TV News Political Slant Report by Network: 1/16 – 1/20

Welcome to the Mediate Metrics inaugural TV news political slant measurement report, based on our version 1.0 text classifier.

To our knowledge, this is the first objective TV news slant rating service ever published. Slant, by our definition, is news containing an embedded statement of bias (opinion) OR an element of editorial influence (factual content that reflects positively or negatively on a particular U.S. political party). This initial report focuses specifically on evaluating  slant contained in the weekday transcripts of  the national nightly news programs on the 3 major broadcast networks (ABC, CBS, and NBC) , as well as programming aired from 5 PM until 11 PM eastern time on top 3 cable news channels (CNN, Fox, and MSNBC). At this stage, analytical coverage varies by network, program, and date, but our intention is to fill in the blanks over time.

CHART 1: Slant by Network - January 16 to January 20, 2012

In keeping with U.S. political tradition, content favoring the Republican party in Chart 1 is portrayed in red (positive numbers), while content that tilts towards the Democratic Party is shown in blue (negative numbers)

To grossly over-simplify, the numerical slant ratings supporting the Chart 1 emanate from a custom text analysis “classifier,” built to extract statements of political slant from TV news transcripts. (For more on the underlying technology, see our post on Text Analytics Basics at http://wp.me/p1MQsU-at.) We have trained our classifier to interpret slant quite conservatively, conforming to strict guidelines for the sake of consistency and objectivity. As such, the ratings we present may be perceived as under-reporting the absolute slant of the actual content under review, but the appropriate way to view our ratings is as relative to similar programming.

As mentioned, our analytical coverage varies by network, program, and date. Correspondingly, our rating confidence is directly proportional to the amount of transcript text available for classification.The exact amount of coverage per network is shown in the table to the right, but we have graphically indicated depth-of-coverage in Chart 1 by way of color shading. For example, the bars representing the slant ratings for both NBC and CBS were purposely made lighter to reflect the relatively small transcript coverage for those particular networks.

During development, we determined that the Republican presidential primaries are an enterprise for which scrutiny is a normal-and-valuable part of the vetting process.  Related news content, however, tends to be disproportionately negative, and often times does not contain a clear inter-party comparison — an element we view as a crucial condition for the evaluation of political slant. With those factors mind, we have partitioned statements about the Republican Presidential primaries, and have excluded them from most slant ratings at this juncture. Similarly, the Republican Presidential debates and other such dedicated program segments have been excluded in their entirety from classification since they do not reflect the political positions of the networks, programs, or contributors under a consideration.

We’ll publish slant ratings by program for the same January 16 – 20 time period tomorrow.

Text Analytics Basics

Text analytics, also known as “text mining,” automates what people — researchers, writers, and all seekers of knowledge through the written word—have been doing for years[i]. Thanks to the power of the computer and advancements in the field of Natural Language Processing (NLP), interested parties can tap into the enormous amount of text-based data that is electronically archived, mining it for analysis and insight. In execution, text analytics involves a progression of linguistic and statistical techniques that identify concepts and patterns. When properly tuned, text analytics systems can efficiently extract meaning and relationships from large volumes of information.

To some degree, one can think of the process of text analytics as the evolution of the simple internet search function we use every day, but with added layers of complexity. Searching and ranking words, or even small phrases, is a relatively simple task. Extracting information from large combinations of words and punctuation — which may include elements of slang, humor, or sarcasm — is significantly more difficult. Still, text mining systems generally employ a number of layered techniques to extract meaningful units of information from unstructured text, including:

• Word/Phrase Search Frequency Ranking – What words or “n-grams” appear most often.

• Tokenization – Identification of distinct elements within a text.
• Stemming – Identifying variants of word bases created by conjugation, case, pluralization, etc.
• POS (Part of Speech) Tagging – Specifically identifying parts of speech.
• Lexical Analysis – Reduction and statistical analysis of text and the words and multi-word terms it contains.
• Syntactic Analysis – Evaluation of sequences of language elements, from words and punctuation, and ultimately mapping natural language into a set of grammatical patterns.

The purpose for all of this NLP processing is to compare those computational nuggets with classifications or “codings,” that trained experts have assigned to representative text samples. Interpreting language is an exceedingly complex endeavor, and one that computers and software cannot effectively do without being “trained.” As such, text classification systems are designed to compare human codings with the patterns that emerge from computational analysis, and then mimic the expert coders for all future input.

As you may expect, the quality of any custom text analysis system is largely determined by the quality of the human coders it is trained on. As such, strict rules must be enforced on the human coders, with the knowledge that software classification systems are very literal (think “Dr. Spock”). Still, once effective coding rules are established that result in discernible patterns, text analysis systems are incredibly fast and consistent. Advanced classification systems, like the one employed by Mediate Metrics, are also adaptive, constantly evolving with the ebb and flow of political issues and rhetoric.

