impressions that form around technology as a whole in a given story<\/p>\n\n \u2022\u00a0\u00a0\u00a0 Company threads<\/strong> refers to concepts or impressions that form around a particular company in a given story <\/p>\n\n Coding Team & Process for the Additional Coding<\/strong> In addition to the main intercoder testing conducted on all NCI variables, supplemental testing was conducted on the additional variables used in this portion of the study. For the following codes, 30 randomly selected stories were coded by all members of the coding team. <\/p>\n\n The percent agreement for each variable was as follows: <\/p>\n\n Tech storyline: 91% Specific company threads<\/em><\/p>\n\n Google is innovative and its products are superior\/better designed: 97% The sections in this report about social media are based off the data collected from PEJ\u2019s New Media Index. The NMI is a weekly report that captures the leading commentary of blogs and social media sites focused on news and compares those subjects to coverage in the mainstream press. <\/p>\n\n This study aggregates the weekly data collected for the News Media Index from June 1, 2009, through June 25, 2010. <\/p>\n\n Universe <\/strong> Two prominent Web tracking sites, Technorati<\/a> and Icerocke<\/a>t, monitor millions of blogs and pieces of social media, using the links to articles embedded on these sites as a proxy for determining what these subjects are. The website Tweetmeme<\/a> uses a similar method to monitor the popular links on the social networking site Twitter<\/a>. <\/p>\n\n Each of these sites offers lists of the most linked-to news stories based on the number of blogs, tweets, or other pages that link to them. PEJ does not determine what constitutes a \u201cnews\u201d story (as opposed to some other topic), but rather relies on the classifications used by each of the tracking sites.<\/p>\n\n A PEJ staff member manually captured the lists from each site every weekday between 9 and 10 am ET. From those lists, the top five linked to articles were captured for further analysis by PEJ staff (SEE BELOW). <\/p>\n\n Through July 3, 2009, PEJ captured information about blogs from both Technorati and Icerocket. However, the relevant component of Technorati\u2019s site stopped working in early July and has been down ever since. Therefore, the NMI reports beginning the week of July 6-10, 2009, only included blog data from Icerocket.\u00a0 <\/p>\n\n Coding Procedures <\/strong> Almost all of the codes and rules are the same as with the NCI. The variables coded in both projects include story date, source, story word count, story format, story describer, big story and broad story topic. <\/p>\n\n In order to meet high standards of reliability, these variables are all included as part of PEJ\u2019s continuing intercoder testing involving 15 coders and reached levels of agreement above 80%. <\/p>\n\n
The third part of the study involves data collected about social media as part of PEJ\u2019s New Media Index. Details of the social media sample <\/strong>are also below. <\/p>\n\n
<\/strong><\/u> <\/div>\n\n
Coded every day
NY Times <\/p>\n\n
Washington Post
USA Today
Los Angeles Times
Wall Street Journal <\/p>\n\n
Kansas City Star
Pittsburgh Post-Gazette
San Antonio Express-News
San Jose Mercury News <\/p>\n\n
Herald News (MA)
Anniston Star (AL)
Spokesman-Review (WA)
Meadville Tribune (PA) <\/p>\n\n
CNN.com
Yahoo News
MSNBC.com
Google News
AOL News
Foxnews.com
USAToday.com
Washingtonpost.com
ABCNews.com
BBC News (international version)
Reuters.com
NYTimes.com <\/p>\n\n
ABC \u2013 Good Morning America
CBS \u2013 Early Show
NBC \u2013 Today <\/p>\n\n
ABC \u2013 World News Tonight
CBS \u2013 CBS Evening News
NBC \u2013 NBC Nightly News
PBS \u2013 NewsHour with Jim Lehrer (rotated daily between the first 30 minutes and the second 30 minutes of the hour-long broadcast) <\/p>\n\n
Daytime (2:00 to 2:30 pm) coded 2 out of 3 every day
CNN
Fox News
MSNBC <\/p>\n\n
Situation Room (6 pm)
Lou Dobbs Tonight
CNN Prime Time\/Campbell Brown: No Bias, No Bull
Anderson Cooper 360 <\/p>\n\n
Special Report w\/ Bret Baier
Fox Report w\/ Shepard Smith
O\u2019Reilly Factor
Hannity <\/p>\n\n
The Ed Show\/1600 Pennsylvania Ave
Hardball (7 pm)
Countdown w\/ Keith Olbermann
Rachel Maddow <\/p>\n\n
NPR Morning Edition every day (rotated daily between the first 30 minutes of the first hour and first 30 minutes of the second hour)
ABC Radio headlines at 9am and 5pm
CBS Radio headlines at 9am and 5pm <\/p>\n\n
Rush Limbaugh every other day
1 out of 2 additional conservatives each day
Sean Hannity
Michael Savage
1 out of 2 liberals each day
Ed Schultz
Randi Rhodes <\/p>\n\n
Coded two out of these four every weekday and Sunday
The New York Times
Los Angeles Times
USA Today
Wall Street Journal<\/p>\n\n
The Washington Post
The Columbus Dispatch
Tampa Tribune
Seattle Times <\/p>\n\n
The Day (CT)
Rome News Tribune (GA)
