September 25, 2020

Volume X, Number 269

September 25, 2020

Subscribe to Latest Legal News and Analysis

September 24, 2020

Subscribe to Latest Legal News and Analysis

September 23, 2020

Subscribe to Latest Legal News and Analysis

5 Questions with Kimberly Neuendorf: Content Analysis

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.

Copyright ©2020 Cornerstone ResearchNational Law Review, Volume X, Number 223

TRENDING LEGAL ANALYSIS


About this Author

Lisa Tichy IP Healthcare Cornerstone Research
Principal

Lisa Tichy consults on intellectual property and antitrust matters, with a particular focus on matters in the pharmaceutical and healthcare industry. She also analyzes consumer behavior in the context of consumer fraud and product liability, as well as defamation matters. Dr. Tichy has experience with consumer surveys and the analysis of media and social media content. She conducts economic analyses to assist with all stages of the litigation process, including discovery, class certification, expert reports, depositions, trial, and arbitration.

Her work on pharmaceuticals and...

212.605.5309
Anna Shakotko IP Product Liability Analysis Cornerstone Research
Principal

Anna Shakotko focuses on product liability, trade, and intellectual property disputes. She has over a decade of experience providing economic analysis in all stages of litigation, including motion practice, trials, arbitration, and settlement negotiations. Ms. Shakotko works on cases across numerous industries with particular expertise in consumer goods, agricultural products, and technology markets.

Consumer class actions and product disputes

Ms. Shakotko has extensive experience with consumer class actions and private product disputes including false advertising, trademark infringement, and product disparagement. She specializes in applied marketing research such as surveys, and statistical and content analyses. Ms. Shakotko has managed a range of projects that include:

  • Analyzing consumer decision-making and purchase behavior in a variety of industries, including automobile and retail
  • Conducting content analyses of company advertising to determine how particular product features were communicated
  • Performing content analyses of blogs and social media to assess diffusion of information and public understanding of product features
  • Using econometric analyses to determine the price impact of company disclosures about product features to evaluate how consumers value these features

Trade disputes

Ms. Shakotko has worked on International Trade Commission investigations, including patent disputes and antidumping/countervailing duty investigations. Her work includes:

  • Evaluating whether an import exclusion order for a pharmaceutical product would adversely affect public interest
  • Assessing the substitutability of forest products to determine the effect of importing certain products
  • Analyzing regional supply and demand factors in an agricultural market

Intellectual property

Ms. Shakotko has experience related to valuing intangible assets such as brands. She has analyzed branding issues in the context of transfer pricing, including assessing how firms build and sustain brand equity.

Valuation and damages

Ms. Shakotko has conducted valuations and calculated damages in a variety of contexts, such as product liability, lost wages disputes, securities litigation (both equity and debt instruments), and bankruptcy disputes. She has experience using econometric techniques as well as discounted cash flow (DCF) and comparables valuation methodologies.

212.605.5405