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[i] Much of the explanation contained herein was gleaned from Text Analytics Basics, Parts 1 & 2, by Seth Grimes. July 28, 2008. http://www.b-eye-network.com/view/8032.

Related articles

• The Year of Text Analytics (customerthink.com)

Understanding The News

I’m about halfway through Blur by Tom Rosenstiel and Bill Kovach, and I can’t recommend this book enough. Classes on its content should be made an educational requirement.

Here are some factoids I gleaned from my readings:

• Newspaper staffs are down by roughly 30 percent from ten years earlier. For network news, the cuts have been even steeper.
• Traditional online news sites have gotten bigger, not smaller, despite the proliferation of online news outlets.
• When changes in communicating to the masses have occurred in the past, existing power elites have tried to exploit the transition in almost every case.
• The argument culture limits the information we get, rather than broadening it.

The fundamental premise of the book is that the objective-and-disciplined mediation function has largely disappeared from news reporting. In parallel, business models have arisen that focus more on assertion (goal: disseminate information quickly) and affirmation (goal: maintain audience loyalty) than on verification. As such, the burden of editing and analyzing the news falls to individuals. In essence, we must become our own investigative journalists.

The authors point out that the clash between fact-and-faith has occurred numerous times in history, providing both risks and opportunities in every case. In this instance, the challenge of “skeptically knowing” the truth about the news is indeed burdensome, but we can now interact with the media, political leaders, and others in an unprecedented manner.

Powerful stuff, if you think about it.

Related articles

• Blur: Who is Who in Journalism? (teesdiary.wordpress.com)

Open Season on Journalists: Trump versus Todd

It seems like every time I turn around, some political figure is taking on a journalist. Here’s the latest installment, featuring a very edgy Donald Trump chastising Chuck Todd of MSNBC during his Daily Rundown Show this morning.

http://www.youtube.com/watch?v=MyVq-Vj9IlY

Wouldn’t you love to see focus group ratings during one of these exchanges?

My sense is that many political figures already have.

Editorial Selection: Fox and MSNBC

Building on the theme of editorial selection and the news, I decided to once again use my “tag cloud” (most popular words) tool on evening and prime time broadcasts from Fox News and MSNBC on November 14th and 15th. As I highlighted yesterday, media outlets can broadcast but a tiny portion of the available news, so I decided to see what these 2 competitors decided to emphasize.

DISCLAIMER #1: I could not wait to get this out, so I’m sure I will be making additional edits and refinements.

DISCLAIMER #2: Tag clouds are not surgical instruments. That fact, combined with the knowledge that I manually culled words that did not directly relate to specific topics and messaging themes should tell the reader to view the following with a critical eye…. as you should with all interpretative journalism.

Which virtually all political news is.

Disclaimers aside, examining the content selection of Fox and MSNBC is like having box seats at a gun fight. It’s clear that MSNBC is putting Republican Presidential candidates under a microscope, taking pot shots at local Republican candidates whenever possible, and positioning themselves as the mouth-piece for the middle class. Similarly, Fox has President Obama and the 2012 election in the cross hairs, featuring topics that cast him or his administration in a negative light, with specific emphasis on job creation (or a lack thereof).

Those are the highlights — or low-lights, depending on your point of view — but there is more information in the clouds if you are willing to stare at them briefly …

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MSNBC “TOP 25” TAG CLOUD:

• Substantial Republican Primary/Candidate focus, with Herman CAIN (236 occurrences) still drawing the most attention, ROMNEY (82 occurrences) a distant second, and Perry (52 occurrences) in third.
• Occupy Wall Street is a significant topic, as evidenced by the occurrence of the related tag words MOVEMENT, OCCUPY, and STREET. Why WALL did not make the top 25, I have no idea.
• SCOTT is in the top 25 primarily due to parallel references to Republican governors Scott Walker (Wisconsin) and Scott Brown (Florida). Similarly, JOHN was also mentioned frequently in relation to Ohio governor John Kasich, but I removed that name because several other JOHNs were intermingled in the word count.
• Frequent references to AMERICANS (and AMERICANS by default, since my tag cloud tool intermittently extracts root words in parallel) and the middle CLASS seems to represent a positioning theme for MSNBC
• JUDGE generally shows up in 2 different contexts: 1.) The judge who let Penn State coach Sandusky out on reduced bail and; 2.) The impartiality Judges Scalia and Thomas related to the Supreme Court case on health care.
• CASE shows up in several different contexts, again related to the tag cloud tools penchant to extract root words — ObamaCARE, HealthCARE, MediCARE, and are “they” sCAREd?

