5 Questions with Kimberly Neuendorf: Content Analysis


A periodic feature by Cornerstone Research, in which our affiliated experts, senior advisors, and professionals, talk about their research and findings.

We interview Professor Kimberly Neuendorf of Cleveland State University to gain her insights on content analysis: what it is, when it is used, and what is driving its remarkable growth. Professor Neuendorf is an authority on content analysis methods. She specializes in how communications can drive audience and consumer preferences, perceptions, and behavior.

How would you define content analysis?

Content analysis is the systematic, objective, quantitative assessment of message characteristics. Messages can be text-based (such as news articles, commentary on websites, social media posts), visual (photos, video), or aural (radio programming, speeches). Using specific techniques, researchers can analyze these messages in a manner that is both replicable and consistent with the scientific method.

What techniques are used in content analysis?

Two primary techniques drive content analysis: computer-aided text analysis and human coding.

Computer-aided text analysis: Because content analysis is intended to be free of bias, computer algorithms are highly effective tools. Computers can, for example, quantify the occurrence of certain words or phrases over time, as well as correlations between certain words. Once computer algorithms have mapped those data, researchers can identify larger patterns and themes.

Human coding: For tasks that computers cannot adequately perform—such as analyzing visual images or video—human skills come into play. More nuanced textual content can also require human interpretation: sarcasm employed in a speech is a good example.

Human coders adhere to specific procedures and follow a detailed written scheme as they classify messages. Typically, two people code each message independently; comparing their respective work allows the content analyst to verify that the coding is reliable and objective.

In what circumstances do academic researchers rely on content analysis?

Content analysis is used in a wide variety of academic disciplines, including political science, psychiatry, and linguistics. To give examples from yet another field, media studies, researchers have sought to identify the differences in news coverage over time or across geographic regions, or to analyze particular media themes, such as violence.

In the legal arena, content analyses of historical judicial opinions can provide quantitative means for interpreting case law. Specifically, by conducting content analysis on past court decisions, lawyers can gain insight into factors that might inform future rulings.

When is content analysis used in litigation?

In false advertising cases, experts have conducted content analyses to identify themes or terms that companies use in their advertisements. In defamation and consumer fraud and product liability settings, content analysis can help gauge public sentiment about a given topic. For instance, content analysts have assessed Twitter posts to understand consumer perception of a product. In event studies, content analysis can show when and how news is disseminated, and what information is in the public domain at certain points in time. Finally, law firms have used news coverage analyses of their high-profile clients as evidence to support change-of-venue motions.

In addition to these specific scenarios, content analysis can be a vital e-discovery and organizing tool in cases that draw upon voluminous numbers of documents.

What is driving the growth of content analysis?

One key factor is the explosion of the Internet. There is a vast amount of content to analyze: social media, blogs, YouTube, websites, online archives of documents. The list is long and gets longer every minute.

Another driver is the increasing sophistication of computers, which now perform tasks previously reserved for humans. Specifically, computers can use machine learning (such as neural networks) to recognize some content patterns without human input. Such advancements will make content analysis faster and more accessible in the near future.

The views expressed herein do not necessarily represent the views of Cornerstone Research.


Kimberly Neuendorf

Professor, School of Communication,
Cleveland State University