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Part 1 of 4: Why Measuring Political Bias is So Hard, and How We Can Do It Anyway: The Media Bias Chart Horizontal Axis

Post One of a Four-Part Series

The Media Bias Chart Horizontal Axis:


Part 1:

Measuring Political Bias–Challenges to Existing Approaches and an Overview of a New Approach

Many commentators on the Media Bias Chart have asked me (or argued with me about) why I placed a particular source in a particular spot on the horizontal axis. Some more astute observers have asked (and argued with me about) the underlying questions of “what do the categories mean?” and “what makes a source more or less politically biased?” In this series of posts I will answer these questions.

In previous posts I have discussed how I analyze and rate quality of news sources and individual articles for placement on the vertical axis of the Media Bias Chart. Here, I tackle the more controversial dimension of rating sources and articles for partisan bias on the horizontal axis. In my post on Media Bias Chart 3.0, I discussed rating each article on the vertical axis by taking each aspect, including the headline, the graphic(s), the lede, AND each individual sentence and ranking it. In that post, I proposed that when it comes to sentences, there are at least three different ways to score them for quality on a Veracity scale, an Expression scale, and a Fairness scale. However, the ranking system I’ve outlined for vertical quality ratings doesn’t address everything that is required to rank partisan bias. Vertical quality ratings don’t necessarily correlate with horizontal partisan bias ratings (though they often do, hence the somewhat bell-curved distribution of sources along the chart).

Rating partisan bias requires different measures, and is more controversial because it disagreements about it enflame the passions of those arguing about it. It’s also very difficult, for reasons I will discuss in this series. However, I think it’s worth trying to 1) create a taxonomy with a defined scope for ranking bias and 2) define a methodology for ranking sources within that taxonomy.

In this series, I will do both things. I’ve created the taxonomy already—the chart itself—and in these posts I’ll explain how I’ve defined its horizontal dimension. The scope of this horizontal axis has some arbitrary limits and definitions,  For example, it is limited in scope to US political issues, as they exist within the last one year or so, and uses the positions of various elected officials as proxies for the categories. You can feel free to disagree with each of these. However, it has to start and end somewhere in order to create a systematic, repeatable way of ranking sources within it. I’ll discuss how I define each of the horizontal categories (most extreme/hyper-partisan/skews/neutral). Then, I’ll discuss a formal, quantitative, and objective-as-possible methodology for systematically rating partisan bias, which has evolved from the informal and somewhat subjective processes I had been using to rate it in the past. This methodology comprises:

  • An initial placement of left, right, or neutral for the story topic selection itself
  • Three measurements of partisanship on quantifiable scales which include
    1. a “Promotion” scale
    2. a “Characterization” scale, and
    3. a “Terminology” scale

3)  A systematic process for measuring what is NOT in an article, the absence of which results in partisan bias.

  1. Problems with existing bias rating systems

To the extent that organizations try to measure news media stories and sources, they often do so only by judging or rating partisan bias (rather than quality). Because it is difficult to define standards and metrics by which partisan bias can be measured, such ratings are often made through admittedly subjective assessments by the raters (see here, for example), or are made by polling the public or a subset thereof (see here, for example). High levels of subjectivity can cause the public to be skeptical of ratings results (see, e.g., all the comments on my blog complaining about my bias), and polling subsets of the public can skew results in a number of directions.

Polling the public, or otherwise asking the public to rate “trustworthiness” or bias of news sources has proven problematic in a number of ways. For one, people’s subjective ratings of trustworthiness of particular sources tend to correlate very highly with their own political leanings, so while liberal people will tend to rate MSNBC as highly trustworthy and FOX as not trustworthy, conservative people will do the opposite, which says very little about an objective level of actual trustworthiness of each of those sources. Further, current events have revealed that certain segments of the population are extremely susceptible to influence by low-quality, highly biased, and even fake news, and those segments have proven themselves unable to reliably discern measures of quality and bias, making them unhelpful to poll.

Another way individuals and organizations have attempted to rate partisan bias is through software-enabled text analysis. The idea of text analysis software is appealing to researchers because the sheer volume of text of news sources is enormous. Social media companies, advertisers, and other organizations have recently used such software to perform “sentiment analysis” of content such as social media posts in order to identify how individuals and groups feel about particular topics, with the hopes that knowing such information can influence purchasing behavior. Some have endeavored to measure partisan bias in this way, by programming software to count certain words that could be categorized as “liberal” or “conservative.” A study conducted by researchers at UCLA tried to measure such bias by references by media figures to conservative and liberal think tanks. However, such attempts to rate partisan bias have had mixed results, at best, because of the variation in context in which these words are presented. For example, if a word is used sarcastically, or in a quote by someone on the opposite side of the political spectrum from the side that uses that word, then the use of the word is not necessarily indicative of partisan bias. In the UCLA study, references to political think tanks were too infrequent to generate a meaningful sample. I submit that other factors within an article or story are far more indicative of bias.