Ventura News (CA)<\/p>\n\n
Yahoo News
MSNBC.com
CNN.com
NYTimes.com
Google News
AOL News
FoxNews.com
USAToday.com
WashingtonPost.com
ABCNews.com
HuffingtonPost.com
Wall Street Journal Online <\/p>\n\n
ABC \u2013 Good Morning America
CBS \u2013 Early Show
NBC \u2013 Today <\/p>\n\n
Code 2 out of 3 each weekday
ABC \u2013 World News Tonight
CBS \u2013 CBS Evening News
NBC \u2013 NBC Nightly News <\/p>\n\n
Code two consecutive days, then skip one
Newshour with Jim Lehrer<\/p>\n\n
Daytime (2:00 to 2:30 pm) coded 2 out of 3 every day
CNN
Fox News
MSNBC <\/p>\n\n
Situation Room (6 pm)
John King, USA
CNN Prime Time
Anderson Cooper 360
Nighttime Fox News \u2013 coded 2 out of the 4 every day <\/em>
Special Report w\/ Bret Baier
Fox Report w\/ Shepard Smith
O’Reilly Factor
Hannity<\/p>\n\n
The Ed Show
Hardball (7 pm)
Countdown w\/ Keith Olbermann
The Rachael Maddow Show<\/p>\n\n
NPR
Code 1 out of 2 every weekday
Morning Edition
All Things Considered <\/p>\n\n
ABC Radio headlines at 9am or 5pm
CBS Radio headlines at 9am or 5pm <\/p>\n\n
1 out of 2 conservatives each day
Rush Limbaugh
Sean Hannity <\/p>\n\n
Ed Schultz <\/p>\n\n
For the broad sample, PEJ analyzed all stories with a national or international focus that appeared as follows:
\u2022\u00a0\u00a0 \u00a0On the front page of newspapers
\u2022\u00a0\u00a0 \u00a0During the first 30 minutes of network morning news, cable programs, and talk radio shows
\u2022\u00a0\u00a0 \u00a0During a thirty minute segment of NPR\u2019s Morning Edition and PBS\u2019 NewsHour with Jim Lehrer
\u2022\u00a0\u00a0 \u00a0As one of the top 5 stories on each Web site at the time of capture
\u2022\u00a0\u00a0 \u00a0During the entirety of the commercial network evening newscasts and syndicated news headlines segments on ABC radio and CBS radio <\/p>\n\n
<\/u> <\/div>\n\n
Apple
Video game consoles
Microsoft\/Yahoo Search Agreement
MySpace Suicide Case
Google Launches Chrome
Digital TV Switch
LA Train Crash
Craigslist Killer
Texting while driving
Phoebe Prince Suicide\/Cyber Bullying Case
General Google News <\/p>\n\n
All outlets are captured and included in PEJ\u2019s media archive. <\/p>\n\n
The data derived from PEJ\u2019s regular Index coding was conducted by PEJ\u2019s team of 15 trained coders. We have tested all of the variables contained in the regular weekly Index coding and all the variables reached a level of agreement of 80% or higher. For specific information about those tests, see the methodology section for the NCI. <\/a> <\/p>\n\n
For the stories from the technology-focused sample additional coding was conducted for six specific variables. <\/p>\n\n
A team of four of PEJ\u2019s experienced coders worked with a senior researcher in order to complete the additional coding for this particular study. <\/p>\n\n
Tech area: 88%
General topic thread: 80%
Lead company: 91%
Apple presence: 99%
Google presence: 98%
Twitter presence: 96%
Facebook presence: 96%
Microsoft presence: 100%<\/p>\n\n
Google makes content more searchable\/findable: 98%
Google is a pretty virtuous company, really does try to follow the slogan \u201cdon\u2019t be evil\u201d:100
Google has too much information\/power: 100%
Google steals others\u2019 content and ideas: 100%
Google creates business competition for other companies: 98%
Microsoft is innovative and its products are superior\/breakthrough\/better designed: 100%
Microsoft is a dangerous manipulative monopoly: 100%
Microsoft products have too many bugs\/viruses: 100%
Microsoft is behind the innovation curve: 100%
Apple is innovative and Apple\u2019s products are superior\/better designed: 96%
Apple\u2019s products are user friendly and that\u2019s why they are popular: 95%
Apple is anti-competitive and too controlling with its products and services: 99%
Apple creates products people don\u2019t really need\/planned obsolescence: 97%
Apple has a loyal\/rabid fan base unlike other companies: 99%
Apple\u2019s products are overhyped and overpriced: 96%
Twitter makes dissemination of information easier and connects people: 98%
Twitter is pointless, harms creativity, is self centered etc.: 100%
Facebook brings people together: 96%
Facebook fosters communication: 97%
Facebook doesn\u2019t protect its user\u2019s privacy\/is an unsure network: 100% <\/p>\n\n
<\/u> <\/div>\n\n
To study new and social media, PEJ wanted to be able to include as wide a range of outlets as possible. For unlike the traditional press, blogs and social media pages reach into the millions and change daily as new ones emerge and other dissolve. In exploring various options, we saw value combining the work of some sites that specialize in tracking these outlets continuously with our own coding scheme and analytics. <\/p>\n\n
Once the lists of articles were compiled, PEJ staff conducted a content analysis of the subject matter of these linked-to news articles in a similar manner to the News Coverage Index. <\/a> <\/p>\n\n