FOX “TOP 25” TAG CLOUD:

• No references to the Republican Primary candidates by name in the Top 25 tag words. In contrast, PRESIDENT (65 occurrences) and OBAMA (42 occurrences) are the top 2 most popular tag words in the cloud. When viewed in relation to the MSNBC tag cloud, one cannot help but conclude that negative politics extends to these 2 networks.
• Similar, but not exactly the same, thematic positioning around AMERICA, but not so much on CLASS.
• BOOK was an area of focus mostly because of controversies surrounding Bill O’Reilly’s new book (“Killing Lincoln”), and Peter Schweizer’s book about alleged congressional insider trading.
• A greater focus on activities in the SUPER COMMITTEE, and with question on whether a satisfactory DEAL can be made.
• DEAL was also used in the context of favorable (and ethically questionable) deals made on IPOs and land, leveraging the insider trading immunity afforded to congressman.
• CONGRESS was primarily used in 2 contexts: 1.) There were several CONGRESS persons on the prime time Fox News programs I analyzed, and; 2.) Numerable references were made along the lines of our “Do-nothing CONGRESS. ..”
• ELECTION appeared primarily as part of 2 topics: 1.) Forward-looking statements related to the 2012 Presidential election, and; 2.) The fact that negative news related to Solyndra was allegedly throttled by administration officials.
• FLORIDA made the top 25 based on Florida government officials on the shows whose transcripts I analyzed.
• JOB and JOBS are in the top group because of a focus on the subject of job creation.
• LEGAL is attached to either the constitutional rights that should or should not be afforded terrorists, as well as related to immigration issues.
• The term SPEAKER rose to the top because of references and sound bites from House Speaker John Boehmer, as well as an interview with FORMER SPEAKER of the House Newt Gingrich.

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If you would like to know more about the specific details of my process or the specific programs I included in this analysis, just email me at: barry@mediatemetrics.com.

White Paper Excerpt: “Bias and Objectivity in the News Media”

I remain convinced that one can measure media bias electronically, at least to some extent, by examining the text of news broadcasts and objectively identifying the speaker’s personal value judgments. With that said, it is far more difficult to extract bias based on that content that is chosen to be aired. The following excerpt, taken from a 2004 white paper published by The Foundation for Critical Thinking titled, “How to Detect Media Bias and Propaganda” by Dr. Richard Paul and Dr. Linda Elder, explains this far more eloquently than I ever could.

Enjoy.

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The logic of constructing news stories is parallel to the logic of writing history. In both cases, for events covered, there is both a massive background of facts and a highly restricted amount of space to devote to those facts. The result in both cases is the same: 99.99999% of the “facts” are never mentioned at all (see Figure 1).

FIGURE 1

If objectivity or fairness in the construction of news stories is thought of as equivalent to presenting all the facts and only the facts (“All the news that’s fit to print”), objectivity and fairness is an illusion. No human knows more than a small percentage of the facts and it is not possible to present all the facts (even if one did know them). It isn’t even possible to present all the important facts, for many criteria compete for determining what is “important.” We must therefore always ask, “What has been left out of this article?” “What would I think if different facts had been highlighted here?” “What if this article had been written by those who hold a point of view opposite to the one embedded in the story as told?”

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Amen.

Who’s News? YOU Decide.

The more I study media bias, the more I realize that TV coverage flows (and often overflows) in certain directions because viewers vote with their eyeballs.

The blogosphere is crackling today with reports on the CBS internal memo which directed their debate moderators to devote fewer questions to Michelle Bachmann. The issue certainly has ignited the fanaterati. Don’t get me wrong; editorial selection bias is a very real phenomenon. Still, a thinking person should consider other possibilities.

So here is one: Perhaps we get a disproportionate amount of coverage on certain issues and people because they drive viewership. Combined with the extensive amount of news capacity that needs to be filled, media outlets are motivated to keep popular stories alive because lots of people are following them. As an unfortunate by-product, reporters and commentators fan the  flames over time by digging up all kinds of corner-cases, then sensationalizing them as “New Developments!” And that’s when we enter the realm of the absurd.

Circling back to the issue du jour, giving Michelle Bachmann more debate time does not make sense for the network in that context. It’s an inexact science, but it is a network executive’s job is to promote viewership … which drives ad revenue …which increases company profits, equity value, and personal paychecks.

It’s tempting to see a conspiracy here, and maybe there is one, but I think it is equally possible that this is just capitalism in action.

Political News: More Commonly Used Media Bias Techniques

Combing through news transcripts for bias indicators provides you with either unique insights or temporary insanity. Despite my questionable mental state, I’ve uncovered some subtler tricks-of-the-news-trade that I’d like to share with my readers.

Value Judgments: By definition, a value judgment is an assessment that reveals more about the values of the person making the assessment than about the reality of what is assessed. Value judgments can be either direct or projected.