I also submit that large-scale, software-enabled analysis bias ratings are not useful if the results do not align well with the subjective bias ratings gathered by a group of knowledgeable media observers. That is, if we took a poll of an equal number of knowledgeable left-leaning and right-leaning media observers, we could come to some kind of reasonable average for ratings bias. To the extent the software-generated results disagree, that suggests that the software model is wrong. I earlier stated my dissatisfaction with consumer polls as the sole indicator of bias ratings because it is consumer-focused and not content-focused. I think there is a way to develop a content-based approach to ranking bias that aligns with our human perceptions of bias, and that once that is developed, it is possible to automate portions of that content-based approach. That is, we can get computers to help us rate bias, but we have to first create a very thorough bias-rating model.

  1. Finding a better way to rank bias

When I started doing ratings of partisanship, I, like all others before me, rated them subjectively and instinctively from my point of view. However, knowing that I, like every other human person, have my own bias, I tried to control for my own bias (as referenced in my original methodology post), possibly resulting in overcorrection. I wanted a more measurable and repeatable way to evaluate bias of both entire news sources and individual news stories.

I have created a formal framework for measuring political bias in news sources within the defined taxonomy of the chart. I have started implementing this formal framework when analyzing individual articles and sources for ranking on the chart. This framework is a work in progress, and the sample size upon which I have tested it is not yet large enough to conclude that it is truly accurate and repeatable. However, I am putting it out here for comments and suggestions, and to let you know that I am designing a study for the dual purposes of 1) rating a large data set of articles for political bias and 2) refining the framework itself. Therefore, I will refer to some of these measurements in the present tense and others in the future tense. My overall goal is to create a methodology by which other knowledgeable media observers, including left-leaning and right-leaning ones, can reliably and repeatably rate bias of individual stories and not deviate too far from each other in their ratings.

My existing methodology for ranking an overall source on the chart takes into account certain factors related to the overall source as a first step, but is primarily based on rankings of individual articles within the source. Therefore, I have an “Entire Source” bias rating methodology and an “Individual Article” bias rating methodology.

  1. “Entire Source” Bias Rating Methodology

I discussed ranking partisan bias of overall sources in my original methodology post, which involves accounting for each of the following factors:

  1. Percentage of news media stories falling within each partisanship category (according to the “Individual Story” bias ranking methodology detailed below)
  2. Reputation for a partisan point of view among other news sources
  3. Reputation for a partisan point of view among the public
  4. Party affiliation of regular journalists, contributors, and interviewees
  5. Presence of an ideological reference or party affiliation in the title of the source

In my original methodology post, I identified a number of other factors for ranking sources on both the quality and partisanship scales that I am not necessarily including here. These include the factors of 1) number of journalists 2) time in existence and 3) readership/viewership. This is because I am starting with an assumption that the factors (a-e) listed above are more precise factors indicating partisanship that would line up with polling results of journalists and knowledgeable media consumers. In other words, my starting assumption is that if you used factors (a-e) to rate partisanship of a set of sources, and then also polled significant samples of journalists and consumers, you would get similar results. I believe that over time, some of the factors 1-3 (number of journalists, time in existence, and readership/viewership) may be shown to correlate strongly with indications of partisanship or non-partisanship. For example, I suspect that the factor “number of journalists” may be found to correlate high numbers of journalists with low partisanship, for the reason that it is expensive to have a lot of journalists on staff, and running a profitable news enterprise with a large staff would require broad readership across party lines. I suspect that “time in existence” may not necessarily correlate with partisanship, because there are several new sources that have come into existence within just the last few years that strive to provide unbiased news. I suspect that readership/viewership will not correlate very much with partisanship, for the simple reason that as many people seem to like extremely partisan junk as like unbiased news. Implementation of a study based on the above listed factors should verify or disprove these assumptions.

I have “percentage of news media stories falling within each partisanship category” listed as the first factor for ranking sources, and I believe it is the most important metric. Whenever someone disagrees with a particular ranking of an overall source on the chart, they usually cite their perceived partisan bias of a particular story that they believe does not align with my ranking of the overall source. What should be apparent to all thoughtful media observers out there, though, is that individual articles can themselves be more liberal or conservative than the mean or median partisanship bias of its overall source. In order to accurately rank a source, you have to accurately rank the stories in it.

             2. “Individual Story” Bias Rating Methodology

As previously discussed, I propose evaluating partisanship of an individual article by: 1) creating an initial placement of left, right, or neutral based on the topic of the article itself, 2) measuring certain factors that exist within the article and then 3) accounting for context by counting and evaluating factors that exist outside of the article. I’ll discuss this fully in Posts 3 and 4 of this series.

In my next post (#2 in this series) I will discuss the taxonomy of the horizontal dimension. I’ll cover many reasons why it is so hard to quantify bias in the first place. Then I’ll define what I mean by “partisanship,” the very concepts of “liberal,” “mainstream/center,” and “conservative,” and what each of the categories (most extreme/hyper-partisan/skews/neutral or balance) mean within the scope of the chart.

 Until then, thanks for reading and thinking!