Direct value judgments are often preceded with “I,” either explicitly or as understood. Examples are: “I don’t believe that …,” “that won’t work …” Projected value judgments are less obvious, but are used extensively by certain commentators and politicians. Speakers, often wrapping themselves in the flag or as the spokesperson for some popular group, stealthily project their personal opinions with statements like, “Americans won’t support…,”or  “People are not going to …” It doesn’t jump out at you, but the speaker is putting their view in someone else’s mouth.

Loaded Questions and Leading Questions:  A program anchor is in a position of power to determining how the news is presented while viewers sit passively, accepting that the commentator is objectively informing and moderating discussions based on years of conditioning. In the modern era of news programming that is often not the case. Dialogs are rife with loaded and leading questions.

The popular definition of a loaded question is one which contains as controversial assumption but, for the purposes of semantically evaluating bias, my definition is that it is one that contains indisputable evidence of bias. It gives a strong indication of how an anchor wants his/her respondent to answer. Guidelines for recognizing loaded questions include:

• Embedded value-judgments by the questioner: “Don’t you think that sounds <odd/wrong/funny/strange>”?
• Multiple questions within the same statement: “Who would support…?”, “What is the thinking….?”, “Where did they get…?”, “When …?”, “Why …?”

Leading questions are usually more subtle, and don’t have the clear indicators of loaded questions. Still, a savvy viewer can generally pick them out instinctively, particularly when considered together with succeeding responses. For the most part, news programs conform to the cardinal rule of litigation: Don’t ask a question if you don’t know how it will be answered. In the information age, commentators are rarely uninformed about the positions of their guests. In fact, most of them are regulars.

Once you are aware of these rhetorical devices, you’ll be surprised how often you will notice them while watching, “The News.”

Political News Sources: More is Better

I came across an interesting study this weekend, conducted by World Public Opinion. Org, that portrays the extent of misinformation among voters. Further on, it states that , “… those who had greater levels of exposure to news sources had lower levels of misinformation.” Click the link below for the full article and study.
 

www.worldpublicopinion.org/pipa/articles/brunitedstatescanadara/671.php?nid=&id=&pnt=671&l

RAZING CAIN: Analysis of Prime Time Cable Coverage on Herman’s Sexual Harassment Saga

Using available transcripts (10/31 – 11/1) from  and my tag cloud tool, I compared the prime time coverage differences between CNN, Fox, and MSNBC.  I readily admit that tag clouds are a coarse analytical tool, but some interesting themes  emerge, nonetheless.

Across the board, I removed proper names of network news personalities, and terms that conveyed no significant meaning. In doing so, I was as consistent as possible across all 3 transcripts; my intent was to extract thematic meaning from the accompanying tag clouds. A full list of the words removed from the respective transcripts is available upon request.

CNN

• Tag cloud indicates in-depth coverage on Cain’s SEXUAL (45 occurrences) harassment issue.
• Had Politco’s John Martin on show, who helped break the story.
• Much more scrutiny on the AGREEMENT (42 occurrences) and SETTLEMENT (37 occurrences) than other networks.
• CAIN (269 occurrences) coverage dwarfs other topics, particularly if one considers all the related terms in the tag cloud. The related terms of PRESIDENT (61 occurrences) and OBAMA (46 occurrences) are the only other major discussion topics of note.
•  Word count – 34,889

FOX

• BLACK (23 occurrences) is somewhat overstated because of the interviews with former Secretary of State Condoleeza Rice, but most mentions relate to Herman Cain being a black conservative.
• Use of DON’T is somewhat conspicuous (123 occurrences). Lots of “I don’t think they …” and “don’t you believe that ..”
• PRESIDENT (86 occurrences) and OBAMA (56 occurrences) still a major news focus. General distribution of topics discussed, beyond the Cain sexual harassment issue, appears more diverse than CNN’s in this time period.
•  Word count – 34,644

MSNBC

• MSNBC’s analysis is skewed towards 10/31. For some reason, they only posted 1 transcript (Politics Nation) on their 11/1 shows.
•  Based on the 25 most popular tag words included in the cloud, their coverage of the core issues is similar to Fox’s and CNN’s. Still, it’s hard to tell if the limited 11/1 transcript coverage made a difference.
•  Politics Nation is represented twice in this sample, and that show highlighted the Republican Congressional resolution reaffirming “In God We Trust” (GOD – 50 occurrences), and on the Republican Congressional initiative to cut food stamp subsidies (FOOD – 42 occurrences).
•  Like Fox, the use of DON’T (169 occurrences) is conspicuously prominent. (“I don’t find” … “we don’t have time” …”maybe they don’t …”)
• Raw word count of PRESIDENT (92 occurrences) and OBAMA (61 occurrences) is on par with Fox, but the total word count of the MSNBC transcripts is ~ 29% higher than the other 2 networks.
•  Word count – 45,611